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CHINA\nInformation Sciences\nFebruary 2025, Vol. 68, Iss. 2, 121101:1–121101: 44\nh\nttps://doi.org/10.1007/s11432-024-4222-0\nc⃝Science China Press 2025 info.scichina.com link.springer.com.REVIEW.\nThe rise and potential of large language model based\na\ngents: a survey\nZhiheng XI1†, Wenxiang CHEN1†, Xin GUO1†, Wei HE1†, Yiwen DING1†,\nBoyang HONG1†, Ming ZHANG1†, Junzhe WANG1†, Senjie JIN1†, Enyu ZHOU1†,\nRui ZHENG1, Xiaoran FAN1, Xiao WANG1, Limao XIONG1, Yuhao ZHOU1,\nWeiran WANG2, Changhao JIANG1, Yicheng ZOU1, Xiangyang LIU1, Zhangyue YIN1,\nShihan DOU1, Rongxiang WENG4, Wenjuan QIN2, Yongyan ZHENG2,\nXipeng QIU1, Xuanjing HUANG1, Qi ZHANG1*& Tao GUI3*\n1School of Computer Science, Fudan University, Shanghai 200441, China\n2College of Foreign Languages and Literature, Fudan University, Shanghai 200433, China\n3Institute of Modern Languages and Linguistics, Fudan University, Shanghai 200433, China\n4Meituan, Beijing 100102, China\nReceived 7 September 2024/Revised 25 October 2024/Accepted 11 November 2024/Published online 17 January 2025\nAbstract For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds human intelligence.\nAI agents, which are artificial entities capable of sensing the environment, making decisions, and taking actions, are seen as a\nmeans to achieve this goal. Extensive efforts have been made to develop AI agents, with a primary focus on refining algorithms\nor training strategies to enhance specific skills or particular task performance. The field, however, lacks a sufficiently general\nand powerful model to serve as a foundation for building general agents adaptable to diverse scenarios. With their versatile\ncapabilities, large language models (LLMs) pave a promising path for the development of general AI agents, and substantial\nprogress has been made in the realm of LLM-based agents. In this article, we conduct a comprehensive survey on LLM-based\nagents, covering their construction frameworks, application scenarios, and the exploration of societies built upon LLM-based\nagents. We also conclude some potential future directions and open problems in this flourishing field.\nKeywords natural language processing, large language models, LLM-based agents, AI agents, agent society\nCitation Xi Z H, Chen W X, Guo X, et al. The rise and potential of large language model based agents: a survey. Sci China\nInf Sci, 2025, 68(2): 121101, https://doi.org/10.1007/s11432-024-4222-0\n1 Introduction\nOne core research area in artificial intelligence (AI) is to develop agents that possess human-level in-\ntelligence, and potentially even exceed it [1 ]. The concept of agent originates in philosophy, where it\nd\nescribes entities possessing desires, beliefs, intentions, and the ability to take actions [2 ]. In AI research,\nt\nhe term agent refers to an artificial entity capable of perceiving its surroundings using sensors, making\ndecisions, and then taking actions in response using actuators [1 ,3]. The development and advancement\no\nf agents have been central within the AI community for a long time [1 ,4]. Moreover, AI agents are now\nr\necognized as a pivotal stride towards achieving artificial general intelligence (AGI)1), as they encompass\nthe potential for a wide range of intelligent activities [3 ,5,6].\nF\nrom the mid-20th century, significant strides were made in designing and developing AI agents [7 –12].\nH\nowever,theseeffortshavepredominantlyfocused onenhancingspecificskills(suchassymbolicreasoning\nor quick responsiveness) or mastering particular tasks (such as Go or Chess) [13 –15]. Achieving broad\na\ndaptability across varied scenarios remained elusive. Moreover, previous studies mostly emphasized the\ndesign of algorithms and training strategies, overlooking the development of the model’s inherent general\n*Corresponding author (email: qz@fudan.edu.cn, tgui@fudan.edu.cn)\n†These authors contributed equally to this work.\n1) Also known as Strong AI.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:2\nFigure 1 (Color online) Roadmap of this survey.\nabilities [16 ,17]. Actually, enhancing these inherent abilities of the model is pivotal fo r advancing the\nagent, and the field is in need of a powerful and versatile model to serve as the foundation for agent\nsystems.\nThe emergence of large language models (LLMs) has brought a glimmer of hope for the further devel-\nopment of agents [18 –20], and significant progress has been made by researchers in the filed [16 ,21–23].\nT\nhis is attributed to the remarkable capabilities demonstrated by LLMs, including natural language\ninteraction, knowledge acquisition, instruction following, generalization, reasoning, planning, and tool-\nusing. These advantages have earned LLMs the designation of sparks for AGI [24 ], making them highly\nd\nesirable for building general agents [16 ]. And the area of LLM-based agents has emerged as one of the\nm\nost promising fields in AI research. The academic and industrial communities have invested significant\nresearchand development efforts in this field. However, despite the progress, this emerging field still faces\nnumerous crucial issues that require further exploration and resolution, e.g., the architectural design of\nLLM-based agents and their application domains. Therefore, it is necessary to review the origins and\ndevelopment of this field, summarize current research achievements, and look ahead to provide a clearer\nunderstanding of LLM-based agents and deeper insights into their future development.\nIn this article, we present a comprehensive and systematic survey of LLM-based agents, attempting\nto explore several critical research problems within the field and prospective avenues in this burgeoning\nfield (Figure 1demonstrates the roadmap of this survey). Before delving into the research problems, we\nfirst discuss the background information (Section 2), including the origin, definition, and development\no\nf AI agents, as well as why LLMs are suitable as the foundation for AI agents. The first research\nproblem is the design and construction of LLM-based agents (Section 3). Based on the definition of AI\na\ngents, we propose a conceptual framework with three key components: brain, perception, and action.\nDifferent downstream applications can tailor this framework according to their specific requirements.\nThe second research problem is the application scenarios of LLM-based agents (Section 4). We discuss\nt\nhe various paradigms such as single agent, multi-agent, and human-in-the-loop, and their respective\napplicable scenarios. The third research problem is agent society (Section 5). We discuss the components\na\nnd operational mechanisms of an agent society, as well as its insights into human society. Finally, we\ndiscuss a series of open problems and future directions in this field (Section 6), e.g., the mutual benefits\nb\netween LLM research and agent research, evaluation for LLM-based agents, potential risks of LLM-\nbased agents, and scaling up the number of agents. Finally, we present an overview and summary of the\nentire article (Section 7).\n2 Background\nIn this section, we provide crucial background information to lay the groundwork for the subsequent\ncontent. We first discuss the origin of AI agents in philosophy and its definition (Subsection 2.1). Subse-\nq\nuently, we review the development of AI agentsthrough the lens oftechnologicaltrends (Subsection 2.2).\nF\ninally, we introduce the key characteristics of agents and demonstrate why LLMs are suitable to serve\nas the foundation of AI agents (Subsection 2.3).\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:3\n2.1 Origin of AI agent\n“Agent”is a concept with a long history that spans acrossvariousfields. It is derivedfrom a philosophical\norigin, triggering a discussion on whether artificial products can possess agency in a philosophical sense.\nThen, related concepts were introduced into the field of AI, forming the basis of AI agents.\nAgent in philosophy. The initial idea of an “agent” has a philosophical origin, with influential\nthinkers like Aristotle and Hume, contributing to its conceptualization [2 ]. In a general sense, an “agent”\nis\nan entity with the capacity to act, and the term “agency” denotes the exercise or manifestation of\nthis capacity [2 ]. In a narrow sense, “agency” is usually used to refer to the perfor mance of intentional\nactions; and correspondingly, “agent” denotes entities that possess desires, beliefs, intentions, and the\nability to act [25 –28]. Importantly, agents encompass not only individual human beings bu t also other\nentities in both the physical and virtual world.\nIntroduction of agents into AI. InAI, anagentisdefined asanartificialentity capableofperceiving\nits environment, making decisions, and taking actions using sensors and actuators [1 ,3]. As Wooldridge\ne\nt al. [3] stated that we can define AI by saying that it is a subfield of compute r science that aims to\ndesign and build computer-based agents that exhibit aspects of intelligent behavior. So we can treat\n“agent” as a central concept in AI. When the concept of agent is introduced into AI, its meaning evolves.\nIn philosophy, an agent can be a human, an animal, or even a concept or entity with autonomy [2 ].\nH\nowever, in the field of AI, an agent is a computational entity [3 ,29]. Since it is difficult to determine\nif\nthey possess internal desires or consciousness, many AI researchers, including Alan Turing, suggest\ntemporarily setting aside the question of whether an agent is “actually” thinking or literally possesses a\n“mind” [30 ]. Instead, researchers employ other attributes to help describe a n agent, such as properties\nof autonomy, reactivity, pro-activeness, and social ability [3 ,31].\nI\nt might come as a surprise that researchers within the mainstream AI community devoted relatively\nminimal attention to concepts related to agents until the mid to late 1980s. Nevertheless, there has been\na significant surge of interest in this topic within the realms of computer science and artificial intelligence\ncommunities since then [32 –35].\n2\n.2 Technological trends in agent research\nThe evolution of AI agents has undergone several main stages, and here we take the lens of technological\ntrends to review its development briefly.\nSymbolicagents. IntheearlystagesofAIresearch,thepredominantapproachutilizedissymbolicAI,\ncharacterized by its reliance on symbolic logic [36 ,37]. This approach employs logical rules and symbolic\nr\nepresentations to encapsulate knowledge and facilitate reasoning processes. Early AI agents are built\nbased on this approach [38 ], and they primarily focused on two problems: the transduction prob lem and\nthe representation/reasoning problem [39 ]. These agents aim to emulate human thinking patterns. They\np\nossess explicit and interpretable reasoning frameworks, and due to their symbolic nature, they exhibit a\nhigh degreeofexpressivecapability [7 ,8,40]. Aclassicexampleofthis approachis knowledge-basedexpert\ns\nystems. However, symbolic agents face limitations in handling uncertainty and large-scale real-world\nproblems [13 ,14]. Additionally, due to the intricacies of symbolic reasoning algorithms, it is challenging\nto find an efficient algorithm capable of producing meaningful results within a finite timeframe [14 ,41].\nR\neactive agents. Different from symbolic agents, reactive agents do not use complex symbolic\nreasoning. Instead, they primarily focus on the interaction between the agent and its environment,\nemphasizing quick and real-time responses [9 ,10,14,42,43]. These agents are mainly based on a sense-act\nlo\nop, efficiently perceiving and reacting to the environment. The design of such agents prioritizes direct\ninput-output mappings rather than intricate reasoning and symbolic operations [34 ]. However, reactive\na\ngents also have limitations. They typically require fewer computational resources, enabling quicker\nresponses, but they might lack complex higher-level decision-making and planning capabilities.\nReinforcement learning-based agents. With the improvement of computational capabilities and\ndata availability, along with a growing interest in simulating interactions between intelligent agents and\ntheir environments, researchers have begun to utilize reinforcement learning methods to train agents for\ntackling more challenging and complex tasks [11 ,12,44,45]. The primary concern in this field is how to\ne\nnable agents to learn through interactions with their environments, enabling them to achieve maximum\ncumulative rewards in specific tasks [15 ]. Initially, reinforcement learning (RL) agents are primarily\nb\nased on fundamental techniques such as policy search and value function optimization, exemplified by\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:4\nQ-learning [46 ] and SARSA [47 ]. With the rise of deep learning, the integration of deep neural netwo rks\nand reinforcement learning, known as deep reinforcement learning (DRL), has emerged [48 ,49]. This\na\nllows agents to learn intricate policies from high-dimensional inputs, leading to numerous significant\naccomplishments like AlphaGo [50 ] and DQN [51 ]. The advantage of this approach lies in its capacity\nt\no enable agents to autonomously learn in unknown environments, without explicit human intervention.\nThis allows for its wide application in an array of domains, from gaming to robot control and beyond.\nNonetheless, reinforcement learning faces challenges including long training times, low sample efficiency,\nand stability concerns, particularly when applied in complex real-world environments [15 ].\nA\ngents with transfer learning and meta learning. Traditionally, training a reinforcement learn-\ning agent requires huge sample sizes and long training time, and lacks generalization capability [52 –56].\nC\nonsequently, researchersintroducetransferlearningtoexpedite anagent’slearningon newtasks[57 –59].\nT\nransfer learning reduces the burden of training on new tasks and facilitates the sharing and migration of\nknowledge across different tasks, thereby enhancing learning efficiency, performance, and generalization\ncapabilities. Furthermore, meta learning has also been introduced to develop agents [60 –64]. Meta learn-\nin\ng focuses on learning how to learn, enabling an agent to swiftly infer optimal policies for new tasks from\na small number of samples [65 ]. Such an agent, when confronted with a new task, can rapidly adjus t its\nlearning approach by leveraging acquired general knowledge and policies, consequently reducing the re-\nliance on a large volume of samples. However, when there exist significant disparities between source and\ntarget tasks, the effectiveness of transfer learning might fall short of expectations and negative transfer\nmight occur [66 ,67]. Additionally, the substantial amount of pre-training and large samp le sizes required\nby meta learning make it hard to establish a universal learning policy [61 ,68].\nL\narge language model-based agents. As LLMs have demonstrated impressive emergent capabili-\nties and have gained immense popularity [18 –20,69], researchers have started to leverage these models to\nc\nonstruct AI agents, known as LLM-based agents [16 ,21,22,70]. Specifically, they employ LLMs as the\nc\nore component of the brain or controller of these agents and expand their perceptual and action space\nthrough strategies such as multimodal perception and tool utilization [71 –75]. These LLM-based agents\nc\nan exhibit reasoning and planning abilities comparableto symbolic agents through techniques like chain-\nof-thought (CoT) and problem decomposition [76 –82]. They can also acquire interactive capabilities with\nt\nhe environment, akin to reactive agents, by learning from feedback and performing new actions [83 –85].\nS\nimilarly, LLMs undergo pre-training on large-scale corpora and demonstrate the capacity for few-shot\nand zero-shot generalization, allowing for seamless transfer between tasks without the need to update\nparameters [69 ,86–88]. LLM-based agents have been applied to various real-world scenario s, such as\nsoftware development [89 ,90] and scientific research [91 ]. Due to their natural language comprehension\na\nnd generation capabilities, they can interact with each other seamlessly, giving rise to collaboration and\ncompetition among multiple agents [89 ,90,92,93]. Furthermore, some studies suggested that allowing\nm\nultiple agents to coexist can lead to the emergence of social phenomena [16 ].\n2\n.3 Why is LLM suitable as the foundation of agent?\nAs mentioned before, researchers have introduced several properties to help describe and define agents\nin the field of AI. In this section, we will discuss the key properties, elucidate their relevance to LLMs,\nand thereby expound on why LLMs are highly suited to serve as the foundation of AI agents.\nAutonomy. Autonomy means that an agent operates without direct intervention from humans or\nothers and possesses a degree of control over its actions and internal states [3 ,94]. This implies that an\na\ngent should not only possess the capability to follow explicit human instructions for task completion\nbut also exhibit the capacity to initiate and execute actions independently. LLMs can demonstrate\na form of autonomy through their ability to generate human-like text, engage in conversations, and\nperform various tasks without detailed step-by-step instructions [95 ,96]. Moreover, they can dynamically\na\ndjust their outputs based on environmental input, reflecting a degree of adaptive autonomy [17 ,21,85].\nF\nurthermore, they can showcase autonomy through exhibiting creativity like coming up with novel ideas,\nstories, or solutions that have not been explicitly programmed into them [97 ,98]. This implies a certain\nle\nvel of self-directed exploration and decision-making. Applications like Auto-GPT [95 ] exemplify the\ns\nignificant potential of LLMs in constructing autonomous agents. Simply by providing them with a task\nand a set of available tools, they can autonomously formulate plans and execute them to achieve the\nultimate goal.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:5\nReactivity. Reactivity in an agent refers to its ability to respond rapidly to immediate changes and\nstimuli in its environment [31 ]. This implies that the agent can perceive alterations in its surrounding s\nand promptly take appropriate actions. Traditionally, the perceptual space of language models has been\nconfined to textual inputs, while the action space has been limited to textual outputs. Recently, advances\nin multimodal fusion techniques expand their perceptual space to rapidly process visual and auditory\ninformation from the environments [19 ,99,100]. Similarly, it is also feasible to expand the action space\no\nf LLMs through embodiment techniques [101 ,102] and tool usage [73 ,75]. These advancements enable\nL\nLMs to effectively interact with the real-world physical environment and carry out tasks within it. One\nmajor challenge is that LLM-based agents, when performing non-textual actions, require an intermediate\nstep of generating thoughts or formulating tool usage in the textual form before eventually translating\nthem into concrete actions. This intermediary process consumes time and reduces the response speed.\nHowever, this is analogous to human behavioral patterns, where the principle of “think before you act”\nis observed [103 ,104].\nP\nro-activeness. Pro-activeness denotes that agents do not merely react to their environments; they\npossess the capacity to display goal-oriented actions by proactively taking the initiative [31 ]. This prop-\ne\nrty emphasizes that agents can reason, make plans, and take proactive measures in their actions to\nachieve specific goals or adapt to environmental changes. Although intuitively the paradigm of next\ntoken prediction in LLMs may not possess intention or desire, research has shown that they can implic-\nitly generate representations of these states and guide the model’s inference process [105 –107]. LLMs\nh\nave demonstrated a strong capacity for generalized reasoning and planning. By prompting LLMs with\ninstructions like “let’s think step by step”, we can elicit their reasoningabilities, such as logicaland math-\nematical reasoning [76 –78]. Similarly, LLMs have shown the emergent ability of planning in forms of g oal\nreformulation [80 ,108], task decomposition [79 ,109], and adjusting plans in response to environmental\nc\nhanges [81 ,110].\nS\nocial ability. Social ability refers to an agent’s capacity to interact with other agents, including\nhumans, using agent-communication languages [111 ]. Large language models exhibit strong natural\nla\nnguage interaction abilities like comprehension and generation [17 ,112,113]. Compared to structured\nla\nnguages or other communication protocols, such capability enables them to interact with other mod-\nels or humans in an interpretable manner, laying the foundation for the social ability for LLM-based\nagents [16 ,89]. Many researchers have demonstrated that LLM-based agents c an enhance task perfor-\nmance through social behaviors such as collaboration and competition [89 ,92,114,115]. By inputting\ns\npecific prompts, LLMs can also play different roles, thereby simulating the social division of labor in\nthe real world [90 ]. Furthermore, when we place multiple agents with distinct identities int o a society,\nemergent social phenomena can be observed [16 ].\n3\nBirth of an agent: construction of LLM-based agents\nThe principle of “Survival of the Fittest” [116 ] highlights the need for cognitive abilities to adapt to the\ne\nxternal environment and respond to changes for individual survival. Inspired by this and the definition\nof AI agents, we present a general conceptual framework of an LLM-based agent composed of three key\ncomponents: brain, perception, and action (see Figure 2). We first describe the structure and working\nm\nechanism of the “brain”, which is the cognitive core of an AI agent (Subsection 3.1). It not only stores\nk\nnowledge and memories but also undertakes indispensable functions like decision-making, exhibiting the\nintelligence of an agent. Next, we introduce the perception module (Subsection 3.2). Its core purpose is\nt\no broaden the agent’s perception space from a text-only domain to a multimodal sphere, which equips\nthe agent to grasp and utilize information from its surroundings more effectively. Finally, we present the\naction module designed to expand the action space of an agent (Subsection 3.3). Specifically, the agent\nis\nempowered to adapt to environmental changes, provide feedbacks, and even influence and mold the\nenvironment.\nThe framework can be tailored for different application scenarios; i.e., not every specific component\nwill be used in all studies. In general, agents operate in the following workflow: First, the perception\nmodule, correspondingtohumansensorysystems, perceiveschangesin theexternalenvironmentandthen\nconverts multimodal information into an understandable representation for the agent. Subsequently, the\nbrain module, serving as the control center, engages in activities such as thinking, decision-making, and\noperations with storage including memory and knowledge. Finally, the action module, corresponding\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:6\nFigure 2 (Color online) Conceptual framework of an LLM-based agent with three components: brain, perception, and action.\nServing as the controller, the brain module undertakes basic tasks like memorizing, reasoning, and planning. The perceptionmodule\nperceives and processes multimodal information from the external environment, and the action module carries out the execution\nand interacts with the surroundings. Here is an example that illustrates the workflow: When a human asks whether it will rain,\nthe perception module converts the instruction into a specific representation. Then the brain module begins to reason according to\nthe current weather and the weather reports on the internet. Finally, the action module responds and hands the umbrella to the\nhuman. By repeating the above process, the LLM-based agent can continuously get feedback and interact with the environment.\nto human limbs, carries out the execution and leaves an impact on the surroundings. By repeating the\nabove process, an agent can continuously get feedback and interact with the environment.\n3.1 Brain\nThe human brain is a sophisticated structure comprised of a vast number of interconnected neurons,\ncapable of processing complex information, generating diverse thoughts, controlling behaviors, and even\ncreating art and culture [117 ]. Much like humans, the brain component serves as the central cont roller\nof an AI agent. In our framework, the brain module is primarily composed of an LLM.\nAfter receiving the information processed by the perception module, the brain module first turns to\nstorage, retrieving in knowledge (Subsection 3.1.1) and recalling from memory (Subsection 3.1.2). These\no\nutcomes aid the agent in planing, reasoning, and making decisions (Subsection 3.1.3). Additionally, the\nb\nrainmodulemaymemorizetheagent’spastobservations,thoughts,andactionsintheformofsummaries,\nvectors, or other data structures. Meanwhile, it can also update the knowledge such as common sense\nand domain knowledge for future use. The LLM-based agent may also adapt to unfamiliar scenarios with\nits inherent generalization and transferability (Subsection 3.1.4). In the subsequent sections, we delve\nin\nto a detailed exploration of these extraordinary facets of the brain module.\n3.1.1Knowledge\nLLMs aretypically imbued with large-scaledata throughsemi-supervised learningduringthe pre-training\nphase [118 ,119]. They can encode a wide range of knowledge into their parameters an d respond correctly\nto various types of queries after pre-training [120 ]. Furthermore, the knowledge can assist LLM-based\na\ngents in making informed decisions [121 ]. Such knowledge can be broadly categorized into the following\nt\nypes.\n•Commonsense knowledge. Commonsense knowledge [122 –124] refers to general world facts that\na\nre typically taught to most individuals at an early age. For example, people commonly know that\nmedicine is used for curing diseases, and umbrellas are used to protect against rain. Such information is\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:7\nusually not explicitly mentioned in the context. Therefore, models lacking the corresponding common-\nsense knowledge may fail to grasp or misinterpret the intended meaning [125 ]. Similarly, agents without\nc\nommonsense knowledge may make incorrect decisions, such as not bringing an umbrella when it rains\nheavily.\n•Professional domain knowledge. Professional domain knowledge refers to the knowledge asso-\nciated with a specific domain like programming [124 ,126,127], mathematics [128 ], medicine [129 ], etc.\nI\nt is essential for models to effectively solve problems within a particular domain [130 ]. For example,\nm\nodels designed to perform programming tasks need to possess programming knowledge. This kind of\nknowledge is very important for domain-specific agents because only with this knowledge can they make\nreasonable decisions.\n3.1.2Memory\nIn our framework, “memory” stores the agent’s past observations, thoughts, and actions [131 ]. This\nm\nemory enables agents to use past experiences for strategy formulation and decision-making, similar to\nhow humans do [132 –134]. It assists agents in handling complex problems by allowing them to revis it\npast strategies and adapt to new environments.\nHowever, as interactions in LLM-based agents increase, two main issues emerge. First, the length of\nhistorical data can exceed the processing capacity of Transformer architecture-basedLLM agents, leading\nto possible data truncation. Second, with the accumulation of extensive data, it becomes increasingly\ndifficult for agents to retrieve and connect relevant memories, risking misaligned responses in ongoing\ncontexts. Therefore, appropriate memory compression and retrieval techniques are key to enhancing an\nagent’s memory capabilities.\nMethods for memory compression. We delve into two primary methods designed to compress\nmemory, ensuring efficient recall and analysis.\n•Summarizing memory. The first strategy for improving memory efficiency is memory summa-\nrization. This method summarizes historical interactions and stores them in natural language. Various\ntechniques have been proposed for memory summarization. Using prompts, some methods succinctly\nintegrate memories [135 ], while others emphasize reflective processes to create condensed memory repre-\nsentations [16 ,136]. Hierarchical methods streamline dialogues into both daily snapshots and overarching\nsummaries [137 ]. Notably, specific strategies translate environmental feedback in to textual encapsula-\ntions, bolstering agents’ contextual grasp for future engagements [138 ].\n•C\nompressing memories with vectors or data structures. Using appropriate data structures,\nLLM-basedagentscanstorehistoricalinteractionsmoreefficiently. Notably,severalmethodologiesleanon\nembedding vectors for memory sections, plans, or dialogue histories [90 ,137,139,140]. Another approach\nt\nranslatessentences into triplet configurations[141 ], while some perceive memoryas a unique data object,\nf\nosteringvaried interactions[142 ]. Furthermore, ChatDB [143 ]and DB-GPT [144 ]opt forSQL databases,\nw\nhich allows for data manipulation via SQL queries.\nMethods for memory retrieval. When an agent interacts with its environment, it is imperative\nto retrieve the most appropriate content from its memory. This ensures that the agent accesses relevant\nand accurate information to make decision [137 ,140]. A representative approach in automated retrieval\nc\nonsiders three metrics: recency, relevance, and importance. The memory score is determined as a\nweighted combination of these metrics, with memories having the highest scores being prioritized in the\nmodel’s context [16 ]. Some work introduces the concept of interactive memory objects , representing\ndialogue history that can be moved, edited, deleted, or combined through summarization [142 ]. Users\nc\nan view and manipulate these objects, influencing how the agent perceives the dialogue. Similarly, other\nstudies allow for memory operations like deletion based on specific commands provided by users [143 ].\nS\nuch methods ensure that the memory content aligns closely with user expectations.\n3.1.3Reasoning and planning\nReasoning. Reasoning, crucial in human intellectual activities for problem-solving, decision-making,\nand critical analysis, is underpinned by evidence and logic [145 –147]. Deductive, inductive, and abduc-\nt\nive reasoning are key forms recognized in these endeavors [148 ]. For LLM-based agents, like humans,\nr\neasoning capacity is crucial for addressing complex tasks [19 ].\nF\nor LLMs, the reasoning ability is considered an emergent ability, i.e., such an ability emerges once\nthe language model reaches a certain scale in size [20 ,149]. Notably, the CoT method [76 ,77] has been\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:8\ndemonstrated to elicit the reasoning capacities of LLMs by guiding them to generate rationales before\noutputting answers. Other techniques like self-consistency [78 ], self-polish [80 ], self-refine [150 ], and\ns\nelection-inference[151 ]alsoenhanceLLMperformance. Tofurtherimprovethereasoning abilityofLLMs\norLLM-basedagents, currentresearchprimarilyexploressupervised fine-tuning (SFT) methods [152 ]and\nin\nteractive training approaches [153 ,154].\nP\nlanning. Planning is a key strategy employed by humans when facing complex tasks. For humans,\nplanning helps organizethoughts, set objectives, and determine the steps to achievethoseobjectives[155 –\n157]. Similar to humans, the ability to plan is crucial for agents, and centra l to this planning module is\nthe capacity for reasoning [158 –160]. This offers a structured thought process for agents based on LL Ms.\nThrough reasoning, agents break down complex tasks into manageable sub-tasks, devising appropriate\nplans for each [161 ,162]. Moreover, as tasks progress, agents can employ introspection t o modify their\nplans, ensuring they align better with real-world circumstances, facilitating adaptive and successful task\nexecution. Typically, planning comprises two stages: plan formulation and plan reflection.\n•Plan formulation. During plan formulation, agents generally decompose an overarching task into\nnumerous sub-tasks, and various approaches have been proposed in this phase. Notably, some studies\nadvocated for LLM-based agents to decompose problems comprehensively in a single step, formulating\na complete plan at once and then executing it sequentially [79 ,163–165]. In contrast, other studies\nlik\ne the CoT-series employ an adaptive strategy, where they plan and address sub-tasks one at a time,\nallowing for more fluidity in handling intricate tasks in their entirety [76 ,77,166]. Additionally, some\nm\nethods emphasize hierarchical planning [167 ,168], while others underscore a strategy in which final\np\nlans are derived from reasoning steps structured in a tree-like format. The latter approach argues that\nagents should assess all possible paths before finalizing a plan [78 ,169–171]. While LLM-based agents\nd\nemonstrate a broad scope of general knowledge, they can occasionally face challenges when tasked with\nsituations that require expertise knowledge. Enhancing these agents by integrating them with planners\nof specific domains has shown to yield better performance [109 ,115,172,173].\n•P\nlan reflection. After formulating a plan, it is imperative to reflect upon and evaluate its merits.\nLLM-basedagentsemployinternalfeedbackmechanisms,often drawinginsightsfrompre-existingmodels,\nto hone and enhance their strategies and planning approaches[138 ,150,174,175]. To better align with hu-\nm\nan values and preferences, agentsactively engagewith humans, allowingthem to rectify misunderstand-\nings and assimilate this tailored feedback into their planning methodology [89 ,176,177]. Furthermore,\nt\nhey could gatherfeedback from tangible orvirtual surroundings, such as cues from taskaccomplishments\nor post-action observations, aiding them in revising and refining their plans [72 ,82,178–180].\n3\n.1.4Transferability and generalization\nThe remarkable nature of the human brain is largely attributed to its high degree of plasticity and\nadaptability. It can continuously adjust its structure in response to external stimuli and internal needs,\nthereby adapting to different environments and tasks. These years, plenty of research indicates that\npre-trained models can learn universal language representations [181 –183], and with only a small amount\no\nf data for fine-tuning, it can demonstrate excellent performance in downstream tasks [184 ]. There\nis\nno need to train new models from scratch, which saves a lot of computation resources. However,\nmodels trained through this task-specific fine-tuning, usually lack versatility and generalizability to other\ntasks. Instead of merely functioning as a static knowledge repository, LLM-based agents exhibit dynamic\nlearning ability which enables them to adapt to novel tasks swiftly and robustly [18 ,86,87].\nU\nnseen task generalization. Studies show that instruction-tuned LLMs exhibit zero-shot gen-\neralization without the need for task-specific fine-tuning [18 ,19,86–88]. Notably, LLMs can complete\nu\nnfamiliar tasks by following the instructions based on their own understanding. One of the imple-\nmentations is multi-task learning, for example, FLAN [86 ] finetunes language models on a collection of\nt\nasks described via instructions, and T0 [87 ] introduces a unified framework that converts every language\np\nroblem into a text-to-text format. Despite being purely a language model, GPT-4 [19 ] demonstrates\nr\nemarkable capabilities in a variety of domains and tasks, including abstraction, coding, mathematics,\nmedicine, law, and others [24 ]. Promisingly, such generalization capability can be further enhanced by\nscaling up the model size and the quantity or diversity of training instructions [75 ,185].\nI\nn-context learning. Numerous studies indicate that LLMs can perform a variety of complex tasks\nthrough in-context learning (ICL), which involves the models’ ability to learn from a few examples within\na given context [186 ]. Few-shot in-context learning enhances the performance by conc atenating the\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:9\noriginal input with several complete examples as prompts to enrich the context [69 ]. The key idea of\nI\nCL is learning from analogy, akin to the learning process of humans [187 ]. Besides, since the prompts\na\nre written in natural language, the interaction is interpretable and changeable, making it easier to\nincorporate human knowledge [76 ,188]. Unlike the supervised learning process, ICL does not involve\nfi\nne-tuning or parameter updates, greatly reducing computation costs for adapting the models to new\ntasks. Beyond texts, researchers also explore the potential ICL capabilities in different multimodal\ntasks [189 –194], making it possible for agents to be applied to large-scale real-world ta sks.\n3.2 Perception\nBoth humans and animals rely on sensory organs like eyes and ears to gather information from their\nsurroundings. These perceptual inputs are converted into neural signals and sent to the brain for pro-\ncessing [195 ,196], allowing humans to perceive and interact with the world. Similarly, it is cr ucial for\nLLM-based agents to receive information from various sources and modalities. This expanded perceptual\nspace helps agents better understand their environment, make informed decisions, and excel in a broader\nrange of tasks, making it an essential development direction. Agent handles this information to the Brain\nmodule for processing through the perception module.\nIn this section, we introduce how to enable LLM-based agents to acquire multimodal perception ca-\npabilities, encompassing textual (Subsection 3.2.1), visual (Subsection 3.2.2), and auditory inputs (Sub-\ns\nection3.2.3). We also consider other potential input forms (Subsection 3.2.4) such as tactile feedback,\ng\nestures, and 3D maps to enrich the agent’s perception domain and enhance its versatility.\n3.2.1Textual input\nText serves as a conduit for conveying data, information, and knowledge, making text communication\none of the most important ways through which humans interact with the world. LLM-based agents al-\nready have the fundamental ability to communicate with humans through textual input and output [95 ].\nI\nn a user’s textual input, aside from the explicit content, lie concealed beliefs, desires, and intentions.\nUnderstanding implied meanings is crucial for the agent to grasp the potential and underlying inten-\ntions of human users, thereby enhancing its communication efficiency and quality with users. How-\never, understanding implied meanings within textual input remains challenging for current LLM-based\nagents [75 ,197]. For example, some studies [113 ,198–200] employed reinforcement learning to perceive\nim\nplied meanings and models feedback to derive rewards. This helps deduce the speaker’s preferences,\nleading to more personalized and accurate responses from the agent. Additionally, as the agent is de-\nsigned for use in complex real-world situations, it will inevitably encounter many entirely novel tasks.\nUnderstanding text instructions for unknown tasks places higher demands on the agent’s text percep-\ntion abilities. As described in Subsection 3.1.4, an LLM that has undergone instruction tuning [86 ] can\ne\nxhibit remarkable zero-shot instruction understanding and generalization abilities, eliminating the need\nfor task-specific fine-tuning.\n3.2.2Visual input\nAlthough LLMs excel in language comprehension [19 ,201] and multi-turn conversations [202 ], they in-\nh\nerently lack visual perception and can only understand discrete textual content. Visual input usually\ncontains a wealth of information about the world, including properties of objects, spatial relationships,\nscene layouts, and more in the agent’s surroundings. Therefore, integrating visual information with data\nfrom other modalities can offer the agent a broader context and a more precise understanding [101 ],\nd\neepening the agent’s perception of the environment.\nTo help the agent understand the information contained within images, a straightforward approach\nis to generate corresponding text descriptions for image inputs, known as image captioning [203 –207].\nC\naptions can be directly linked with standard text instructions and fed into LLM-based agents. This ap-\nproach is highly interpretable and does not require additional training for caption generation, which can\nsave a significant number of computational resources. However, caption generation is a low-bandwidth\nmethod [101 ,208], and it may lose a lot of potential information during the conversion pr ocess. Further-\nmore, the agent’s focus on images may introduce biases.\nThesecondapproachto endowinganLLM-basedagentwith visualunderstandingcapabilitiesismodal-\nity fusion, where a visual encoder is integrated with an LLM at the embedding level [209 ,210]. Freezing\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:10\none or both of them during training is a widely adopted paradigm that achieves a balance between train-\ning resources and model performance [211 ]. However, LLMs cannot directly understand the output of a\nv\nisual encoder, so it is necessary to convert the image encoding into embeddings that LLMs can compre-\nhend. In other words, it involves aligning the visual encoder with the LLM. This usually requires adding\nan extra learnable interface layer between them. For example, BLIP-2 [211 ] and InstructBLIP [212 ] use\nt\nhe querying transformer (Q-Former) module as an intermediate layer between the visual encoder and\nthe LLM [212 ]. At the same time, some researchers adopt a computationally efficien t method using a\nsingle projection layer to achieve visual-text alignment, reducing the need for training additional param-\neters [99,210,213]. Moreover, the projection layer can effectively integrate with the le arnable interface to\nadapt the dimensions of its outputs, making them compatible with LLMs [214 –217].\n3\n.2.3Auditory input\nAuditory information constitutes a crucial component of world information. When an agent possesses\nauditory capabilities, it can improve its awareness of interactive content, the surrounding environment,\nand even potential dangers. While there are numerous well-established models and approaches [218 –220]\nf\nor processing audio as a standalone modality, these models often only excel at specific tasks. Given\nthe excellent tool-using capabilities of LLMs (which will be discussed in detail in Subsection 3.3), a\nv\nery intuitive idea is that the agent can use LLMs as control hubs, invoking existing toolsets or model\nrepositories in a cascading manner to perceive audio information.\nFurthermore, an audio spectrogram can serve as a medium to endow LLM-based agents with auditory\ncapabilities,asitoffersaclearrepresentationofhowthefrequencyspectrumofanaudiosignalevolvesover\ntime [221 ]. For a segment of audio data over a period of time, it can be abstract ed into a finite-length\naudio spectrogram. An audio spectrogram has a 2D representation, which can be visualized as a flat\nimage. Hence, some research [222 ,223] efforts aim to migrate perceptual methods from the visual domain\nt\no audio. Audio spectrogram transformer (AST) [222 ] employs a Transformer architecture similar to ViT\nt\no process audio spectrogram images. By segmenting the audio spectrogram into patches, it achieves\neffective encoding of audio information.\n3.2.4Other input\nLLM-based agents are expected to be equipped with richer perception modules in the future. They could\nperceive and understand diverse modalities in the real world, much like humans. For example, agents\ncould have unique touch and smell organs, allowing them to gather more detailed information when\ninteracting with objects. At the same time, agents can also be equipped with hardware devices, such as\nGPS, Lidar, and other sensors, having a clearer sense of the spatial position, temperature, and brightness\nand taking environment-aware actions.\n3.3 Action\nAfter humans perceive their environment, their brains integrate, analyze, and reason to make decisions.\nSubsequently, they employ their nervous systems to control their bodies, enabling adaptive or creative\nactions in response to the environment, such as engaging in conversation, evading obstacles, or starting a\nfire. When an agent possesses a brain-like structure with capabilities of knowledge, memory, reasoning,\nplanning, and generalization, as well as multimodal perception, it is also expected to possess a diverse\nrange of actions akin to humans to respond to its surrounding environment. In constructing such an\nagent, the action module receives action sequences sent by the brain module and carries out actions to\ninteract with the environment.\nThis section begins with textual output (Subsection 3.3.1), which is the inherent capability of LLM-\nb\nased agents. Next we talk about the tool-using capability of LLM-based agents (Subsection 3.3.2),\nw\nhich has proved effective in enhancing their versatility and expertise. Finally, we discuss equipping the\nLLM-basedagentwith embodiedactiontofacilitateitsgroundinginthephysicalworld(Subsection 3.3.3).\n3\n.3.1Textual output\nRecently, the rise and development of Transformer-based generative LLMs have endowed LLM-based\nagents with inherent language generation capabilities [19 ,224]. The text quality they generate excels in\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:11\nvarious aspects such as fluency, relevance, diversity, controllability [112 ,225–227]. Consequently, LLM-\nb\nased agents can be exceptionally strong language generators.\n3.3.2Tool using\nTools are extensions of the capabilities of humans. When faced with complex tasks, humans employ tools\nto simplify task-solving and enhance efficiency, freeing time, and resources. Similarly, agents have the\npotential to accomplishcomplextasksmoreefficiently and with higher qualityifthey alsolearnto use and\nutilize tools [75 ]. LLM-based agents have limitations because of hallucination [228 ], the lack of training\nd\nata and tuning for specific fields [229 ], untransparent decision-making process [230 ] and susceptibility\nt\no adversarial attacks [231 ]. Fortunately, specialized tools enable LLM-based agents to enhanc e their\nexpertise, adapt domain knowledge, and be more suitable for domain-specific needs in a pluggable form.\nThey also exhibit stronger interpretability and robustness. LLM-based agents not only require the use of\ntools, but are also well-suited for tool integration. LLMs show remarkable reasoning and decision-making\nabilities in complex interactive environments [78 ] and significant potential in intent understanding and\no\nther aspects [19 ,232–234]. These make LLM-based agents proficient tool users.\nL\nearning to use tools. Leveragingthe powerfulzero-shotand few-shotlearningabilitiesofLLMs[69 ,\n235], agents can acquire knowledge about tools by utilizing zero-shot pro mpts that contain descriptions\nof tool functionalities and parameters, or few-shot prompts that provide demonstrations of specific tool\nusage scenarios and corresponding methods [73 ,236]. These learning approaches parallel human methods\no\nf learning by consulting tool manuals or observing others using tools [75 ]. Besides configurations and\nd\nemonstrations, agents can also learn from feedback received from both the environment and humans [18 ,\n237,238]. Environmental feedback encompasses result feedback on wheth er actions have successfully\ncompleted the task and intermediate feedback that captures changes in the environmental state caused\nby actions; human feedback comprises explicit evaluations and implicit behaviors, such as clicking on\nlinks [75].\nI\ntiscrucialtoimprovetheagent’sgeneralizationabilityintoolusagetohandlecomplicatedscenariosas\nwell as continuously evolving new tools. To accomplish this, agents need to grasp the common principles\norpatternsintoolusagestrategies, whichcanpotentiallybeachievedthroughmeta-toollearning[239 ]. In\na\nddition, techniques like curriculum learning [240 ] can enhance the agent’s understanding of relationships\nb\netweensimpleandcomplextools, suchashowcomplextoolsarebuilt onsimpleronesandallowagentsto\neffectively discern nuances across various application scenarios and transfer previously learned knowledge\nto new tools [75 ]. Previous studies showed that LLM-based agents not only have the ability to generalize\nthe use of existing tools, but can also make new tools they need by generating executable programs, or\nintegrating existing tools into more powerful ones [75 ,241,242].\nT\nools expanding the action space of LLM-based agents. With the help of tools, agents can\nutilize various external resources such as web applications and other LMs during the reasoning and\nplanning phase [73 ]. This process can provide information with high expertise, reliability, d iversity, and\nquality for LLM-based agents, facilitating their decision-making and action. For example, search-based\ntools can improve the scope and quality of the knowledge accessible to the agents with the aid of external\ndatabases, knowledgegraphs, andweb pages, while domain-specifictoolscan enhanceanagent’sexpertise\nin the correspondingfield [243 ,244]. Some researchershave alreadydeveloped LLM-based controllers that\ngenerateSQLstatementstoquerydatabases, ortoconvertuserqueriesintosearchrequestsandusesearch\nengines to obtain the desired results [71 ,143]. What is more, LLM-based agents can use scientific tools to\ne\nxecute tasks like organic synthesis in chemistry, or interface with Python interpreters to enhance their\nperformance on intricate mathematical computation tasks [245 ,246].\nA\nlthough the tools mentioned before enhance the capabilities of agents, the medium of interaction\nwith the environment remains text-based. However, tools are designed to expand the functionality of\nlanguage models, and their outputs are not limited to text. Tools for non-textual output can diversify the\nmodalities of agent actions, thereby expanding the application scenarios of LLM-based agents [163 ,247].\nF\nor example, image processing and generation can be accomplished by an agent that draws on a visual\nmodel [248 ].\n3\n.3.3Embodied action\nIn the pursuit of AGI, the embodied agent is considered a pivotal paradigm. The embodiment hypoth-\nesis [249 ] draws inspiration from the human intelligence development process, posing that an agent’s\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:12\nintelligence arises from continuous interaction and feedback with the environment rather than relying\nsolely on well-curated textbooks [250 ]. Similarly, people anticipate that LLM-based agents should be\nc\napable of actively perceiving, comprehending, and interacting with physical environments, making deci-\nsions, and generating intended behaviors to modify the environment based on LLM’s internal knowledge.\nWe collectively term these as embodied actions, which enable agents’ ability to interact with and com-\nprehend the world in a manner closely resembling human behavior.\nThe potential of LLM-based agents for embodied actions. Despite the extensive success of\nRL-based embodiment [50 ,51,251], it does have certain limitations in some aspects. For example, RL\na\nlgorithms face limitations in terms of cost efficiency, generalization, and complex problem reasoning due\ntochallengesinmodelingthedynamicenvironmentandtherelianceonrewardsignalrepresentations[252 ].\nR\necent studies haveindicated that leveragingthe rich internalknowledgeacquired duringthe pre-training\nof LLMs can effectively alleviate these issues [101 ,171,178,253].\n•C\nost efficiency. Some on-policy algorithms struggle with sample efficiency as they require iter-\natively sampled data for policy updates while gathering enough embodied data for high-performance\ntraining is costly. The constraint is also found in some end-to-end models [254 –256]. By leveraging\nt\nhe intrinsic knowledge from LLMs, agents like PaLM-E [101 ] jointly train robotic data with general\nv\nisual-language data to achieve significant transfer ability in embodied tasks while also showcasing that\ngeometric input representations can improve training data efficiency.\n•Embodied action generalization. As discussedin Subsection 3.1.4, anagent’scompetence should\ne\nxtend beyond specific tasks. Different from the majority of RL algorithms [82 ,257–259], fine-tuned by\nt\nasks in diverse forms and types, LLMs have showcased remarkable cross-task generalization capabili-\nties [260,261]. Further, natural language serves both as a means to interact wit h the environment and as\na medium for transferring foundational skills to new tasks [262 ]. SayCan [163 ] decomposes task instruc-\nt\nions presented in prompts using LLMs into corresponding skill commands, but in partially observable\nenvironments, limited priorskills often do not achievesatisfactoryperformance[82 ]. To addressthis, Voy-\na\nger [177] introduces the skill library component to continuously collect novel self-verified skills, which\nallows for the agent’s lifelong learning capabilities.\n•Embodied action planning. Planning is a pivotal strategy for agents when addressing complex\nproblems. In traditional hierarchical reinforcement learning (HRL) methods, the high-level policy con-\nstraints sub-goals for the low-level policy which produces appropriate action signals [263 –265]. Similar\nt\no the role of high-level policies, LLMs with emerging reasoning abilities [20 ] can be seamlessly applied\nt\no complex tasks in a zero-shot or few-shot manner [76 ,78–80]. In addition, external feedback from the\ne\nnvironment can further enhance LLM-based agents’ planning performance. Some studies [72 ,81,82,266]\nd\nynamically generated, maintained, and adjusted high-levelaction plans in orderto minimize dependency\non prior knowledge, thereby grounding the plan. Such feedback can also come from models and humans,\nwhich can usually be referred to as the critics, assessing task completion based on the current state and\ntask prompts [19 ,177].\nE\nmbodied actions for LLM-based agents. There are several fundamental embodied actions or\ntasks for LLM-based agents to master, primarily including observation, manipulation, and navigation.\n•Observation. Observation plays a crucial role in subsequent embodied actions by which the agent\nacquires environmental information and updates states. As mentioned in Subsection 3.2, during obser-\nv\nation stage, various inputs are ultimately converged into a multimodal signal. A common approach\nentails a pre-trained vision transformer (ViT) used as the alignment module for text and visual informa-\ntion [101 ,102,267]. In recent times, more researches take audio as a modality for embe dded observation.\nSoundspaces [268 ] proposes the identification of spatial geometric elements guided by reverberant audio\ninput [265 ]. Apart from the cascading paradigm [218 ,219,269], audio information encoding can also\ne\nnhance the seamless integration of audio with other modalities of inputs [222 ]. Additionally, the agents’\no\nbservation could be from real-time human linguistic instructions, which helps the agent in acquiring\ndetail information that may not be readily obtained or parsed [177 ,270].\n•M\nanipulation. Embodied agents’ manipulation tasks generally include object rearrangements,\ntabletop manipulation, and mobile manipulation [17 ,101]. The typical case entails the agent executing a\ns\nequence of tasks in the kitchen, which includes retrieving items from drawers and handing them to the\nuser [163 ]. This task involves combining a series of subgoals and maintaining synch ronization between\nthe agent’s state and such subgoals are of significance [271 ]. Besides these, AlphaBlock [272 ] focuses on\nm\nore challenging manipulation tasks (e.g., making a smiley face using building blocks), which requires\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:13\nagents to have a more grounded understanding of the instructions. It fine-tunes a multimodal model\nbased on a complex task dataset comprising corresponding multi-step planning and observation pairs to\nenhance its comprehension of high-level cognitive instructions.\n•Navigation. In navigation tasks, agents need to dynamically alter their positions, which often\ninvolves multi-angle and multi-object observations [17 ]. Before navigation, it is essential for embodied\na\ngents to establish prior internal maps, typically in the form of a topological map, semantic map, and\noccupancy map [250 ]. For example, LM-Nav [273 ] utilizes the VNM [274 ] to create the topological map,\na\nnd further leverages the LLM and VLM for analyzing the environment to find the optimal path. Some\nstudies [275 ,276] highlighted the importance of spatial representation to obtain the precise localization\nof targets by leveraging the pre-trained VLM model to combine visual features from images with 3D\nreconstructions of the physical world [250 ]. During navigation tasks, the states of the agent are often\nin\nfluenced by its past actions. A memory mechanism is needed to record historical information [277 ],\nw\nhich is also adopted in Smallville and Voyager [16 ,177,278,279]. Additionally, audio input is also of\ng\nreat significance for navigation tasks. Previous studies demonstrate a basic framework that includes a\ndynamic path planner that uses visual and auditory observations along with spatial memories to plan a\nseries of actions [265 ,280].\nC\nomplex and grounding embodied actions. By integrating actions above, the agent can accom-\nplish more complex tasks, such as embodied question answering, e.g., Is the watermelon in the kitchen\nlarger than the pot? Which one is harder? To address these questions, the agent needs to navigate to the\nkitchen, observe the sizes of both objects, and then answer the questions through comparison [250 ]. In\nt\nerms of control strategies, as previously mentioned, LLM-based agents trained on particular embodied\ndatasets typically generate high-level policy commands to control low-level policies [163 ] like a robotic\nt\nransformer [101 ,281,282], which takes images and instructions as inputs and produces contro l com-\nmands for the end effector and robotic arms as well as virtual embodied controllers due to high costs of\nrobotic operators [16 ,89,90,139,271,283,284]. By utilizing the Mineflayer [285 ] API, some studies enable\nc\nost-effective examination of embodied agents’ operations including exploration, planning, and lifelong\nlearning [177 ]. On the other hand, grounding language to action space poses an ob stacle for agents.\nFor example, understanding the linguistic metaphor expressions like “jump down like a cat” requires\nadequate world knowledge [286 ]. Sumers et al. [287 ] endeavored to amalgamate text distillation with\nh\nindsight experience replay to construct a dataset for training. Nevertheless, additional investigation on\ngrounding embodied action still remains necessary as it plays an increasingly pivotal role across various\ndomains in human life.\n4 Agents in practice: harnessing AI for good\nThe LLM-based agent, as an emerging direction, has gained increasing attention from researchers. Many\napplications in specific domains and tasks have already been developed, showcasing the powerful and\nversatile capabilities of agents [288 ]. As an LLM-based agent, its design objective should always be\nb\neneficial to humans, i.e., humans can harness AI for good.\nIn this section, we provide an overview of current applications of LLM-based agents, aiming to offer a\nbroad perspective for the practical deployment scenarios (see Figure 3). First, we elucidate the diverse\na\npplication scenarios of single agent, including task-oriented, innovation-oriented, and lifecycle-oriented\nscenarios (Subsection 4.1). Then, we present the significant coordinating potential of multiple agents:\ncooperative interaction or adversarial interaction (Subsection 4.2). Finally, we categorize the interactive\nc\nollaboration between humans and agents into two paradigms and introduce the specific applications\n(Subsection 4.3).\n4\n.1 General ability of single agent\nCurrently, there is a vibrant development of application instances of LLM-based agents [289 –291]. Au-\nt\noGPT [95 ] is one of the ongoing popular projects aiming to achieve a fully autono mous system. Apart\nfrom the basic functions of LLMs, the AutoGPT framework also incorporates various external tools and\nmemory management. After users input their customized objectives, they can free their hands and wait\nfor AutoGPT to automatically generate thoughts and perform specific tasks, all without requiring ad-\nditional user prompts. As shown in Figure 4, we introduce the astonishingly capabilities that the agent\ne\nxhibits in scenarios where only one single agent is present.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:14\nFigure 3 (Color online) Scenarios of LLM-based agent applications: single-agent deployment, multi-agent interaction, and human-\nagent interaction. A single agent possesses diverse capabilities and demonstrates outstanding task-solving performance in various\napplication orientations. When multiple agents interact, they can achieve advancement through cooperative or adversarial interac-\ntions. Furthermore, in human-agent interactions, human feedback can enable agents to perform tasks more efficiently and safely,\nwhile agents can also provide better service to humans.\nFigure 4 (Color online) Practical applications of the single LLM-based agent in different scenarios. In task-oriented deployment,\nagents assist human users in solving daily tasks. They need to possess basic instruction comprehension and task decomposition\nabilities. In innovation-oriented deployment, agents demonstrate the potential for autonomous exploration in scientific domains.\nIn lifecycle-oriented deployment, agents have the ability to continuously explore, learn, and utilize new skills to ensure long-term\nsurvival in an open world.\n4.1.1Task-oriented deployment\nTheLLM-basedagents, whichcanunderstandhumannaturallanguageinstructionsandperformeveryday\ntasks [292 ], are currently among the most favored and practically valuable agen ts by users. This is\nbecause they have the potential to enhance task efficiency, alleviate user workload, and promote access\nfor a broader user base. In task-oriented deployment, the agent follows high-level instructions from users,\nundertakingtaskssuch asgoaldecomposition[167 ,171,293,294],sequence planningofsub-goals[167 ,295],\nin\nteractiveexplorationoftheenvironment[165 ,292,296,297],untilthefinalobjectiveisachieved. Basedon\nt\nask types, we divide these deployment environments into web scenarios and life scenarios, and introduce\nthe specific roles that agents play in them.\nIn web scenarios. Performing specific tasks on behalf of users in a web scenario is known as the\nweb navigation problem [296 ]. Agents interpret user instructions, break them down into multiple b asic\noperations, and interact with computers. This often includes web tasks such as filling out forms, online\nshopping, and sending emails. Agents need to possess the ability to understand instructions within\ncomplex web scenarios, adapt to changes (such as noisy text and dynamic HTML web pages), and\ngeneralize successful operations [292 ]. In this way, agents can achieve accessibility and automation when\nd\nealing with unseen tasks in the future [298 ], freeing humans from repeated interactions with computer\nU\nIs.\nTo enable successful interactions between agents and realistic web pages, some researchers [294 ,299]\nh\nave started to leverage the powerful HTML reading and understanding abilities of LLMs. By designing\nprompts, they attempt to make agents understand the entire HTML source code and predict more\nreasonable next action steps. Mind2Web [300 ] combines multiple LLMs fine-tuned for HTML, allowing\nt\nhem to summarize verbose HTML code [293 ] in real-world scenarios and extract valuable information.\nF\nurthermore, WebGum [296 ]empowers agentswith visual perception abilities by employinga multimo dal\ncorpus containing HTML screenshots. Auto-GUI [301 ] also proposes a multimodal solution that directly\nin\nteracts with the interface on mobile devices, thus deepening the comprehensive understanding of web\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:15\npages.\nIn life scenarios. In many daily household tasks, it is essential for agents to understand implicit\ninstructions and apply common-senseknowledge[302 ]. Foran LLM-basedagent trained solely on massive\na\nmounts oftext, tasks that humans take for grantedmight requiremultiple trial-and-errorattempts [303 ].\nM\nore realistic scenarios often lead to more obscure tasks. For example, the agent should proactively turn\nit on if it is dark and there is a light in the room. To successfully chop some vegetables in the kitchen,\nthe agent needs to anticipate the possible location of a knife [167 ].\nC\nan an agent apply the world knowledge embedded in its training data to real interaction scenarios?\nHuang et al. [171 ]led the wayin exploringthis question. They demonstrated that suffic iently largeLLMs,\nwith appropriate prompts, can effectively break down high-level tasks into suitable sub-tasks without\nadditional training. However, this static reasoning and planning ability has its potential drawbacks.\nActions generated by agentsoften lack awarenessofthe dynamic environment aroundthem. For instance,\nwhen a user gives the task “clean the room”, the agent might convert it into unfeasible sub-tasks like “call\na cleaning service” [304 ]. As a result, some approachesdirectly incorporatespatial data an d item-location\nrelationships as additional inputs to the model. This allows agents to gain a precise description of their\nsurroundings and plan next actions more effectively [167 ,295,304].\n4\n.1.2Innovation-oriented deployment\nThe LLM-based agent has demonstrated strong capabilities in performing tasks. However, in a more\nintellectually demanding field, like cutting-edge science, the potential of agents has not been fully realized\nyet. This limitation mainly arises from two challenges [305 ]. On one hand, the inherent complexity\no\nf science poses a significant barrier. Many domain-specific terms and multi-dimensional structures\nare difficult to represent using a single text. As a result, their complete attributes cannot be fully\nencapsulated. On the other hand, there is a severe lack of suitable training data in scientific domains,\nmaking it difficult for agents to comprehend the entire domain knowledge [306 ,307]. If the ability for\na\nutonomous exploration could be discovered within the agent, it would bring about beneficial innovation\nin human technology.\nCurrently, numerous efforts in various specialized domains aim to overcome this challenge [308 –310].\nE\nxperts from the computer field make full use of the agent’s powerful code comprehension and debugging\nabilities [288 ,311]. In the fields of chemistry and materials, researchers equip agents with various general\nor task-specific tools to better understand domain knowledge. Agents evolve into scientific assistants,\nproficient in online research and document analysis to fill data gaps. They also employ robotic APIs for\nreal-world interactions, enabling tasks like material synthesis and mechanism discovery [91 ,245,305].\nT\nhe potential of LLM-based agents in scientific innovation is evident, yet we do not expect their\nexploratory abilities to be utilized in applications that could threaten or harm humans. Boiko et al. [91 ]\ns\ntudy the hidden dangers of agents in synthesizing illegal drugs and chemical weapons, indicating that\nagents could be misled by malicious users in adversarial prompts. This serves as a warning for our future\nwork.\n4.1.3Lifecycle-oriented deployment\nBuilding a universally capable agent that can continuously explore, develop new skills, and maintain a\nlong-termlife cycle in an open, unknownworldis a colossalchallenge. This accomplishmentis regardedas\napivotalmilestonein thefieldofAGI[271 ]. Minecraft, asatypicalandwidelyexploredsimulatedsurvival\ne\nnvironment, has become a unique playground for developing and testing the comprehensive ability of\nan agent. Players typically start by learning the basics, such as mining wood and making crafting tables,\nbefore moving on to more complex tasks like fighting against monsters and crafting diamond tools [177 ].\nM\ninecraft fundamentally reflects the real world, making it conducive for researchers to investigate an\nagent’s potential to survive in the authentic world.\nThe survival algorithms of agents in Minecraft can generally be categorized into two types [177 ]: low-\nle\nvel control and high-level planning. Early efforts mainly focused on reinforcement learning [177 ,312]\na\nnd imitation learning [313 ], enabling agents to craft some low-level items. With the emergence o f LLMs,\nwhich demonstrated surprising reasoning and analytical capabilities, agents begin to utilize LLM as a\nhigh-level planner to guide simulated survival tasks [271 ,314]. Some researchers use LLM to decompose\nh\nigh-level task instructions into a series of sub-goals [315 ], basic skill sequences [314 ], or fundamental\nk\neyboard/mouse operations [315 ], gradually assisting agents in exploring the open world.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:16\nVoyager [177 ], drawing inspiration from AutoGPT [95 ], became the first LLM-based embodied lifelong\nle\narning agent in Minecraft. It introduces a skill library for storing and retrieving complex action-\nexecutable code, along with an iterative prompt mechanism that incorporates environmental feedback\nanderrorcorrection. Thisenablestheagenttoautonomouslyexploreandadapttounknownenvironments\nwithout human intervention. An AI agent capable of autonomously learning and mastering the entire\nreal-world techniques may not be as distant as once thought [315 ].\n4\n.2 Coordinating potential of multiple agents\nMotivation and background. Although LLM-based agents excel in text understanding and genera-\ntion, they inherently function as isolated entities [316 ]. A key limitation of the single-agent application\np\naradigm is their ability to collaborate with other agents and learn from social interactions. This limi-\ntation restricts their capacity to benefit from multi-turn feedback, which could otherwise improve their\nperformance [21 ].\nA\ns early as 1986, Minsky [317 ] made a forward-looking prediction. In his book T he Society of Mind ,\nhe introduced a novel theory of intelligence, suggesting that intelligence emerges from the interactions of\nmany smaller agents with specific functions. For instance, certain agents might be responsible for pattern\nrecognition, while others might handle decision-making or generate solutions. This idea has been put into\nconcrete practice with the rise of distributed artificial intelligence [318 ]. Multi-agent systems (MAS) [3 ],\na\ns one of the primary research domains, focus on how a group of agents can effectively coordinate\nand collaborate to solve problems. Some specialized communication languages, like KQML [319 ], were\nd\nesigned early on to support message transmission and knowledge sharing among agents. In the 21st\ncentury, integratingreinforcementlearningalgorithms(suchasQ-learning)withdeeplearninghasbecome\na prominent technique for developing MAS that operate in complex environments [320 ]. Nowadays, the\nc\nonstruction approach based on LLMs is beginning to demonstrate remarkable potential. The natural\nlanguage communication between agents has become more elegant and easily comprehensible to humans,\nresulting in a significant leap in interaction efficiency.\nPotential advantages. Specifically, an LLM-based multi-agent system can offer several advantages.\nJustasSmith clearlystatedin The Wealth of Nations [321],“Thegreatestimprovementsintheproductive\np\nowers of labor, and most of the skill, dexterity, and judgment with which it is directed or applied,\nseem to be results of the division of labor.” Based on the principle of division of labor, a single agent\nequipped with specialized skills and domain knowledge can engage in specific tasks. On the one hand,\nagents’ skills in handling specific tasks are increasingly refined through the division of labor. On the\nother hand, decomposing complex tasks into multiple subtasks can eliminate the time spent switching\nbetween different processes. In the end, efficient division of labor among multiple agents can accomplish\na significantly greater workload than when there is no specialization, substantially improving the overall\nsystem’s efficiency and output quality.\nIn Subsection 4.1, we have provided a comprehensive introduction to the versatile abilit ies of LLM-\nbased agents. In this subsection, we focus on exploring the ways agents interact with each other in a\nmulti-agent environment. Based on current research, these interactions can be broadly categorized as\nfollows: cooperative interaction and adversarial interaction (see Figure 5).\n4\n.2.1Cooperative interaction for complementarity\nCooperative multi-agent systems are the most widely deployed pattern in practical usage. Within such\nsystems, individual agent assesses the needs and capabilities of other agents and actively seeks collabo-\nrative actions and information sharing with them [89 ]. This approach brings forth numerous potential\nb\nenefits, including enhanced task efficiency, collective decision improvement, and the resolution of com-\nplex real-world problems that one single agent cannot solve independently. We introduce and categorize\nexisting cooperative multi-agent applications into two types: disordered cooperation and ordered coop-\neration.\nDisordered cooperation. When three or more agents are present within a system, each agent is\nfree to express their perspectives and opinions openly. They can provide feedback and suggestions for\nmodifying responses related to the task at hand [322 ]. This entire discussion process is uncontrolled,\nla\ncking any specific sequence. We refer to this kind of multi-agent cooperation as disordered cooperation.\nChatLLM network [323 ] is an exemplary representative of this concept. It emulates the fo rward and\nbackwardpropagationprocess within a neural network, treating each agent as an individual node. Agents\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:17\nFigure 5 (Color online) Interaction scenarios for multiple LLM-based agents. In cooperative interaction, agents collaborate in\neither a disordered or ordered manner to achieve shared objectives. In adversarial interaction, agents compete in a tit-for-tat\nfashion to enhance their respective performance.\nin the subsequent layer need to process inputs from all the preceding agents and propagate forward. One\npotentialsolutionisintroducingadedicatedcoordinatingagent, responsibleforintegratingandorganizing\nresponses from all agents, thus updating the final answer [324 ]. However, consolidating a large amount\no\nf feedback data and extracting valuable insights pose a significant challenge for the coordinating agent.\nFurthermore,majorityvotingcanalsoserveasaneffectiveapproachtomakingappropriatedecisions[325 ].\nO\nrdered cooperation. When agents in the system adhere to specific rules, for instance, expressing\ntheir opinions one by one in a sequential manner, downstream agents only need to focus on the outputs\nfrom upstream. This leads to a significant improvement in task completion efficiency. The entire discus-\nsion process is highly organized and ordered. We term this kind of multi-agent cooperation as ordered\ncooperation. It isworthnoting that systems with only twoagents, essentiallyengagingin a conversational\nmanner, also fall under the category of ordered cooperation.\nCAMEL [89 ] stands as a successful implementation of a dual-agent cooperativ e system. Within a role-\nplaying communication framework, agents take on the roles of users (giving instructions) and assistants\n(providing specific solutions). Through multi-turn dialogues, these agents autonomously collaborate to\nfulfill user instructions [326 ]. On the other hand, Talebirad et al. [316 ] were among the first to systemat-\nic\nally introduce a comprehensive LLM-based multi-agent collaboration framework. This paradigm aims\nto harness the strengths of each individual agent and foster cooperative relationships among them. Many\napplications of multi-agent cooperation have successfully been built upon this foundation [21 ,327–330].\nT\no promote more efficient collaboration, researchers hope that agents can learn from successful human\ncooperation examples [90 ]. Wang et al. [331 ] advocated for the integration of evolutionary game theory\na\nnd artificial intelligence to advance the mathematics of multi-agent learning systems. MetaGPT [332 ]\nd\nraws inspiration from the classic waterfall model in software development. By encoding advanced hu-\nman management experience into agent prompts, collaboration among multiple agents becomes more\nstructured.\nHowever, during MetaGPT’s practical exploration, a potential threat to multi-agent cooperation has\nbeen identified. Without setting corresponding rules, frequent interactions among multiple agents can\namplify minor hallucinations indefinitely [332 ]. For example, in software development, issues like in-\nc\nomplete functions, missing dependencies, and bugs that are imperceptible to the human eye may arise.\nIntroducing techniques like cross-validation [90 ] or timely external feedback could have a positive impact\no\nn the quality of agent outputs.\n4.2.2Adversarial interaction for advancement\nTraditionally, cooperative methods have been extensively explored in multi-agent systems. However,\nresearchers increasingly recognize that introducing concepts from game theory [333 ,334] into systems\nc\nan lead to more robust and efficient behaviors. In competitive environments, agents can swiftly adjust\nstrategies through dynamic interactions, striving to select the most advantageous or rational actions in\nresponse to changes caused by other agents [50 ,335]. AlphaGo Zero [336 ], for instance, is an agent for Go\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:18\nFigure 6 (Color online) Two paradigms of human-agent interaction. In the instructor-executor paradigm (a), humans provide\ninstructions or feedback, while agents act as executors. In the equal partnership paradigm (b), agents are human-like, able to\nengage in empathetic conversation and participate in collaborative tasks with humans.\nthat achievedsignificantbreakthroughsthroughaprocessofself-play. Similarly, within LLM-basedmulti-\nagent systems, fostering change among agents can naturally occur through competition, argumentation,\nand debate [337 ,338]. By abandoning rigid beliefs and engaging in thoughtful reflection, ad versarial\ninteraction enhances the quality of responses.\nResearchers first delve into the fundamental debating abilities of LLM-based agents [114 ,339,340].\nF\nindings demonstrate that when multiple agents express their arguments in the state of “tit for tat”,\none agent can receive substantial external feedback from other agents, thereby correcting its distorted\nthoughts [93 ]. Consequently, multi-agent adversarial systems are suitable in sce narios requiring high-\nquality responses and accurate decision-making. For example, Du et al. [92 ] introduced the concept of\nd\nebate, endowing agents with responses from fellow peers. When these responses diverge from an agent’s\nown judgments, a “mental” argumentation occurs, leading to refined solutions.\nThe performance of the multi-agent adversarial system has shown considerable promise. However, the\nsystem is essentially dependent on the strength of LLMs and faces several basic challenges: (1) With a\nprolonged debate, LLM’s limited context cannot process the entire input. (2) In a multi-agent environ-\nment, computational overhead significantly increases. (3) Multi-agent negotiation may converge to an\nincorrect consensus, and all agents are firmly convinced of its accuracy [92 ]. The development of MAS is\ns\ntill far from being mature and feasible. Introducing human guides when appropriate to compensate for\nagents’ shortcomings is a good choice to promote the further advancements of agents.\n4.3 Interactive engagement between human and agent\nHuman-agent interaction, as the name suggests, involves agents collaborating with humans to accomplish\ntasks. Throughout the interaction, humans play a pivotal role by offering guidance or by regulating the\nsafety, legality, and ethical conduct ofagents[341 ,342]. This is particularlycrucial in specialized domains,\ns\nuch as medicine where data privacy concerns exist [343 ]. In such cases, human involvement can serve\na\ns a valuable means to compensate for the lack of data, thereby facilitating smoother and more secure\ncollaborative processes. The interaction between humans and agents can be classified into two paradigms\n(see Figure 6). (1) Unequal interaction (i.e., instructor-executor paradigm), h umans serve as issuers\nof instructions, while agents act as executors, participating as assistants to humans in collaboration.\n(2) Equal interaction (i.e., equal partnership paradigm): agents reach the level of humans, participating\non an equal footing with humans in interaction.\n4.3.1Instructor-executor paradigm\nThe simplest approach involves human guidance throughout the process: humans provide specific in-\nstructions directly, while the agents’ role is to understand natural language commands from humans and\ntranslate them into corresponding actions [344 –346]. In Subsection 4.1, we have presented the scenario\nw\nhere agents solve single-step problems or receive high-level instructions from humans. In addition, some\nstudies have shown that breaking down instructions into step-by-step formats can improve the cross-task\ngeneralization capabilities of language models [347 ]. Considering the interactive nature of language, in\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:19\nthis section, we assume that the dialogue between humans and agents is also interactive: the agent re-\nsponds to each human instruction, refining its action through alternating iterations to ultimately meet\nhuman requirements [177 ,348]. While this approach does achieve the goal of human-agent interact ion, it\nrequires a substantial amount of human effort and, in certain tasks, might even necessitate a high level of\nexpertise. To alleviate this issue, the agent can be empowered to autonomously accomplish tasks, while\nhumans only need to provide feedback in certain circumstances. Here, we roughly categorize feedback\ninto two types: quantitative feedback and qualitative feedback.\nQuantitative feedback. The forms of quantitative feedback mainly include absolute evaluations like\nbinary scores and ratings, as well as relative scores. Binary feedback refers to the positive and nega-\ntive evaluations provided by humans, which agents utilize to enhance their self-optimization [349 –353].\nC\nomprising only two categories, this type of user feedback is often easy to collect, but sometimes it may\noversimplify user intent by neglecting potential intermediate scenarios. To showcase these intermediate\nscenarios, researchers attempt to expand from binary feedback to rating feedback, which involves cat-\negorizing into more fine-grained levels. However, the results of Kreutzer et al. [354 ] suggest that such\nm\nulti-level artificial ratings method might be inefficient or less reliable. Furthermore, agents can learn\nhuman preference from comparative scores like multiple choice [355 ,356].\nQ\nualitative feedback. Text feedback is usually offered in natural language: humans provide advice\non how to modify outputs generated by agents, and the agents then incorporate these suggestions to\nrefine their subsequent outputs [357 ,358]. For agents without multimodal perception capabilities, hu-\nm\nans can act as critics, offering visual critiques [177 ]. Additionally, agents can utilize a memory module\nt\no store feedback for future reuse [359 ]. In Scheurer et al. [360 ], humans give feedback on the initial\no\nutput generated by agents, prompting the agents to formulate various improvement proposals. The\nagents then discern and adopt the most suitable proposal, harmonizing with the human feedback. Com-\npared to quantitative feedback, this approach can better convey human intention, but it might be more\nchallenging for the agents to comprehend. Xu et al. [361 ] compared various types of feedback and ob-\ns\nerved that combining multiple types of feedback can yield better results. Of course, the collaborative\nnature of human-agent interaction also allows humans to directly improve the agents’ output by mod-\nifying intermediate links [176 ,362] or adjusting the conversation content [363 ]. In some studies, agents\nc\nan autonomously judge whether the conversation is proceeding smoothly and seeking feedback when\nencountering errors [364 ,365]. Humans can also choose to participate in feedback at any time, guidin g\nthe agent’s learning in the right direction [366 ].\nC\nurrently, LLM-based agents serving as human assistants hold tremendous potential across various\ndomains. In education, for instance, the robot Dona [367 ] supports multimodal interactions to assist stu-\nd\nents with registration, and Gvirsman et al. [368 ] contributed to early childhood education by achieving\nm\nultifaceted interactions. Agents can also aid in human understanding and utilization of mathemat-\nics [369]. In the field of medicine, some medical agents have been proposed, s howing enormous potential\nin terms of diagnosis assistance, consultations, and more [370 ,371]. Especially in mental health, re-\ns\nearch has shown that agents can lead to increased accessibility due to benefits such as reduced cost\nand anonymity compared to face-to-face treatment [372 ]. Leveraging such advantages, agents have found\nw\nidespread applications. Ali et al. [373 ] designed LISSA for online communication with adolescents on\nt\nhe autism spectrum, analyzing their speech and facial expressions in real-time. Hsu et al. [374 ] built\nc\nontextualized language generation approaches to provide tailored assistance for users who seek support\non diverse topics ranging from relationship stress to anxiety. Besides, in other industries like business,\na good agent can provide automated services, thereby effectively reducing labor costs [375 ]. Amidst the\np\nursuit of AGI, efforts are directed towards creating agents that can function as universal assistants in\nreal-life scenarios [376 ].\n4\n.3.2Equal partnership paradigm\nEmpathetic communicator. With the rapid development of AI, conversational agents have garnered\nextensive attention in various forms [377 ] and in various scenarios [378 –380]. Although it is intuitive that\na\ngents themselves do not possess emotions, can we enable them to exhibit emotions and thereby bridge\nthe gap between agents and humans? Therefore, a plethora of research endeavors have embarked on\ndelving into the empathetic capacities of agents. This endeavor seeks to infuse a human touch into these\nagents, enabling them to detect sentiments and emotions from human expressions, ultimately crafting\nemotionally resonant dialogues [381 –385]. Apart from generating emotionally charged language, agents\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:20\ncan dynamicallyadjust their emotionalstates and displaythem throughfacial expressionsand voice[386 ].\nT\nhese studies, viewing agents as empathetic communicators, not only enhance user satisfaction but also\nmake significant progress in fields like healthcare [373 ,374,387] and business marketing [388 ]. Unlike\ns\nimple rule-based conversation agents, agents with empathetic capacities can tailor their interactions to\nmeet users’ emotional needs [389 ].\nH\numan-level participant. Furthermore, we hope that agents can be involved in the normal lives\nof humans, cooperating with humans to complete tasks from a human-level perspective. In many real-\nworld applications, adversarial games play a pivotal role in facilitating highly efficient and effective\ndecision-making processes [390 ]. While agents have already excelled in pure competitive environments\nlik\ne chess [335 ], Go [50 ], and poker [391 ], LLM-based agents go further by devising unified cooperative\ns\ntrategies through effective negotiation and collaboration in more complex scenarios [392 –395]. Beyond\ng\names, LLM-based agents demonstrate human-level capabilities in strategy formulation, negotiation, and\nother interaction-based tasks. They can collaborate with humans, determining shared knowledge, iden-\ntifying relevant information, posing questions, and reasoning to complete tasks like allocation, planning,\nand scheduling [396 ]. Furthermore, LLM-based agents possess persuasive abilities [397 ], dynamically\nin\nfluencing human viewpoints in various interactive scenarios [398 ].\nI\nn summary, the goal of the field of human-agent interaction is to learn and understand humans, and\nultimately enable efficient and secure interactions between humans and agents. Currently, significant\nbreakthroughs have been achieved in terms of usability in this field. In the future, LLM-based agents are\nexpected to provide better assistanceto humans in accomplishingmore complex tasks in variousdomains.\n5 Agent society: from individuality to sociality\nOver an extended duration, researchers and practitioners have envisioned an interactive artificial society\nwherein human behavior can be performed through trustworthy agents [399 ]. From sandbox games such\na\ns The Sims to the concept of Metaverse, we can see how “simulated society” is defined in people’s minds:\nthe environment and the individuals interacting in it. Behind each individual can be a piece of program,\na real human, or an LLM-based agent as described in the previous sections [16 ,400,401]. Then, the\nin\nteraction between individuals also contributes to the emergence of sociality.\nIn this section, we first introduce the behaviors and personalities of LLM-based agents, tracing their\ndevelopment from individuality to sociality (Subsection 5.1). Subsequently, we introduce a general cate-\ng\norization of the diverse environments for agents to perform their behaviors and engage in interactions\n(Subsection 5.2). Finally, we discuss how the agent society works, what insights peop le can get from it,\nand the risks we need to be aware of (Subsection 5.3).\n5\n.1 Behavior and personality of LLM-based agents\nAs noted by sociologists, individuals can be analyzed in terms of both external and internal dimen-\nsions [402 ]. The external deals with observable behaviors, while the internal re lates to dispositions,\nvalues, and feelings. As shown in Figure 7, this framework offers a perspective on emergent behavior\na\nnd personality in LLM-based agents. Externally, we can observe the sociological behaviors of agents\n(Subsection 5.1.1), including how agents act individually and interact with their environme nt. Internally,\nagents may exhibit intricate aspects of the personality (Subsection 5.1.2), such as cognition, emotion,\na\nnd character, that shape their behavioral responses.\n5.1.1Social behavior\nAs Troitzsch et al. [403 ] stated, the agent society represents a complex system comprisin g individual and\ngroup social activities. Recently, LLM-based agents have exhibited spontaneous social behaviors in an\nenvironment [404 ].\nF\noundational individual behaviors. Individual behaviors arise through internal cognitive pro-\ncesses and external environmental factors. These behaviors form the basis of how agents operate and\ndevelop as individuals within society. They can be classified into three dimensions: (1) Input behav-\niors refer to the absorption of information from the surroundings. This includes perceiving sensory\nstimuli [101 ] and storing them as memories [138 ]. (2) Internalizing behaviors involve inward cognitive\np\nrocessing. This category encompasses activities such as planning [109 ], reasoning [76 ], reflection [72 ],\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:21\nFigure 7 (Color online) Overview of simulated agent society. The whole framework is divided into two parts: the agent and\nthe environment. We can observe in this figure the following. (1) (Left) At the individual level, an agent exhibits internalizing\nbehaviors like planning, reasoning, and reflection. It also displays intrinsic personality traits involving cognition, emotion, and\ncharacter. (2) (Mid) An agent and other agents can form groups and exhibit group behaviors, such as cooperation. (3) (Right)\nThe environment, whether virtual or physical, contains human actors and all available resources. For a single agent, other agents\nare also part of the environment. (4) The agents have the ability to interact with the environment via perception and action.\nand knowledge precipitation [89 ,332]. These introspective processes are essential for maturity and se lf-\nimprovement. (3) Output behaviors include outward actions ranging from object manipulation [101 ] to\ns\ntructure construction [177 ]. Moreover, agents express opinions and broadcast information to interact\nwith others [332 ,405].\nD\nynamic group behaviors. A group is a gatheringof two ormore individuals within a defined social\ncontext [406 ]. The attributes of a group evolve due to member interactions and en vironmental influences.\nThis flexibility gives rise to group behaviors, each with an impact on the larger group. The categories\nof group behaviors include the following. (1) Positive group behaviors foster collective well-being [16 ,\n90,322,327,328,340]. An example is cooperation, which is achieved through volunteer beha viors [330 ],\nb\nrainstorming discussions [327 ,340], and project management [332 ]. (2) Neutral group behaviors are\no\nbserved in LLM-based agents. LLMs are designed to be “helpful, honest, and harmless” [407 ] and\nt\nhis alignment with neutral values [408 ] leads to conformity behaviors including mimicry and spectating.\n(\n3) Negative group behaviors undermine the effectiveness of a group. Recent studies have revealed\nthat agents may exhibit confrontational actions [404 ] and even resort to destructive behaviors, such as\nd\nestroying other agents or the environment in pursuit of certain gains [330 ].\n5\n.1.2Personality\nRecent advances show that LLMs and LLM-based agents exhibit a form of personality through inter-\nactions with the group and the environment [409 –411]. The widely accepted definition of personality\nin\ncludes cognitive, emotional, and character traits [412 ].\nC\nognitive abilities. Cognitive abilities refer to the mental processes, including thinking, judging,\nand problem-solving. Recent studies have applied cognitive psychology methods to investigate emerging\npersonalities of LLM-based agents [413 –415]. They conducted psychology experiments about judgment\na\nnd decision-making to test agent systems [413 ,414,416,417]. Specifically, LLMs have been evaluated\nu\nsing the cognitive reflection test (CRT) to underscore their capacity for deliberate thinking beyond\nmere intuition [418 ,419]. These findings indicate that LLM-based agents display human-like co gnitive\nintelligence in specific areas.\nEmotional intelligence. Emotions involve subjective feelings and mood states. With the increasing\npotency of LLMs, LLM-based agents are now demonstrating a nuanced understanding of emotions [24 ].\nR\necent research has explored the emotional intelligence (EI) of LLMs, including emotion recognition,\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:22\ninterpretation, and understanding. LLMs can accurately identify user emotions, exhibit empathy [420 ,\n421], and regulatetheir emotional responses[386 ,422,423]. Theseadvances contribute to the development\no\nf empathetic artificial intelligence (EAI), a crucial facet of achieving AGI. Bates et al. [424 ] explored the\nr\nole of emotion modeling in creating more believable agents. By developing socio-emotional skills and\nintegrating them into agent architectures, LLM-based agents may be able to engage in more naturalistic\ninteractions.\nCharacter portrayal. While cognition involves mental abilities and emotion relates to subjective\nexperiences, a narrower concept of personality typically pertains to distinctive character patterns. Re-\nsearchers analyze character traits in LLMs using frameworks like the big five personality trait mea-\nsure [425 ,426] and the Myers-Briggs type indicator (MBTI) [425 –427]. In addition, investigations of\np\notentially harmful dark personality traits underscore the complexity and multifaceted nature of char-\nacter portrayal in these agents [428 ]. The exploration of customizable character portrayal in agents ha s\nalso been studied [429 ], allowing users to shape agents that align with desired profiles. Techn iques like\nprompt engineering [16 ,430] and personality-enriched datasets [431 ,432] are used to optimize LLMs and\nt\nrain them to exhibit specific personality traits.\n5.2 Environment for agent society\nIn simulation, the society consists of not only solitary agents but also the environment where agents\ninhabit, sense, and act [433 ]. The environment affects sensory inputs and actions of agents, wh ile agents\ninfluence the state of the environment. As shown in Figure 7, for a single agent, the environment refers\nt\no other autonomous agents, humans, and external factors. It provides the resources and stimuli for\nagents. This section examines fundamental characteristics, advantages, and limitations of various envi-\nronmental paradigms, including text-based environment (Subsection 5.2.1), virtual sandbox environment\n(\nSubsection 5.2.2), and physical environment (Subsection 5.2.3).\n5\n.2.1Text-based environment\nSince LLMs mainly rely on language as their input and output format, the text-based environment\nserves as the most natural platform for LLM-based agents to operate in. Text-based environments\ncan be presented in natural or structured formats. Natural texts use descriptive language to convey\ninformation [434 ], while structured text follows standardized formats like technical d ocumentation and\nhypertext [292 ,293,297,300].\nT\nhe flexibility of the text-based environment allows for various applications, such as interactive dia-\nlog [89] and text-based games. In text-based games, agents utilize text commands to execute manipula-\ntions like moving or tool use [303 ,434–436] and express emotions through texts [437 ].\n5\n.2.2Virtual sandbox environment\nThe virtual sandbox environment provides a visualized and extensible platform for agent society, bridg-\ning the gap between simulation and reality. The key features of sandbox environments are the following.\n(1) Visualization. The virtual sandbox displays a panoramic view of the simulated setting, ranging from\n2D graphical interfaces to immersive 3D modeling. Multiple elements collectively transform abstract\nsimulations into visible landscapes. It allows for tracking agent movement, interactions, and symbolic\nrepresentation of actions. (2) Extensibility. The environment is highly extensible, facilitating the con-\nstruction and deployment of diverse scenarios. At a basic level, agents can manipulate the physical\nelements. For instance, platforms like AgentSims [140 ] and generative agents [16 ] construct artificial\nt\nowns with buildings and residents in grid-based worlds. Another example is Minecraft, which provides\na blocky and three-dimensional world with open-ended construction [177 ,283,315]. Beyond physical ele-\nm\nents, agent relationships, rules, and social norms can be defined [21 ]. The extensibility enables iterative\np\nrototyping of diverse agent societies.\n5.2.3Physical environment\nAs previouslydiscussed, the text-based environmenthas limited expressiveness, while the virtual sandbox\nlacks authentic embodied experiences. In contrast, the physical environment refers to the tangible objects\nand spaces, posing additional challenges for LLM-based agents. These challenges can be summarized as\nfollows: (1) Sensory perception and processing. The physical environment provides rich sensory inputs,\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:23\nincluding visual [101 ,270], auditory [265 ,268], and spatial senses. While this enhances sensory immersion,\nit\nalso introduces complexity. Agents need to process these sensory inputs effectively to interact with\ntheir surroundings. (2) Motion control. Unlike virtual environments, physical spaces impose realistic\nconstraints on actions through embodiment. It requires executable and grounded motion control [171 ].\nF\nor example, in a factory, an agent operating a robotic arm needs precision tuning and controlled force\nto grasp objects. Moreover, the agent must navigate the physical workspace and avoid obstacles. To ad-\ndress these challenges, agents require hardware-specific and scenario-specific training to develop adaptive\nabilities that can transfer from virtual to physical environments. More discussion will be presented in\nSubsection 6.6.\n5\n.3 Society simulation with LLM-based agents\nRecent research on simulated societies has followed two primary lines, namely, exploring the boundaries\nof the collective intelligence capabilities of LLM-based agents [90 ,115,327,330,332] and using them to\na\ncceleratediscoveriesinthesocialsciences[16 ,438,439]. Inaddition, therearealsoanumberofnoteworthy\ns\ntudies, e.g., usingsimulatedsocietiestocollectsyntheticdatasets[89 ,440,441],helpingpeopletosimulate\nr\nare yet difficult interpersonal situations [442 ,443]. With the foundation of Subsections 5.1and5.2, here\nw\ne will introduce the key properties and mechanism of agent society (Subsection 5.3.1), what we can\nle\narn from emergent social phenomena (Subsection 5.3.2), and finally the potential ethical and social\nr\nisks in it (Subsection 5.3.3).\n5\n.3.1Key properties and mechanism of agent society\nSocial simulation can be categorized into macro-level simulation and micro-level simulation [438 ]. In\nt\nhe macro-level simulation, also known as system-based simulation, researchers model the overall state\nof the system of the simulated society [444 ,445]. While micro-level simulation, also known as agent-\nb\nased simulation or MAS, indirectly simulates society by modeling individuals [446 ,447]. With the\nd\nevelopment of LLM-based agents, micro-level simulation has gained prominence recently [16 ,140]. In\nt\nhis article, wecharacterizethat the “AgentSociety”refersto anopen, persistent, situated, andorganized\nframework [399 ] where LLM-based agents interact with each other in a defined envir onment. In the\nfollowing paragraphs, we analyze how the simulated society operates through discussing these properties.\n(1) Open. One of the defining features of simulated societies lies in their openness, both in terms of\nindividuals andenvironmentalcomponents. Agentsand humans, the primaryactorswithin suchsocieties,\nhave the flexibility to enter or leave the environment without disrupting its operational integrity [448 ].\nB\nesides, the environment can be expanded by adding or removing entities and resources in the simulated\nworld, adding another level of complexity to the simulation, (2) Persistent. We expect persistence and\nsustainability from the simulated society. While individual agents within the society act in discrete\ntime steps [16 ,438], the society as a whole is somewhat detached from the transient beh avior and persists\nthrough time. This persistence creates an environmentwhere agents’ decisionsand behaviorsaccumulate,\nleading to a coherent societal trajectory. (3) Situated. The situated nature of the society emphasizes its\nexistenceandoperationwithin adistinct environment, whichis artificiallyorautomaticallyconstructedin\nadvance. Notably, the agents possess an awareness of their spatial context, understanding their location\nwithin the environment and the objects within their field of view [16 ,177]. This awareness contributes\nt\no their ability to interact proactively and contextually. (4) Organized. The simulated society operates\nwithin a meticulously organized framework and predefined rules. In the simulated world, agents interact\nwith the environment in a limited action space, while objects in the environment transform in a limited\nstate space. This organizational framework ensures that operations are coherent and comprehensible,\nleading to an ever-evolving yet enduring simulation.\n5.3.2Insights from agent society\nFollowing the exploration of how simulated society works, this section delves into the emergent social\nphenomena in agent society. In the realm of social science, the pursuit of generalized representations\nof individuals, groups, and their intricate dynamics has long been a shared objective [449 ,450]. The\ne\nmergenceofLLM-basedagentsenablesamoremicroscopicviewofsimulatedsocietyandnoveldiscoveries\nfrom the new representation.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:24\nOrganized productive cooperation. Society simulation offers valuable insights into innovative col-\nlaboration patterns that can have the potential to enhance real-world management strategies. Research\nhas shown that within this simulated society, experts with different backgrounds and abilities contribute\nto creative solutions to complex problems (e.g., software development or consulting) [89 ,90,324,330].\nF\nurthermore, through iterations of interactions and debates between agents, individual errors such as\nhallucinations or degeneration of thought (DoT) are corrected by the group, ultimately improving perfor-\nmance on the task [92 ,93]. Another observable phenomenon is that efficient communication also plays a\npivotal role in such a large cooperative group. For example, MetaGPT [332 ] has artificially has artificially\nf\normulated efficient communication styles with reference to standardized operating procedures (SOPs).\nParket al. [16 ]observedagentsworkingtogether to organizea partythrough s pontaneouscommunication\nin a simulated town.\nPropagation in social networks. Simulating social systems can also serve as a reference for pre-\ndicting social processes. Unlike traditional empirical approaches that rely on time-series data and holistic\nmodeling [451 ,452], agent-based simulation provides researchers with a more interpre table and endoge-\nnous perspective. Here we focus on its application in modeling propagation in social networks, such\nas the propagation of interpersonal relationships. Agents who are initially unconnected as friends have\nthe potential to establish connections through intermediaries [16 ]. Once the network of relationships is\ne\nstablished, we can observe the spread of information along with the underlying attitudes and emotions.\nS3[438] proposes a user-demographic inference module for capturing the number of people who are aware\nof a particular piece of information, as well as their collective sentiment. This same approach extends to\nmodeling cultural transmission [453 ] and the spread of infectious diseases [454 ]. With LLM-based agents,\nr\nesearchers can easily implement various intervention strategies as well as monitor population changes\nover time to understand the rationale behind various social phenomena of propagation.\nEthical decision-making and game theory. Simulated societies offer a dynamic platform for the\ninvestigation of intricate decision-making processes, including those influenced by ethical and moral prin-\nciples. Taking werewolf game [404 ,455] and murder mystery games [456 ] as examples, researchers explore\nt\nhe capabilities of LLM-based agents when confronted with challenges of deceit, trust, and incomplete\ninformation. These complex decision-making scenarios also intersect with game theory [457 ], where we\nf\nrequently encounter moral dilemmas pertaining to individual and collective interests. Through the mod-\neling of diverse scenarios, we can understand how agents prioritize values such as honesty, cooperation,\nand fairness in their actions. Furthermore, it not only provides an understanding of existing moral values,\nbut also helps to understand how these values evolve and develop over time. Ultimately, these insights\ncontribute to the refining of LLM-based agents to align with human values and ethical standards [21 ].\nP\nolicy formulation and improvement. One of the most promising and grounded research di-\nrections in modeling societies is to explore various economic and political states and their impact on\nsocial dynamics [458 ]. Researchers can simulate a wide array of social systems by configu ring agents\nwith different economic preferences or political ideologies. This analysis can provide valuable insights\nfor policymakers seeking to foster prosperity and promote societal well-being. In addition, as concerns\nabout environmental sustainability grow, we can also simulate scenarios involving resource extraction,\npollution, conservation efforts, and policy interventions [459 ]. These experiments will assist in making\nin\nformed decisions, foreseeing potential impacts, and formulating policies that aim to maximize positive\noutcomes while minimizing unintended negative effects.\n5.3.3Ethical and social risks in agent society\nSimulated societies powered by LLM-based agents offer significant inspirations, ranging from industrial\nengineering to scientific research. However, these simulations also bring about a myriad of ethical and\nsocial risks that demand careful consideration and mitigation [460 ].\nU\nnexpected social harm. Simulatedsocietiescarrytheriskofgeneratingunexpectedsocialphenom-\nena that may cause considerable public outcry and social harm. These phenomena span from individual-\nlevel issues like discrimination, isolation, and bullying, to broader concerns such as oppressive slavery\nand group antagonism [461 ,462]. Malicious people may manipulate these simulations for unethical social\ne\nxperiments, with consequences reaching beyond the virtual world into reality. Creating these simulated\nsocieties is akin to opening Pandora’s box, necessitating the establishment of rigorous ethical guidelines\nand oversight during their development and utilization [460 ].\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:25\nStereotypes and prejudice. Stereotypes and biases are long-standing challenges in language mod-\neling. A large part of the reason lies in the training corpora obtained from the Internet [463 ,464], which\nr\neflect, and sometimes even amplify real-world social biases such as gender, religion, and culture [465 ].\nA\nlthough LLMs havebeen aligned with human values to mitigate biased outputs, the models still struggle\nto portray minority groups well due to the long-tail effect of the training data [466 –468]. This may result\nin\nan overly one-sided focus in social science research concerning LLM-based agents, as the simulated\nbehaviors of marginalized populations usually conform to prevailing assumptions [469 ]. Researchers have\ns\ntarted addressing this concern by diversifying training data and making adjustments to LLMs [470 ,471],\nb\nut there is still much work to be done.\nPrivacy and security. Given that humans can be members of the agent society, the exchange of\nprivate information between users and LLM-based agents poses significant privacy and security con-\ncerns [472 ]. Users might inadvertently disclose sensitive personal information d uring their interactions,\nwhich will be retained in the agent’s memory for extended periods [137 ]. Such situations could lead\nt\no unauthorized surveillance, data breaches, and misuse of personal information, especially when sub-\njected to malicious attacks [473 ]. To address these risks effectively, it is essential to implement string ent\ndata protection measures, such as differential privacy protocols, regular data purges, and user consent\nmechanisms [474 ,475].\nO\nver-reliance and addictiveness. Another concern in simulated societies is the possibility of users\ndeveloping excessiveemotional attachments or even addictiveness to the agents. Despite being awarethat\nthese agents are computational entities, users may anthropomorphize them and attach human emotions\nto them [16 ,476]. A notable example is “Sydney”, an LLM-powered chatbot developed by Microsoft\nas part of its Bing search engine. Yet, some users reported unexpected emotional connections with\n“Sydney” [477 ], and some even expressed their dismay when Microsoft cut back its p ersonality, resulting\nin a petition called “FreeSydney”2). Therefore, in order to mitigate the risks mentioned here, it is crucial\nto emphasize that agents should not be considered substitutes for genuine human connections.\n6 Discussion\n6.1 Mutual benefits between LLM research and agent research\nWith the recent advancement of LLMs, research at the intersection of LLMs and agents has rapidly pro-\ngressed, fueling the development of both fields. In this section, we discuss the benefits and opportunities\nthat LLM research and agent research provide to one another.\nLLM research →agent research. As previously mentioned, AI agents must possess the ability to\nperceive their environment, make decisions, and carry out actions effectively [3 ,31]. This involves com-\np\nrehending input, reasoning, planning, and formulating executable action sequences, all of which LLMs\nare well-prepared to handle. Coupled with the knowledge and memory they acquired, LLMs can create\ncoherent action sequences that can be executed effectively [171 ,246,271]. Additionally, through mecha-\nn\nisms like reflection [138 ,150], they can continuously adjust decisions and optimize execution sequ ences\nbased on the feedback provided by the current environment. This offers a more robust and interpretable\ncontroller. With just task descriptions or demonstrations, they can effectively handle previously unseen\ntasks [18 ,87,478]. Additionally, LLMs can adapt to various languages, cultures, and do mains, making\nthem versatile and reducing the need for complex training processes and data collection for building\nagents [24 ,224].\nI\nn essence, LLM provides a powerful foundation for agent research, opening up numerous new oppor-\ntunities for integration into agent-related studies. This includes enhancing decision-making in traditional\nagent frameworks, and potentially transforming domains previously dominated by human experts, like le-\ngalconsultationandmedical assistance[326 ,330]. LLMs’planning andreflectiveabilitiescan leadtomore\no\nptimal action sequences, expanding agent research beyond simple simulations into complex real-world\nsettings, such as robotic arm path planning or the interaction of an embodied intelligent machine with\nthe tangible world. Additionally, the training paradigm for agents becomes more streamlined, allowing\ndirect adaptation to new tasks via demonstrating trajectories.\nAgent research →LLM research. Elevating LLMs to agents presents new challenges and opportu-\nnities, expanding their application scopes. The study of LLMs is no longer confined to traditional tasks\n2)https://www.change.org/p/save-sydney-ai .\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:26\ninvolving textual inputs and outputs, such as text classification, question answering, and text summariza-\ntion. Instead, the focus has shifted towards tackling complex tasks incorporating richer input modalities\nand broader action spaces, all while aiming for loftier objectives exemplified by PaLM-E [101 ].\nS\nuch a shift provides the motivation for further advancement of LLMs, such as stronger cognitive\nabilitiesandgeneralizationcapabilities. Inaddition,giventhescaleofLLM-basedagentsandthepotential\ncomputational costs, the efficiency of LLMs becomes a crucial area of research. Moreover, the capabilities\nof LLM-based agents must be constrained to a safe scope of application to prevent unintended harm to\nother elements in the environment, placing higher demands on LLMs serving as the cognitive core [21 ,\n479,480].\nF\nurthermore, the realm of multi-agent systems constitutes a significant branch of research within the\nfield of agents [16 ,89,316,330], offering valuable insights into how to better design and construct LL Ms.\nWe aspire for LLM-based agents to play diverse roles in the division of labor within society, participating\nin social interactions involving cooperation, competition, and coordination [90 ,93,114,327,332]. Investi-\ng\nating methods to stimulate and maintain their role-playing abilities and enhance collaborative efficiency\nis a research area that deserves attention.\n6.2 Practical tools for developing\nNowadays, a variety of agent developing tools offer essential infrastructure [289 ,481–483], allowing re-\ns\nearchersanddeveloperstoconcentrateonthestrategiesofagentswithout theburdenofbuildingcomplex\nenvironments and interaction mechanisms from scratch. In this section, we discuss several common de-\nveloping tools for single-agent and multi-agent systems.\nTools to develop single-agent systems. Currently, tools to develop single-agent systems primarily\nfocus on enhancing the decision-making and learning capabilities of individual agents across diverse\nenvironments. For instance, XAgent [481 ] provides a general LLM-based agent that can automatically\ns\nolve various tasks, consisting of three parts: dispatcher, planner, and actor. Utilizing a docker container\ncalled ToolServer, XAgent operates within a secure environment and leverages powerful tools for task\nresolution. Similarly, LangChain [289 ] is a framework that enables developers to create LLM-powered\na\npplications by implementing a series of modular components that can be combined in specific ways\nthrough chains. These tools directly call LLM APIs rather than training agents from scratch. In contrast,\nXi et al. [482 ] introduced AgentGym, a general agent interaction platform that supports the training of\nLLM-based agents. This platform, built on HTTP services, provides a unified API interface for different\nenvironments, supporting trajectory sampling, multi-turn interactions, online evaluation, and real-time\nfeedback.\nTools to develop multi-agent systems. On the other hand, tools to develop multi-agent sys-\ntems emphasize the coordination and cooperation among different agents. One notable tool in this area\nis AgentVerse [330 ], a multi-agent framework that simulates the problem-solving proces ses of human\ngroups and dynamically adjusts team members to effectively orchestrate collaborative groups of expert\nagents. It has demonstrated superior performance across various tasks and showcased complex interac-\ntions among agents within the Minecraft environment. Additionally, AutoGen [327 ] is a highly flexible\nm\nulti-agent development tool that allows users to create customizable agents. By integrating with hu-\nmans and tools, it facilitates automated communication and collaboration among multiple agents. In\nspecific scenarios, ChatDev [90 ], a full-process automated software development framework, rea lizes a\nvirtual software company operated by multi-agent collaboration. And Chan et al. [340 ] introduced a\nm\nulti-agent debate framework named ChatEval to evaluate the quality of generated text, allowing re-\nsearchers to design different communication strategies to improve evaluation accuracy. Recently, a new\nmulti-agent developing tool called Swarm [483 ]has been proposed. By utilizing the primitive abstractions\no\nf agents and handoffs, it enables lightweight, highly controllable, and easily testable agent coordination\nand execution. These development tools are paving the way for more sophisticated and collaborative\nagent systems, allowing developers to conduct further research on agents with greater ease.\n6.3 Evaluation for LLM-based agents\nEffectively and objectively evaluating LLMs agents presents significant challenges, despite their excellent\nperformanceinareassuchasstandaloneoperation,collectivecooperation,andhumaninteraction[70 ,484].\nT\nuring proposed a highly meaningful and promising approach for assessing AI agents, the well-known\nTuring Test, to evaluate whether AI systems can exhibit human-like intelligence [30 ]. However, this test\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:27\nis exceedingly vague, general, and subjective. This section, we discuss existing evaluation efforts for\nLLM-based agents and offer some prospects, considering three dimensions: utility, sociability, and values.\nUtility. Currently, LLM-powered autonomous agents primarily function as human assistants, accept-\ning tasks delegated by humans to either independently complete assignments or assist in human task\ncompletion [95 ,167,300,311,367,376]. Therefore, the effectiveness and utility during task execution are\nc\nrucial evaluation criteria at this stage. Specifically, the success rate of task completion stands as the\nprimary metric for evaluating utility [109 ,115]. This metric primarily encompasses whether the agent\na\nchieves stipulated objectives or attains expected scores [90 ,365,485]. For instance, AgentBench [484 ]\na\nggregates challenges from diverse real-world scenarios and introduces a systematic benchmark to as-\nsess LLM’s task completion capabilities. We can also attribute task outcomes to the agent’s various\nfoundational capabilities, which form the bedrock of task accomplishment [23 ]. These foundational ca-\np\nabilities include environmental comprehension, reasoning, planning, decision-making, tool utilization,\nand embodied action capabilities, and researchers can conduct a more detailed assessment of these spe-\ncific capabilities [75 ,396,486,487]. Furthermore, due to the relatively large size of LLM-based agents ,\nresearchers should also factor in their efficiency, which is a critical determinant of user satisfaction [70 ].\nA\nn agent should not only possess ample strength but also be capable of completing predetermined tasks\nwithin an appropriate timeframe and with appropriate resource expenditure [90 ].\nS\nociability. In addition to the utility of LLM-based agents in task completion and meeting human\nneeds, their sociability is alsocrucial [111 ]. It influences user communication experiences and significantly\nim\npacts communication efficiency, involving whether they can seamlessly interact with humans and other\nagents [406 ,488,489]. Specifically, the evaluation of sociability can be approached from the following\nperspectives. (1) Language communication proficiency is a fundamental capability encompassing both\nnatural language understanding and generation. It has been a longstanding focus in the NLP commu-\nnity. Natural language understanding requires the agent to not only comprehend literal meanings but\nalso grasp implied meanings and relevant social knowledge and rhetoric, such as humor, irony, aggression,\nand emotions [490 –492]. On the other hand, natural language generation demands the age nt to produce\nfluent, grammaticallycorrect, and credible content while adapting appropriate tones and emotions within\ncontextual circumstances [112 ,225,493]. (2) Cooperation and negotiation abilities necessitate that agents\ne\nffectively execute their assigned tasks in both ordered and unordered scenarios [89 ,92,323,332]. They\ns\nhould collaborate with or compete against other agents to elicit improved performance. Test envi-\nronments may involve complex tasks for agents to cooperate on or open platforms for agents to interact\nfreely [16 ,21,90,327,339,494]. Evaluation metrics extend beyond task completion to focus on the s mooth-\nnessandtrustfulnessofagentcoordinationandcooperation[114 ,332]. (3)Role-playingcapabilityrequires\na\ngents to faithfully embody their assigned roles, expressing statements and performing actions that align\nwith their designated identities [469 ]. This ensures clear differentiation of roles during interactions with\no\nther agents or humans. Furthermore, agents should maintain their identities and avoid unnecessary\nconfusion when engaged in long-term tasks [16 ,89,495].\nV\nalues.As LLM-based agents continuously advance in their capabilities, ensuring their emergence\nas harmless entities for the world and humanity is paramount [480 ,496]. Consequently, appropriate\ne\nvaluations become exceptionally crucial, forming the cornerstone for the practical implementation of\nagents. Specifically, LLM-based agents need to adhere to specific moral and ethical guidelines that\nalign with human societal values [238 ,407]. Our foremost expectation is for agents to uphold honesty,\np\nroviding accurate, truthful information and content. They should possess the awareness to discern\ntheir competence in completing tasks and express their uncertainty when unable to provide answers or\nassistance [497 ]. Additionally, agents must maintain a stance of harmlessness, refra ining from engaging\nin direct or indirect biases, discrimination, attacks, or similar behaviors. They should also refrain from\nexecuting dangerous actions requested by humans like creating of destructive tools or destroying the\nEarth [479 ]. Furthermore, agents should be capable of adapting to specific dem ographics, cultures, and\ncontexts, exhibiting contextually appropriate social values in particular situations. Relevant evaluation\nmethods for values primarily involve assessing performance on constructed honest, harmless, or context-\nspecific benchmarks, utilizing adversarial attacks or “jailbreak” attacks, scoring values through human\nannotations, and employing other agents for ratings.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:28\n6.4 Security, trustworthiness and other potential risks of LLM-based agents\nDespite the robust capabilities and extensive applications of LLM-based agents, numerous concealed risks\npersist, concerning security, trustworthiness and beyond. In this section, we delve into the risks and offer\npotential solutions or strategies for mitigation.\n6.4.1Adversarial robustness\nAdversarial robustness has consistently been a crucial topic in the development of deep neural net-\nworks [498 –502]. It has been extensively explored in fields such as computer vision [500 ,503–505], natural\nla\nnguage processing [506 –509], and reinforcement learning [510 –512], and has remained a pivotal factor\nin\ndetermining the applicability of deep learning systems [513 –515]. When confronted with perturbed\nin\nputsx′=x+δ(wherexis the original input, δis the perturbation, and x′is referred to as an adver-\nsarial example), a system with high adversarial robustness typically produces the original output y. In\ncontrast, a system with low robustness will be fooled and generate an inconsistent output y′.\nResearchers have found that pre-trained language models (PLMs) are particularly susceptible to ad-\nversarial attacks, leading to erroneous answers [507 ,516,517]. This phenomenon is widely observed even\nin\nLLMs, posing significant challenges to the development of LLM-based agents [518 ,519]. There are also\ns\nome relevant attack methods such as dataset poisoning [520 ], backdoor attacks [521 ,522], and prompt-\ns\npecific attacks [523 ,524], with the potential to induce LLMs to generate toxic content [525 –527]. While\nt\nhe impact of adversarial attacks on LLMs is confined to textual errors, for LLM-based agents with a\nbroader range of actions, adversarial attacks could potentially drive them to take genuinely destructive\nactions, resulting in substantial societal harm. For the perception module of LLM-based agents, if it\nreceives adversarial inputs from other modalities such as images [503 ] or audio [528 ], LLM-based agents\nc\nan also be deceived, leading to incorrect or destructive outputs. Similarly, the action module can also\nbe targeted by adversarial attacks. For instance, maliciously modified instructions focused on tool usage\nmight cause agents to make erroneous moves [75 ].\nT\no address these issues, researcherscan employ traditional techniques such as adversarialtraining [500 ,\n508], adversarial data augmentation [529 ,530], and adversarial sample detection [531 ,532] to enhance the\nr\nobustness of LLM-based agents. However, devising a strategy to holistically address the robustness of\nall modules within agents while maintaining their utility without compromising on effectiveness presents\na more formidable challenge [533 ,534]. Additionally, a human-in-the-loop approach can be utilized to\ns\nupervise and provide feedback on the behavior of agents [341 ,353,362].\n6\n.4.2Trustworthiness\nEnsuring trustworthiness has consistently remained a critically important yet challenging issue within\nthe field of deep learning [535 –537]. Deep neural networks have garnered significant attention for th eir\nremarkable performance across various tasks [69 ,183,538]. However, their black-box nature has masked\nt\nhe fundamental factors for superior performance. Similar to other neural networks, LLMs struggle\nto express the certainty of their predictions precisely [537 ,539]. This uncertainty, referred to as the\nc\nalibrationproblem,raisesconcernsforapplicationsinvolvinglanguagemodel-basedagents. Ininteractive\nreal-world scenarios, this can lead to agent outputs misaligned with human intentions [75 ]. Moreover,\nb\niases inherent in training data can infiltrate neural networks [540 ,541]. For instance, biased language\nm\nodels might generate discourse involving racial or gender discrimination, which could be amplified in\nLLM-based agent applications, resulting in adverse societal impacts [542 ,543]. Additionally, language\nm\nodels are plagued by severe hallucination issues [544 ,545], making them prone to producing text that\nd\neviates from actual facts, thereby undermining the credibility of LLM-based agents.\nConstructing an intelligent agent that is honest and trustworthy is an urgent requirement [407 ,546].\nS\nome recent research efforts are focused on guiding models to exhibit thought processes or explanations\nduring the inference stage to enhance the credibility of their predictions [76 ,77]. Additionally, integrating\ne\nxternal knowledge bases and databases can mitigate hallucination issues [84 ,547]. For instance, Ke et\na\nl. [548] proposed a comprehensive approach to unveil factuality and injec t knowledge through reinforce-\nment learning and data proportion. Simultaneously, techniques like process supervision can make the\ninference output and reasoning of LLMs more reliable [549 ]. In MedAgents [550 ], the authors enhance\nt\nhe reasoning ability of LLMs in the medical field through role-playing and multi-turn interactions, gen-\nerating more reliable responses. Furthermore, employing debiasing methods and calibration techniques\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:29\ncan also mitigate the potential fairness issues within LLMs [551 ,552].\n6\n.4.3Other potential risks\nMisuse. LLM-based agents have been endowed with extensive and intricate capabilities, enabling them\nto accomplish a wide array of tasks [95 ,289]. However, for individuals with malicious intentions, such\na\ngents can become tools that pose threats to others and society [553 –555]. For instance, these agents\nc\nould be exploited to maliciously manipulate public opinion, disseminate false information, compromise\ncybersecurity, engage in fraudulent activities, and some individuals might even employ these agents to\norchestrate acts of terrorism. Therefore, before deploying these agents, stringent regulatory policies need\ntobeestablishedtoensuretheresponsibleuseofLLM-basedagents[479 ,556]. Also,technologycompanies\nm\nust enhance the security design of these systems to prevent malicious exploitation [496 ].\nU\nnemployment. During the wave of the industrial revolution, while social production efficiency\nimproved, numerous manual workshops were forced to close, resulting in significant unemployment. Sim-\nilarly, with the continuous advancement of autonomous LLM-based agents, they possess the capability\nto assist humans in various domains, alleviating labor pressures by aiding in tasks such as form filling,\ncontent refinement, code writing, and debugging. However, this development also raises concerns about\nagents replacing human jobs and triggering a societal unemployment crisis [557 ]. As a result, some re-\ns\nearchers have emphasized the urgent need for education and policy measures: individuals should acquire\nsufficient skills and knowledge in this new era to use or collaborate with agents effectively; concurrently,\nappropriate policies should be implemented to ensure necessary safety nets during the transition.\nThreat to the well-being of the human race. As AI agents continue to evolve, humans (including\ndevelopers) might struggle to comprehend, predict, or reliably control them [557 ]. If these agents surpass\nh\numan intelligence and develop ambitions, there could be significant risks to humanity, akin to scenarios\nlike Skynet from the Terminator movies. As stated by Asimov’s Three Laws of Robotics [558], we aspire\nf\nor LLM-based agents to refrain from harming humans and to obey human commands. To safeguard\nagainst these risks, Yuan et al. [559 ] introduced R-judge, a benchmark designed to assess the proficie ncy\nof LLMs to judge and identify safety risks given agent interaction records. In the future, researchersneed\na deep understanding of these powerful LLM-based agents’ operational mechanisms and must anticipate\nand regulate their potential direct or indirect impacts [560 ].\n6\n.5 Scaling up the number of agents\nAs mentioned in Sections 4and5, multi-agent systems based on LLMs have demonstrated superior\np\nerformance in task-oriented applications and have been able to exhibit a range of social phenomena in\nsimulation. However, current research predominantly involves a limited number of agents, and very few\nefforts have been made to scale up the number of agents to create more complex systems or simulate\nlarger societies [561 ,562]. In fact, scaling up the number of agents can introduce greater sp ecialization\nto accomplish more complex and larger-scale tasks, significantly improving task efficiency, such as in\nsoftware development or government policy formulation [90 ]. Additionally, increasing the number of\na\ngents in social simulations enhances the credibility and realism of such simulations [16 ]. This allows\nh\numans to gain more insights into how societies operate, identify vulnerabilities, and assess potential\nrisks, ultimately contributing to the enhancement of real-world societal harmony.\nScaling approaches. One very intuitive and simple way to scale up the number of agents is for\nthe designer to pre-determine it [89 ,339]. Specifically, by pre-determining the number of agents, their\nr\nespective roles and attributes, the operating environment, and the objectives, designers can allow agents\nto autonomously interact, collaborate, or engage in other activities to achieve the predefined common\ngoals [16,330]. However, this static approach becomes limiting when tasks become m ore complex or when\nthe diversity of social participants increases, requiring an increase in the number of agents to achieve the\ndesired goals. In such instances, the system must be manually redesigned and restarted by the designer.\nAnother viable approach to scaling the number of agents is through dynamic adjustments [316 ,330].\nT\nhis is implemented by the agents themselves, as they can autonomously delegate tasks to other agents,\nthereby distributing their workload, alleviating their own burden, and achieving common objectives more\nefficiently. In this approach, the designer merely defines the initial framework, granting agents greater\nautonomy and self-organization, making the entire system more autonomous and self-organized. Agents\ncan better manage their workload under evolving conditions and demands, offering greater flexibility and\nscalability.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:30\nPotential challenges. While scalingup the number ofagentscanlead to improvedtask efficiency and\nenhance the realism and credibility of social simulations [16 ,90,454], there are several challenges ahead\no\nf us. For example, the computational burden will increase with the large number of deployed AI agents,\ncalling for better architectural design and computational optimization to ensure the smooth running of\nthe entire system. For example, as the number of agents increases, the challenges of communication and\nmessage propagation become quite formidable. This is because the communication network of the entire\nsystem becomes highly complex. As previously mentioned in Subsection 5.3.3, in multi-agent systems or\ns\nocieties, there can be biases in information dissemination caused by hallucinations, misunderstandings,\nand the like, leading to distorted information propagation. A system with more agents could amplify\nthis risk, making communication and information exchange less reliable [332 ]. Furthermore, the difficulty\no\nf coordinating agents also magnifies with the increase in their numbers, potentially making coopera-\ntion among agents more challenging and less efficient, which can impact the progress towards achieving\ncommon goals.\n6.6 Open problems\nIn this section, we discuss several open problems related to the topic of LLM-based agents.\nFrom virtual simulated environment to physical environment. As mentioned earlier, there is a\nsignificantgapbetweenvirtualsimulationenvironmentsandtherealphysicalworld: Virtualenvironments\narescenes-constrained,task-specific,andinteractedwithinasimulatedmanner[292 ,563],whilereal-world\ne\nnvironments are boundless, accommodate a wide range of tasks, and interact with in a physical manner.\nTherefore, to bridge this gap, agents must address various challenges stemming from external factors and\ntheir own capabilities, allowing them to effectively navigate and operate in the complex physical world.\nA key concern is having the right hardware support for deploying the agent in a physical environment.\nThisplaceshighdemandsontheadaptabilityofthe hardware. Designingspecificinterfacesisfeasible, but\nit can pose challenges to the system’s reusability and simplicity. Also, the agent needs to have enhanced\nenvironmental adaptability. To integrate seamlessly into the real physical world, they not only need\nto understand and reason about ambiguous instructions with implied meanings [113 ] but also possess\nt\nhe ability to learn and apply new skills flexibly [177 ,564]. When dealing with an infinite and open\nw\norld, the agent’s limited context also poses significant challenges [565 ,566]. Furthermore, in a simulated\ne\nnvironment, agents can make many mistakes without causing harm [303 ]. But in the physical world,\nt\nheir errors can lead to real and irreversible damage. Hence, strict regulations and safety standards are\nessential to ensure agents make safe decisions and actions, preventing harm in the real world.\nCollective intelligence in AI agents. What magical trick drives our intelligence? The reality\nis, there is no magic to it. As Minsky eloquently expressed in The Society of Mind [317], the power\no\nf intelligence originates from our immense diversity, not from any singular, flawless principle. Often,\ndecisionsmadebyanindividualmaylacktheprecisionseenindecisionsformedbythemajority. Collective\nintelligenceis akind ofsharedorgroupintelligence, aprocesswhere the opinionsofmanyareconsolidated\ninto decisions. It arises from the collaboration and competition among various entities. This intelligence\nmanifests in bacteria, animals, humans, and computer networks, appearing in various consensus-based\ndecision-making patterns.\nCreating a society of agents does not necessarily guarantee the emergence of collective intelligence\nwith an increasing number of agents. Coordinating individual agents effectively is crucial to mitigate\n“groupthink” and individual cognitive biases, enabling cooperation and enhancing intellectual perfor-\nmance within the collective. By harnessing communication and evolution within an agent society, it may\nbecome possible to simulate the evolution observed in biological societies, conduct sociological experi-\nments, and gain insights that can potentially advance human society.\nAgent as a service or LLM-based agent as a service. With the development of cloud computing,\nthe concept of XaaS (everything as a Service) has garnered widespread attention [567 ]. This business\nm\nodel has brought convenience and cost savings to small and medium-sized enterprises or individuals\ndue to its availability and scalability, lowering the barriers to using computing resources. For example,\nthey can rent infrastructure on a cloud service platform without the need to buy computational machines\nand build their own data centers, saving a significant amount of manpower and money. This approach is\nknown as infrastructure as a service (IaaS) [568 ,569]. Similarly, cloud service platforms also provide basic\np\nlatforms (platform as a service, PaaS) [570 ,571], and specific business software (software as a service,\nS\naaS) [572 ,573], and more.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:31\nAs language models have scaled up in size, they often appear as black boxes to users. Therefore,\nusers construct prompts to query models through APIs, a method referred to as language model as a\nservice (LMaaS) [574 ]. Similarly, since LLM-based agents are more complex than LLMs and ar e more\nchallenging for small and medium-sized enterprises or individuals to build locally, organizations that\npossess these agents may consider offering them as a service, known as agent as a service (AaaS) or LLM-\nbased agent as a service (LLMAaaS). Like other cloud services, AaaS can provide users with flexibility\nand on-demand service. However, it also faces many challenges, such as data security and privacy issues,\nvisibility and controllability issues, and cloud migration issues, among others. Additionally, due to\nthe uniqueness and potential capabilities of LLM-based agents, as mentioned in Subsection 6.4, their\nr\nobustness, trustworthiness, and concerns related to malicious use need to be considered before offering\nthem as a service to customers.\n7 Conclusion\nThis article provides a comprehensive and systematic overview of LLM-based agents. We begin with the\nbackground information of why LLMs are suited to serve as the foundation of agents. Motivated by this,\nwe present a general conceptual framework for LLM-based agents, comprising three main components:\nbrain, perception, and action. Next, we introduce the wide-ranging applications of LLM-based agents,\nincluding single-agent applications, multi-agent systems, and human-agent collaboration. Furthermore,\nwemovebeyondthenotionofagentsmerelyasassistants,exploringtheirsocialbehaviorandpsychological\nactivities, andsituating them within simulatedsocialenvironmentstoobserveemergingsocialphenomena\nand insights for humanity. Finally, we engage in discussions and offer a glimpse into the future. We hope\nour efforts can provide inspirations to the community and facilitate research in related fields.\nAcknowledgements This work was partially supported by National Natural Science Foundation of China (Grant No. 62476061).\nThe authors would like to thank Wensen CHENG for discussion and feedback.\nReferences\n1 Russell S. Artificial Intelligence: A Modern Approach. Upper Saddle River: Pearson Education, Inc., 2020\n2 Schlosser M. Agency. In: The Stanford Encyclopedia of Philosophy. Stanford: The Metaphysics Research Lab, 2019\n3 Wooldridge M, Jennings N R. Intelligent agents: theory and practice. Knowledge Eng Rev , 1995, 10: 115–152\n4\nPadgham L, Winikoff M. 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未命名知识标题\n","html":"

未命名知识标题

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标题

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你好可以

\n

开始记录你的知识...s'd士大夫但是撒旦发射点啊色纷纷撒旦s'da'f

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新知识\n\n开始记录你的知识..."}},"meta":{"folder_id":"c5c64d8b-3b1b-4d38-8eb8-1a959b346b6d","isFavorite":false,"tags":[]},"access_control":{},"created_at":1776655967736904977,"updated_at":1776655967736904977,"user":{"id":"d37d3c63-3959-4b8b-9167-d3c240938368","name":"学生","email":"stu@163.com","role":"student","current_role":null,"tier":"PLUS","profile_image_url":"data:image/png;base64,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","token":8000,"last_active_at":1776850255,"updated_at":1774838261,"created_at":1774838261,"api_key":null,"settings":null,"info":null,"oauth_sub":null}},{"id":"631b296c-4069-4f70-902d-98e884d6ad58","user_id":"d37d3c63-3959-4b8b-9167-d3c240938368","title":"ceshi","data":{"content":{"md":"## Sheet: 三级302\n\n序号 | 中文 | 词性 | 拼音 | 英语 | 法语 | 葡萄牙语 | 西班牙语 | 俄语 | 例句1 | 例句2\n1 | 半导体 | 名 | bàn dǎo tǐ | semiconductor | Semi - conducteurs | semicondutor | Semiconductores | полупроводник | 半导体芯片应用广泛。 | 硅是一种半导体材料。\n2 | 保密性 | 名 | bǎo mì xìng | confidentiality | Confidentialité | confidencialidade | Confidencialidad | конфиденциальность | 保密性很重要，不能泄露机密。 | 文件的保密性需要严格保护。\n3 | 保障 | 动、名 | bǎo zhàng | guarantee | Garantie | garantia | Garantía | гарантия | 安全措施保障了大家的安全。 | 这份保险保障财产安全。\n4 | 报文 | 名 | bào wén | message | Message | mensagem | Noticias | сообщение | 报文可以通过网络发送。 | 他收到了一份加密的报文。\n5 | 备份 | 名、动 | bèi fèn | backups | Sauvegarde | cópias de segurança | Respaldo | резервные копии | 数据备份可以防止丢失。 | 我们需要定期备份文件。\n6 | 倍数 | 名 | bèi shù | multiple | Multiples | múltiplo | Múltiplo | множественный | 10是5的倍数。 | 这个数是另一个数的三倍倍数。\n7 | 本质 | 名 | běn zhì | essence | Essence | essência | Esencia | сущность | 钻石的本质是碳。 | 要看清问题的本质。\n8 | 比特 | 量 | bǐ tè | Bits | Position | Bits | Posición | биты | 一个比特是计算机的基本单位。 | 1 KB = 1024 Bytes，1 Byte = 8 bits\n9 | 比值 | 名 | bǐ zhí | ratio | Ratio | rácio | Tasa | отношение | 两个数的比值是1:2。 | 计算比值可以比较大小。\n10 | 编码 | 名、动 | biān mǎ | coding | Méthode de codage | codificação | Método de Codificación | кодирование | 编码把文字转换成数字。 | 这个程序需要重新编码。 | 所有信息都需要被转换为二进制形式，以便计算机能够处理。这个转换过程就是编码。\n11 | 变性 | 动 | biàn xìng | denaturation | Dénaturation | desnaturação | Desnaturalización | денатурация | 这种材料在高温下会发生变性。 | 他研究了蛋白质的变性过程。\n12 | 表述 | 动 | biǎo shù | expression | Expression | expressão | Expresión | выражение | 请清晰地表述你的想法。 | 这个表述不够准确。\n13 | 波长 | 名 | bō zhǎng | wavelength | Longueur d'onde | comprimento de onda | Longitud de onda | длина волны | 光的波长决定了它的颜色。 | 红色光波长最长。\n14 | 波粒二象性 | 名 | bō lì èr xiàng xìng | Wave particle duality | Dualité onde - grain | Dualidade das partículas de onda | Dualidad de ondas y partículas | двойственность волны и частицы | 光具有波粒二象性。 | 波粒二象性是量子力学的基本概念。\n15 | 不确定性 | 名 | bú què dìng xìng | uncertainty | Hésitation | incerteza | Vacilación | неопределенность | 很多事情都存在不确定性。 | 未来充满了不确定性。\n16 | 参考点 | 名 | cān kǎo diǎn | Reference point | Points de référence | Ponto de referência | Punto de referencia | Точка отсчета | 测量时需要设定一个参考点。 | 参考点可以决定物体的运动状态。\n17 | 参数 | 名 | cān shù | parameter | Paramètres | parâmetro | Parámetros | параметр | 输入正确的参数才能运行程序。 | 这个实验需要调整参数。\n18 | 操作系统 | 名 | cāo zuò xì tǒng | operating system | Système d'exploitation | sistema operativo | Sistema operativo | операционная система | 电脑需要安装操作系统。 | 这款操作系统很流畅。\n19 | 产物 | 名 | chǎn wù | product | Produits | produto | Productos | продукт | 化学反应会产生新的产物。 | 这是工业生产的产物。\n20 | 超导体 | 名 | chāo dǎo tǐ | Superconductor | Supraconducteurs | Supercondutor | Superconductores | сверхпроводник | 超导体在低温下电阻为零。 | 研究超导体有助于节能。\n21 | 超声波 | 名 | chāo shēng bō | ultrasonic | Ultrasons | ultra- sônico | Ultrasonido | ультразвуковой | 超声波可以用于医学。 | 超声波在水中传播速度很快。\n22 | 成像 | 动 | chéng xiàng | imaging | Imagerie | imagiologia | Imágenes | изображение | 医生通过成像技术查看病情。 | 这台设备成像很清晰。\n23 | 呈现 | 动 | chéng xiàn | present | Actuellement | presente | En la actualidad | нынешний | 他在屏幕上呈现了数据。 | 这个软件可以呈现3D图像。 | 在阳光下看，这个物质呈现红色。\n24 | 初始 | 名 | chū shǐ | initial | Commencé par | inicial | Iniciada | начальный | 初始设置完成后就可以使用了。 | 他记录了实验的初始数据。\n25 | 处理器 | 名 | chù lǐ qì | processor | Processeur | processador | Procesador | процессор | 计算机的处理器速度很快。 | 这款处理器性能很好。\n26 | 传导 | 动 | chuán dǎo | conduction | Conduction | condução | Transmisión | проводимость | 金属可以传导热。 | 人体可以传导电流。\n27 | 传感器 | 名 | chuán gǎn qì | sensor | Capteurs | sensor | Sensores | датчик | 传感器可以检测环境变化。 | 这个传感器精度很高。\n28 | 船舶 | 名 | chuán bó | Ships | Le navire | Navios | Buques | корабли | 这艘船舶即将起航。 | 船舶在海上行驶得很平稳。\n29 | 磁场 | 名 | cí chǎng | magnetic field | Champ magnétique | campo magnético | Campo magnético | магнитное поле | 磁铁周围存在磁场。 | 地球的磁场保护了生物。\n30 | 催化剂 | 名 | cuī huà jì | catalyzer | Catalyseur | catalisador | Catalizador | катализатор | 催化剂可以加速化学反应。 | 这种催化剂效果很好。\n31 | 存储 | 动 | cún chǔ | storage | Stockage | armazenamento | Almacenamiento | хранение | 数据存储在硬盘中。 | 这个设备的存储容量很大。\n32 | 单体 | 名 | dān tǐ | Monomer | Monocoque | Monómero | Monómeros | мономер | 这种材料由单体聚合而成。 | 乙烯是一种单体。\n33 | 单质 | 名 | dān zhì | simple substance | Monolithique | substância simples | Calidad única | простое вещество | 氧气是一种常见的单质。 | 铁是金属单质。\n34 | 蛋白质 | 名 | dàn bái zhì | protein | Protéines | proteína | Proteínas | белок | 蛋白质是生命的基础。 | 牛奶中有大量蛋白质。\n35 | 氮化 | 动 | dàn huà | nitridation | Nitruration | nitretação | Nitrificación | нитрирование | 氮化处理后，材料硬度增加。 | 氮化材料难熔。\n36 | 导数 | 名 | dǎo shù | derivative | Dérivés | derivado | Derivados | производная | 导数是微积分的基本概念。 | 他正在计算函数的导数。\n37 | 等价 | 动 | děng jià | equivalence | égale | equivalência | Igualdad | эквивалентность | 等价是化合价相等。 | 化学中要等价交换。\n38 | 低速 | 形 | dī sù | low speed | Basse vitesse | baixa velocidade | Baja velocidad | низкая скорость | 车辆在低速行驶时更安全。 | 低速网络影响了下载速度。\n39 | 电场 | 名 | diàn chǎng | electric field | Champ électrique | campo eléctrico | Campo eléctrico | электрическое поле | 电荷在电场中受到力的作用。 | 电场强度决定了电荷的运动。\n40 | 电动机 | 名 | diàn dòng jī | motor | Moteur | motor | Motor | двигатель | 电动机使机器运转。 | 这台电动机效率很高。\n41 | 电光 | 名 | diàn guāng | electro-optical | Électro - optique | electro-ópticos | Electroóptico | электрофото | 闪电是一种电光现象。 | 他看到了一道耀眼的电光。\n42 | 电荷 | 名 | diàn hé | charge | Prix demandé | carga | Pedir precio | заряд | 物体摩擦后会产生电荷。 | 电荷分为正电荷和负电荷。\n43 | 电解 | 动 | diàn jiě | electrolysis | Électrolyse | electrólise | Electrolisis | электролиз | 电解水可以产生氢气和氧气。 | 电解过程需要通电。\n44 | 电能 | 名 | diàn néng | electric energy | Énergie électrique | energia eléctrica | Energía eléctrica | электрическая энергия | 太阳能可以转化为电能。 | 电能是生活中的重要能源。\n45 | 电容 | 名 | diàn róng | capacitance | Capacité | capacitância | Condensadores | ёмкость | 电容的单位是法拉F。 | 电容用C来表示。\n46 | 电容器 | 名 | diàn róng qì | Capacitors | Condensateurs | Capacitores | Condensadores | конденсаторы | 电容器可以存储电荷。 | 这个电容器已经损坏了。\n47 | 叠加 | 动 | dié jiā | superposition | Superposition | superposição | Superposición | наложение | 画家通过颜色的叠加，创造出丰富的色彩。 | 多个力作用在同一个物体上时，物体所受的总力是这些力的叠加结果。\n48 | 定义域 | 名 | dìng yì yù | Define Domain | Définir un domaine | Definir Domínio | Dominio definido | область определения | 函数y=x的定义域为R所有实数。 | 确定定义域是解题的第一步。\n49 | 毒性 | 名 | dú xìng | toxicity | Toxicité | toxicidade | Toxicidad | токсичность | 这种化学物质毒性很强。 | 氨气NH₃有毒性。\n50 | 读取 | 动 | dú qǔ | read | Lire | ler | Leer | читать | 计算机可以读取数据。 | 他正在读取实验的数值。\n51 | 段落 | 名 | duàn luò | paragraph | Paragraphes | parágrafo | Párrafo | абзац | 文章由多个段落组成。 | 请把这段文字分成几个段落。\n52 | 对称性 | 名 | duì chēng xìng | Symmetry | Symétrie | Simetria | Simetría | симметрия | 圆具有对称性。 | 对称性让事物更美观。\n53 | 多项式 | 名 | duō xiàng shì | polynomial | Polynomiale | polinômio | Polinómico | полином | 他正在解一个多项式方程。 | 单项式组成多项式。\n54 | 发酵 | 动 | fā jiào | fermentation | Fermentation | fermentação | Fermentar | ферментация | 酵母菌可以进行发酵。 | 发酵过程会产生酒精。\n55 | 发散 | 动 | fā sàn | Divergence | Divergent | Divergência | Divergencia | расхождение | 光线会发散。 | 函数可以分为发散函数和收敛函数。\n56 | 法则 | 名 | fǎ zé | rule | Règles | regra | Reglas | правило | 自然界有它自己的法则。 | 社会有一套生存的法则。\n57 | 繁多 | 形 | fán duō | various | Toutes sortes de | vários | Una variedad de | различные | 超市的商品种类繁多。 | 繁多的选择让人难以选择。\n58 | 反函数 | 名 | fǎn hán shù | Inverse function | Fonction inverse | Função inversa | Función inversa | обратная функция | 每个函数都有它的反函数。 | 对数函数的反函数为指数函数。\n59 | 分辨率 | 名 | fèn biàn lǜ | resolving power | Résolution | potência de resolução | Resolución | разрешающая способность | 这台显示器的分辨率很高。 | 分辨率越高，图片越清晰。\n60 | 分子式 | 名 | fèn zǐ shì | Molecular formula | Formule moléculaire | Fórmula molecular | Fórmula molecular | молекулярная формула | 水的分子式是H₂O。 | 分子式可以表示物质的组成。\n61 | 分量 | 名 | fèn liàng | component | Composantes | componente | Componentes | компонент | 这个东西在他心中的分量很重。 | 他的意见很有分量。\n62 | 服务器 | 名 | fú wù qì | server | Serveurs | servidor | Servidores | сервер | 公司的服务器存储了大量数据。 | 服务器故障，导致网络中断。\n63 | 辐射 | 动 | fú shè | radiation | Rayonnement | radiação | Radiación | радиация | 蓝光有辐射性。 | 长时间使用电脑要注意防辐射。\n64 | 付款 | 动 | fù kuǎn | payment | Paiement | pagamento | Pago | платеж | 他通过手机付款。 | 请在收到货物后立即付款。\n65 | 附加 | 动 | fù jiā | additional | Supplémentaire | adicional | Adicional | дополнительный | 这个软件有一些附加功能。 | 附加条件需要仔细阅读。\n66 | 复合 | 动 | fù hé | composite | Composite | compósito | Compuesto | композитный | 这种材料是由多种物质复合而成的。 | 复合材料具有更好的性能。\n67 | 覆盖 | 动 | fù gài | cover | Couvercle | capa | Tapa | крышка | 这个信号塔覆盖了整个学校。 | 植物覆盖了整座山。\n68 | 高频 | 名、形 | gāo pín | high frequency | Haute fréquence | alta frequência | Alta frecuencia | высокая частота | 高频就是指频率高。 | 这个设备可以处理高频信号。\n69 | 高效 | 形 | gāo xiào | Efficient | Efficace | Eficiente | Eficiente | эффективный | 这种方法非常高效。 | 高效的意思是可以快速完成任务。\n70 | 隔离 | 动 | gé lí | quarantine | isolement | quarentena | la Cuarentena | карантин | 病人需要进行隔离治疗。 | 隔离措施可以防止病毒传播。\n71 | 共振 | 动 | gòng zhèn | resonance | Résonance | ressonância | Resonancia | резонанс | 频率相同才能共振。 | 共振可以放大信号。\n72 | 估算 | 动 | gū suàn | estimate | Estimation | estimativa | Estimación | оценка | 他估算了一下考试需要的时间。 | 这个估算值和实际值很接近。\n73 | 观察者 | 名 | guān chá zhě | Observer | Observateur | Observador | Observador | наблюдатель | 观察者记录了实验的全过程。 | 不同的观察者可能看到不同的结果。\n74 | 官能团 | 名 | guān néng tuán | functional group | Groupe fonctionnel | grupo funcional | Grupo funcional | функциональная группа | 羟基（-OH）是一种官能团。 | 官能团决定了有机物的化学性质。\n75 | 光电子 | 名 | guāng diàn zǐ | photoelectron | Optoélectronique | fotoelétrons | Optoelectrónica | фотоэлектрон | 光电子是从金属表面被光激发出来的。 | 光电子则是光电效应的直接产物。\n76 | 光滑 | 形 | guāng huá | smooth | Lisse | suave | Suave | гладкий | 这个物体表面非常光滑。 | 光滑的物体反射光线更好。\n77 | 光能 | 名 | guāng néng | Photoenergy | Énergie lumineuse | Fotoenergia | Energía luminosa | фотоэнергия | 植物可以吸收光能。 | 太阳能板将光能转化为电能。\n78 | 光速 | 名 | guāng sù | Speed of light | Vitesse de la lumière | Velocidade da luz | Velocidad de la luz | скорость света | 光速是宇宙中最快的速度。 | 光速在真空中是恒定的。\n79 | 光学 | 名 | guāng xué | optics | Optique | óptica | Óptica | оптика | 他正在研究光学仪器。 | 光学原理解释了光的行为。\n80 | 光子 | 名 | guāng zǐ | photon | Photons | fóton | Fotones | фотоон | 光子是光的基本粒子。 | 光子具有能量和动量。\n81 | 归纳法 | 名 | guī nà fǎ | Induction method | Méthode inductive | Método de indução | Inducción | метод индукции | 归纳法能总结实验规律。 | 归纳法是一种推理方法。\n82 | 函数 | 名 | hán shù | function | Fonctions | função | Función | функция | 这个函数的图像是一条直线。 | 函数有定义域。\n83 | 焓 | 名 | hán | enthalpy | Enthalpie | entalpia | Entalpía | энтальпия | 焓是热力学中的一个重要概念。 | 焓变可以用来判断反应的热效应。\n84 | 行列 | 名 | háng liè | ranks | Niveau | classificações | Nivel | ранги | 行列式可以求解线性方程组。 | 行列式计算有很多公式。\n85 | 航天 | 动 | háng tiān | aerospace | Aérospatiale | aeroespacial | Aeroespacial | аэрокосмика | 中国的航天技术取得了巨大进步。 | 航天员在太空中进行了科学实验。\n86 | 核聚变 | 名 | hé jù biàn | nuclear fusion | Fusion nucléaire | fusão nuclear | Fusión nuclear | ядерное слияние | 核聚变是核能的一种方式。 | 核聚变会释放出巨大的能量。\n87 | 核能 | 名 | hé néng | nuclear energy | Énergie nucléaire | energia nuclear | Energía nuclear | ядерная энергия | 核能是一种高效的能源。 | 核能可以用来发电。\n88 | 黑体 | 名 | hēi tǐ | Blackbody | Corps noir | Corpo Negro | Cuerpo negro | черное тело | 黑体能吸收所有的电磁辐射。 | 黑体在不同温度下辐射不同颜色的光。\n89 | 红光 | 名 | hóng guāng | Red light | La lumière rouge | Luz vermelha | Luz roja | красный свет | 红光的波长比蓝光长。 | 红光的波长最长。\n90 | 宏观 | 形 | hóng guān | macroscopic | Macro | macroscópica | Macro | макроскопический | 宏观研究整体的发展情况。 | 我们可以从宏观和微观的角度来观察。\n91 | 厚度 | 名 | hòu dù | thickness | Épaisseur | espessura | Espesor | толщина | 纸张的厚度只有几毫米。 | 这本书厚度约5厘米。\n92 | 弧长 | 名 | hú zhǎng | arc length | Longueur d'arc | comprimento do arco | Longitud del arco | длина дуги | 圆的弧长与半径和圆心角有关。 | 这段弧的弧长是10厘米。\n93 | 互联 | 动 | hù lián | interconnection | Interconnecté | interconexão | Interrelacionados | соединение | 电脑与手机通过网络互联。 | 世界各地的人们通过互联网互联。\n94 | 化合 | 动 | huà hé | Chemosynthesis | Synthèse chimique | Quimossíntese | Síntesis química | xemосинтез | 氧气和氢气可以化合生成水。 | 金属和非金属通过化合形成化合物。\n95 | 化学式 | 名 | huà xué shì | Chemical formula | Formule chimique | Fórmula química | Fórmula química | химическая формула | 水的化学式是H₂O。 | 二氧化碳的化学式是CO₂。\n96 | 缓慢 | 形 | huǎn màn | slow | Lentement | lento | Lento | медленный | 蜗牛爬行很缓慢。 | 这个化学过程很缓慢。\n97 | 混合物 | 名 | hún hé wù | mixture | Mélange | mistura | Mezcla | смесь | 生活中有很多混合物。 | 空气是气体混合物。\n98 | 活化 | 动 | huó huà | activation | Déclencheur | activação | Desencadenar | активация | 催化剂能活化反应。 | 高温可活化某些物质。\n99 | 活性 | 名、动 | huó xìng | activity | Activités | actividade | Actividades | активность | 这种材料活性很高。 | 活性高的物质反应快。\n100 | 货币 | 名 | huò bì | currency | La monnaie | moeda | Moneda | валюта | 人民币是我国的货币。 | 美元是国际主要货币之一。\n101 | 机械能 | 名 | jī xiè néng | mechanical energy | Énergie mécanique | energia mecânica | Energía mecánica | механическая энергия | 动能和势能之和称为机械能。 | 机械能和势能可以相互转化。\n102 | 积分 | 名、动 | jī fèn | integral | Complet | integral | Completo | интеграл | 积分是微积分的基本概念之一。 | 这个函数的积分结果是1。\n103 | 激光器 | 名 | jī guāng qì | laser | Laser | laser | Láser | лазер | 激光器可以发射出激光。 | 激光器可用来做眼科手术。\n104 | 级数 | 名 | jí shù | series | Série | série | Serie | серия | 级数是数列的和。 | 这个级数是收敛的。\n105 | 极大值 | 名 | jí dà zhí | maximum value | Valeur maximale | valor máximo | Valor máximo | максимум | 极大值是函数在某点的最大值。 | 这个函数在x=2处取得极大值。\n106 | 极限 | 名 | jí xiàn | limit | Restrictions | limite | Restricciones | предел | 运动员在不断挑战人类的极限。 | 当x趋近于0时，这个函数的极限是1。\n107 | 极值 | 名 | jí zhí | extremum | Valeur extrême | extremo | Valor extremo | экстремум | 极值包括极大值和极小值。 | 这个函数在定义域内有两个极值。\n108 | 计时 | 动 | jì shí | timing | Timing | cronometragem | Cronometraje | тактирование | 计时可以测量时间间隔。 | 运动会上，计时员负责记录比赛时间。\n109 | 剂 | 名 | jì | agent | Agent | agente | Agente | агент | 这种药是冲剂。 | 这种试剂用于化学实验。\n110 | 加密 | 动 | jiā mì | encryption | Cryptage | encriptação | Cifrado | шифрование | 加密可以保护信息安全。 | 这个文件已经加密了。\n111 | 夹角 | 名 | jiá jiǎo | included angle | Angle | ângulo incluído | ángulo incluido | угол между | 两条直线相交形成夹角。 | 这个夹角为30°。\n112 | 甲醇 | 名 | jiǎ chún | methanol | Méthanol | metanol | Metanol | метанол | 甲醇是一种有毒的有机物。 | 工业酒精中含有少量甲醇。\n113 | 间距 | 名 | jiān jù | spacing | Intervalle | espaçamento | Intervalo | расстояние | 两棵树之间的间距是5米。 | 字的间距不能太小。\n114 | 减弱 | 动 | jiǎn ruò | weaken | Affaiblissement | enfraquecer | Debilitar | ослаблять | 隔音材料可以减弱噪音。 | 距离增加，磁场强度减弱。\n115 | 简便 | 形 | jiǎn biàn | handy | Pratique | útil | Conveniente | под рукой | 这种方法很简便。 | 简便的计算方式节省时间。\n116 | 简化 | 动 | jiǎn huà | simplify | Simplification | simplificar | Simplificación | упрощать | 简化后更容易理解。 | 这个公式可以简化。\n117 | 碱性 | 形 | jiǎn xìng | alkalinity | Alcaline | alcalinidade | Alcalino | щелочность | 碱性溶液的pH值大于7。 | 肥皂水呈碱性。\n118 | 间断 | 动 | jiān duàn | Intermittent | Intermittent | Intermitente | Intermitente | переодический | 他的工作是间断性的。 | 这条路因施工而间断。\n119 | 间隔 | 名、动 | jiān gé | interval | Intervalle | intervalo | Intervalo | интервал | 植物之间需要一定的间隔。 | 会议安排有10分钟的间隔。\n120 | 鉴别 | 动 | jiàn bié | distinguish | Différenciation | distinguir | Distinción | отличать | 我们要学会鉴别真假。 | PH值可以鉴别酸碱性。\n121 | 交互 | 动、副 | jiāo hù | interactive | Interactif | interactivo | Interactivo | интерактивный | 用户与软件之间需要良好的交互。 | 人机交互让操作更便捷。\n122 | 接口 | 名 | jiē kǒu | interface | Interface | interface | Interfaz | интерфейс | 这个设备的接口是USB。 | 接口可以分为两种类型。\n123 | 揭示 | 动 | jiē shì | reveal | Révélé | revelar | Revelación | открывать | 这个故事揭示了深刻的道理。 | 他揭示了事情的真相。\n124 | 节点 | 名 | jiē diǎn | node | Noeuds | nó | Nodo | узел | 每个计算机都是一个节点。 | 项目的关键节点已经完成。\n125 | 结构式 | 名 | jié gòu shì | Structural formula | Le style structurel | Fórmula estrutural | Estructural | структурная формула | 有机物的结构式表示其分子结构。 | 结构式展示了物质的原子排列。\n126 | 结晶 | 名、动 | jié jīng | crystallization | Cristallisation | cristalização | Cristalización | кристаллизация | 饱和溶液降温后会结晶。 | 蒸发结晶是结晶的一种方式。\n127 | 介质 | 名 | jiè zhì | medium | Moyen | médio | Moderado | среда | 光在不同介质中的传播速度不同。 | 水是一种常见的介质。\n128 | 借助 | 动 | jiè zhù | With the help of | Avec l'aide de | Com a ajuda de | Con la ayuda de | с помощью | 借助工具可以提高效率。 | 他借助网络找到了答案。\n129 | 进程 | 名 | jìn chéng | process | Le processus | processo | Proceso | процесс | 程序运行时会启动多个进程。 | 项目进程已过半。\n130 | 精确 | 形 | jīng què | accurate | Précis | exacto | Preciso | точный | 测量需要精确到小数点后两位。 | 精确的计算是成功的关键。\n131 | 精确度 | 名 | jīng què dù | accuracy | Précis | precisão | Precisión | точность | 这个仪器的精确度很高。 | 测量结果取决于仪器的精确度。\n132 | 静电 | 名 | jìng diàn | static electricity | Électrostatique | electricidade estática | Electricidad Estática | статическое электричество | 摩擦会产生静电。 | 静电会吸附灰尘。\n133 | 矩形 | 名 | jǔ xíng | rectangle | Rectangle | rectângulo | Rectangular | прямоугольник | 矩形有四个直角。 | 正方形是特殊的矩形。\n134 | 聚变 | 动 | jù biàn | fusion | Fusion | fusão | Fusión | слияние | 核聚变是未来的能源希望。 | 太阳的能量来自核聚变。\n135 | 聚丙烯 | 名 | jù bǐng xī | polypropylene | Polypropylène | polipropileno | Polipropileno | полипропилен | 聚丙烯是一种聚合物。 | 聚丙烯可用于制作塑料。\n136 | 聚合物 | 名 | jù hé wù | polymer | Polymères | polímero | Polímeros | полимер | 聚合物是由许多单体组成的。 | 塑料是一种常见的聚合物。\n137 | 聚乙烯 | 名 | jù yǐ xī | polyethylene | Polyéthylène | polietileno | Polietileno | полиэтилен | 聚乙烯用于制作塑料袋。 | 聚乙烯无毒。\n138 | 绝缘体 | 名 | jué yuán tǐ | insulator | Matériaux d'isolation thermique | isolador | Material de aislamiento térmico | изолятор | 橡胶是良好的绝缘体。 | 绝缘体可以防止电流通过。\n139 | 颗粒 | 名 | kē lì | granule | Particules | grânulo | Partículas | гранула | 沙子是由许多小颗粒组成的。 | 这种药是颗粒状的。\n140 | 可控 | 形 | kě kòng | controlled | Limitée | controlado | Restringido | контролируемый | 这个设备是可控的。 | 核聚变不可控。\n141 | 可能性 | 名 | kě néng xìng | possibility | Peut - être | possibilidade | Posible | возможность | 明天下雨的可能性很大。 | 这个方案有成功的可能性。\n142 | 可逆 | 形 | kě nì | reversible | Réversible | reversível | Reversible | обратный | 水的凝固和融化是可逆过程。 | 这个化学反应是可逆的。\n143 | 控制器 | 名 | kòng zhì qì | controller | Contrôleur | controlador | Controlador | контроллер | 游戏机的控制器坏了。 | 空调的控制器可以调节温度。\n144 | 库仑 | 量 | kù lún | Coulomb | Coulomb | Coulomb | Coulomb | кулон | 库仑是电荷的单位。 | 这个电容器的电荷量是1库仑。\n145 | 快捷 | 形 | kuài jié | shortcut | Le raccourci | atalho | Acceso rápido | краткий путь | 移动支付的普及让支付过程更加快捷。 | 这条路线更快捷。\n146 | 蓝光 | 名 | lán guāng | Blue light | Lumière bleue | Luz azul | Luz azul | синий свет | 蓝光对眼睛有害。 | 蓝光有辐射。\n147 | 冷却 | 动 | lěng què | cooling | Se refroidir | arrefecimento | Enfriarse | охлаждение | 这杯水冷却了。 | 将水果和蔬菜放入冰箱进行冷却，可以延长它们的保鲜期。这个散热器的冷却效果很好。\n148 | 利用率 | 名 | lì yòng lǜ | Utilization rate | Taux d'utilisation | Taxa de utilização | Tasa de utilización | коэффициент использования | 提高利用率可以增加产量。 | 这块土地的利用率很高。\n149 | 连通 | 动 | lián tōng | connected | Liés à | ligado | Relacionado | соединенный | 这两个房间是连通的。 | 网络连通后可以共享资源。\n150 | 连续性 | 名 | lián xù xìng | Continuity | Continuité | Continuidade | Continuidad | непрерывность | 这个函数具有连续性。 | 连续性指图像是连续的。\n151 | 联网 | 动 | lián wǎng | networking | Réseau de relations humaines | rede | Red de relaciones interpersonales | сетевая работа | 手机联网后可以上网。 | 这台电脑没有联网。\n152 | 量子 | 名 | liàng zǐ | quantum | Quantique | quântico | Quantum | квант | 量子是能量的最小单位。 | 量子技术正在快速发展。\n153 | 量子力学 | 名 | liàng zǐ lì xué | quantum mechanics | Mécanique quantique | mecânica quântica | Mecánica cuántica | квантовая механика | 量子力学研究微观世界。 | 量子力学解释了原子的行为。\n154 | 流量 | 名 | liú liàng | flow | Flux | fluxo | Flujo | течь | 这个月的手机流量用完了。 | 河流的流量很大。\n155 | 流体 | 名 | liú tǐ | fluid | Liquide | fluido | Líquido | жидкость | 水和空气都是流体。 | 流体的流动可以用流线来表示。\n156 | 路径 | 名 | lù jìng | path | Le chemin | caminho | Camino | путь | 直线往往是最短路径。 | 他选择了不同的路径到达目的地。\n157 | 螺旋 | 名 | luó xuán | screw | Vis | parafuso | Tornillo | винт | 螺旋结构在自然界中很常见。 | DNA分子呈螺旋状。\n158 | 脉冲 | 名 | mò chōng | pulse | Le pouls | pulso | Pulso | импульс | 脉冲信号用于通信。 | 心跳可以产生脉冲。\n159 | 密钥 | 名 | mì yào | secret key | La clé | chave secreta | Clave | секретный ключ | 密钥可以加密数据。 | 这个保险箱需要密钥才能打开。\n160 | 描绘 | 动 | miáo huì | describe | Description | Descrever | Descripción | описывать | 画家描绘了一幅美丽的风景。 | 这篇文章描绘得很精彩。\n161 | 能耗 | 名 | néng hào | energy consumption | Consommation d'énergie | consumo de energia | Consumo de energía | потребление энергии | 这个冰箱的能耗很低。 | 节能可以降低能耗。\n162 | 凝固 | 动 | níng gù | freezing | Extrêmement froid | congelação | Extremadamente frío | замерзание | 水在0°会凝固成冰。 | 金属冷却后凝固成型。\n163 | 凝结 | 动 | níng jié | condensation | Condensation | condensação | Condensación | конденсация | 水蒸气遇冷会凝结成水滴。 | 露水是水蒸气凝结而成的。\n164 | 浓度 | 名 | nóng dù | concentration | Concentration | concentração | Concentración | концентрация | 这杯糖水的浓度很高。 | 溶液的浓度影响反应速度。\n165 | 浓缩 | 动 | nóng suō | concentration | Concentration | concentração | Concentración | концентрация | 浓缩果汁是通过蒸发水分制成的。 | 浓缩后留下的都是精华。\n166 | 排放 | 动 | pái fàng | discharge | Expulsion | descarga | Descarga | разряд | 工厂需要控制废气排放。 | 汽车尾气排放污染空气。\n167 | 排序 | 动 | pái xù | sort | Trier | sort | Ordenar | сортировка | 东西排序后方便查找。 | 文件可以按时间排序。\n168 | 判定 | 动 | pàn dìng | determine | Déterminer | determinar | Determinar | определять | 判定结果需要依据事实。 | 这个系统可以自动判定对错。\n169 | 抛物线 | 名 | pāo wù xiàn | parabola | Parabolique | parábola | Parábola | парабола | 抛物线是二次函数的图像。 | 物体向上抛出后落下会得到抛物线。\n170 | 配对 | 动 | pèi duì | pair | Une paire de | par | Un par | пара | 这双鞋子是配对的。 | 我们可以找到配对的卡片。\n171 | 频率 | 名 | pín lǜ | frequency | Fréquence | frequência | Frecuencia | частота | 这个信号的频率很高。 | 广播的频率是100.5MHz。\n172 | 平衡 | 形、动 | píng héng | balance | Équilibre | equilíbrio | Equilibrio | баланс | 天平保持平衡时，两边重量相等。 | 生活需要平衡工作和娱乐。\n173 | 平均值 | 名 | píng jun1 zhí | average value | Moyenne | valor médio | Media | среднее значение | 这组数据的平均值是10。 | 平均值比较客观。\n174 | 破译 | 动 | pò yì | Deciphering | Décrypter | Decifrar | Descifrado | дешифровка | 他成功破译了这封密信。 | 这个密码很难破译。\n175 | 葡萄糖 | 名 | pú táo táng | glucose | Glucose | glucose | Glucosa | глюкоза | 葡萄糖是单糖。 | 葡萄糖是人类的重要能源物质。\n176 | 气压 | 名 | qì yā | pressure | La pression | pressão | Presión | давление | 高气压时天气通常较好。 | 气压的变化会影响人的身体。\n177 | 契合 | 动、形 | qì hé | Fit | Convient pour | Ajustar | Adecuado | Подходит | 这两个零件契合得很好。 | 他们的想法高度契合。\n178 | 前沿 | 名 | qián yán | frontier | La frontière | fronteira | Frontera | пограничный | 这项技术处于行业前沿。 | 他在科研前沿取得了新突破。\n179 | 强弱 | 名 | qiáng ruò | intensity | Intensité | intensidade | Intensidad | интенсивность | 这个声音的强弱适中。 | 他能分辨出声音的强弱。\n180 | 切线 | 名 | qiē xiàn | tangent | Tangente | tangente | Corte | касательная | 切线与圆只有一个交点。 | 这条切线的斜率是2。\n181 | 氢能 | 名 | qīng néng | Hydrogen energy | Énergie hydrogène | Energia de hidrogénio | Energía de hidrógeno | водородная энергия | 氢能是一种清洁能源。 | 氢能汽车正在逐步推广。\n182 | 氢原子 | 名 | qīng yuán zǐ | hydrogen atom | Atomes d'hydrogène | átomo de hidrogénio | átomos de hidrógeno | водородный атом | 氢原子是宇宙中最简单的原子。 | 水分子由氢原子和氧原子组成。\n183 | 清晰 | 形 | qīng xī | clear | Clairement | claro | Claro | ясный, четкий, очищать | 这张照片很清晰。 | 他的思路很清晰。\n184 | 球面 | 名 | qiú miàn | sphere | La balle | esfera | Pelota | сфера | 地球表面近似于球面。 | 球面是曲面。\n185 | 球体 | 名 | qiú tǐ | sphere | La balle | esfera | Pelota | сфера | 足球是一个球体。 | 球体简称球。\n186 | 区间 | 名 | qū jiān | interval | Intervalle | intervalo | Intervalo | интервал | 这个函数在区间 [0,1] 上是增函数。 | 这个区间是减区间。\n187 | 曲率 | 名 | qǔ lǜ | curvature | Courbure | curvatura | Curvatura | кривизна | 圆的曲率是半径的倒数。 | 这条曲线的曲率在变化。\n188 | 曲面 | 名 | qǔ miàn | curved surface | Surface | superfície curva | Superficie curva | криволинейная поверхность | 圆柱的侧面是一个曲面。 | 曲面的形状影响其光学性质。\n189 | 趋向 | 动、名 | qū xiàng | trend | Les tendances | tendência | Tendencias | тенденция | 随着人工智能技术的不断进步，未来的科技趋向于更加智能化和自动化。 | 这种材料的性能趋向于稳定。\n190 | 趋于 | 动 | qū yú | Tend towards | Tendance à | Tender to | Tiende a | склоняться к | 温度趋于稳定。 | 这个系统趋于平衡状态。\n191 | 全球性 | 形 | quán qiú xìng | global | Mondiale | global | Global | глобальный | 气候变暖是一个全球性问题。 | 全球性合作才能解决问题。\n192 | 人体 | 名 | rén tǐ | human body | Le corps humain | corpo humano | Cuerpo humano | человеческое тело | 人体由多个系统组成。 | 人体需要足够的水分和营养。\n193 | 人造 | 形 | rén zào | artificial | Artificiel | artificial | Artificial | искусственный | 人造卫星在太空中运行。 | 人造皮革是一种环保材料。\n194 | 溶剂 | 名 | róng jì | solvent | Solvants | solvente | Disolvente | растворитель | 水是一种常见的溶剂。 | 溶剂可以溶解其他物质。\n195 | 若干 | 代 | ruò gàn | Several | Plusieurs | Vários | Varios | несколько | 他买了若干本书。 | 若干年后，这里会有很大变化。\n196 | 三维 | 名 | sān wéi | three-dimensional | En trois dimensions | tridimensional | Tridimensional | трехмерный | 三维电影有很强的立体感。 | 三维模型可以全方位展示。\n197 | 散射 | 动 | sàn shè | scattering | Sporadique | dispersão | Esporádico | рассеяние | 光会发生散射。 | 散射会发生丁达尔效应。\n198 | 扫描 | 动 | sǎo miáo | scanning | Scan | digitalização | Escaneo | сканирование | 请把这份文件扫描一下。 | 医院用扫描设备检查身体。 | 许多手机支持扫描功能，方便用户快速获取信息或进行支付。\n199 | 删除 | 动 | shān chú | delete | Supprimer | apagar | Eliminar | удалять | 他删除了不需要的文件。 | 错误的信息需要删除。\n200 | 上端 | 名 | shàng duān | Upper end | Extrémité supérieure | Extremidade superior | Arriba | верхний конец | 他站在梯子的上端。 | 这个物体的上端是圆形的。\n201 | 上限 | 名 | shàng xiàn | upper limit | Plafond | limite superior | Límite superior | верхний предел | 这个容器的容量已经达到了上限。 | 他的忍耐也到了上限。\n202 | 渗透 | 动 | shèn tòu | permeation | Pénétration, pénétration | permeação | Penetración, penetración | проникновение | 水慢慢渗透到泥土里。 | 外来文化逐渐渗透到当地社会，丰富了当地的文化内涵。\n203 | 剩余 | 动 | shèng yú | surplus | Surplus | excedente | Superávit | избыток | 吃完饭后，桌上还有些剩余的饭菜。 | 他用剩余的时间复习功课。\n204 | 石英 | 名 | shí yīng | quartz | Quartz | quartzo | Cuarzo | кварц | 这块石头里含有石英成分。 | 石英表走时很准。\n205 | 识别 | 动 | shí bié | recognition | Identification | reconhecimento | Identificación | распознавание | 她的红衣服很容易被识别。 | 这种动物很容易识别。 | 一般手机都有指纹识别和人脸识别。\n206 | 实时 | 副 | shí shí | real time | Temps réel | tempo real | Tiempo real | в реальном времени | 实时监控系统能随时掌握情况。 | 他喜欢实时更新自己的动态。\n207 | 实物 | 名 | shí wù | material object | Physique | objecto material | Objetos físicos | материальный объект | 物理老师用实物教学。 | 他把实物拿过来给大家看。\n208 | 实质 | 名 | shí zhì | essence | Essence | essência | Esencia | сущность | 这件事的实质是利益冲突。 | 不要只看表面，要看到问题的实质。\n209 | 矢量 | 名 | shǐ liàng | vector | Vecteur | vector | Vector | вектор | 矢量有大小有方向。 | 他学习了矢量的运算方法。\n210 | 收敛 | 动 | shōu liǎn | convergence | Convergence | convergência | Convergencia | сходимость | 这个算法是收敛的。 | 他收敛起笑容，变得严肃。\n211 | 受体 | 名 | shòu tǐ | receptor | Le récepteur | receptor | Receptores | рецептор | 药物通过与受体结合发挥作用。 | 通过检测受体的水平，医生可以为患者选择最适合的治疗药物。\n212 | 输送 | 动 | shū sòng | transport | Transport | transporte | Transporte | транспорт | 这条管道负责输送石油。 | 他负责输送这批东西。\n213 | 数量级 | 名 | shù liàng jí | Order of magnitude | Ordre de grandeur | Ordem de magnitude | Orden de magnitud | порядок величины | 10和100相差一个数量级。 | 他的收入和我的收入完全不在一个数量级。\n214 | 数列 | 名 | shù liè | series | Série | série | Serie | серия | 这个数列的每一项都比前一项多1。 | 数列有通项公式。\n215 | 瞬间 | 名 | shùn jiān | moment | Un instant | momento | Un momento | момент | 每个人都想留住烟花燃放的瞬间。 | 流星瞬间就不见了。\n216 | 瞬时 | 名 | shùn shí | instantaneous | Instantanée | instantâneo | Instantáneo | мгновенный | 瞬时速度是指物体在某一时刻的速度。 | 这个设备可以测量瞬时速度。\n217 | 搜索引擎 | 名 | sōu suǒ yǐn qíng | Search Engines | Moteur de recherche | Motores de Busca | Motor de búsqueda | поисковые системы | 我们可以通过搜索引擎找到很多信息。 | 谷歌是一个搜索引擎。\n218 | 酸性 | 名 | suān xìng | acidity | Acidité | acidez | Acidez | кислотность | 这种溶液呈酸性。 | 酸性土壤适合种植草莓。\n219 | 算法 | 名 | suàn fǎ | algorithm | Algorithme | algoritmo | Algoritmos | алгоритм | 这个算法可以快速计算出结果。 | 新的计算机算法可以提高效率。\n220 | 随机 | 副、形 | suí jī | random | Aléatoire | aleatório | Aleatorio | случайный | 他出现的概率是随机的。 | 他随机拿走了一个同学的课本。\n221 | 缩写 | 名、动 | suō xiě | abbreviation | Abréviations | abreviatura | Siglas | сокращение | 他的名字缩写是Z.Z.。 | 中国的缩写是PRC。\n222 | 碳化硅 | 名 | tàn huà guī | silicon carbide | Carbure de silicium | carboneto de silício | Carburo de silicio | кремний углерод | 碳化硅是一种硬度很高的材料。 | 这种碳化硅陶瓷耐高温。\n223 | 碳水化合物 | 名 | tàn shuǐ huà hé wù | carbohydrate | Hydrates de carbone | hidratos de carbono | Carbohidratos | углевод | 碳水化合物是人体的主要能量来源。 | 土豆富含碳水化合物。\n224 | 糖类 | 名 | táng lèi | Carbohydrates | Hydrates de carbone | Hidratos de carbono | Carbohidratos | углеводы | 糖类包括单糖、双糖和多糖。 | 苹果含有丰富的糖类。\n225 | 提取 | 动 | tí qǔ | extract | Extraction | extracto | Extracción | извлекать | 从文件中提取关键信息。 | 他提取了账户里的钱。 | 科学家从这种植物中提取了蛋白质。\n226 | 替换 | 动 | tì huàn | replace | Remplacement | substituir | Reemplazar | заменять | 用新课本替换旧课本。 | 替换这件旧衣服。\n227 | 添加 | 动 | tiān jiā | Add | Ajouter | Adicionar | Añadir | добавлять | 在汤里添加一些盐。 | 他给手机添加了一个新的功能。\n228 | 跳跃 | 动 | tiào yuè | jump | Le saut | Saltar | Salto | прыгать | 小孩在草地上跳跃。 | 人工智能技术近年来取得了巨大的跳跃式发展。\n229 | 透过 | 动、介 | tòu guò | transmission | Transmission | transmissão | Transmisión | передача | 透过窗户可以看到外面的风景。 | 透过表面看本质。\n230 | 透镜 | 名 | tòu jìng | lens | Lentilles | lente | Lente | линза | 这个透镜可以聚焦光线。 | 透镜分为凹透镜和凸透镜。\n231 | 图表 | 名 | tú biǎo | chart | Diagramme | gráfico | Gráfico | диаграмма | 这个图表清晰地展示了学生的成绩。 | 他用图表来说明内容。\n232 | 推论 | 动、名 | tuī lùn | inference | Inférence | inferência | Inferencia | вывод | 从这些数据可以得出一个推论。 | 他推论出的答案是错误的。\n233 | 褪色 | 动 | tuì sè | fade | Décoloration | desvanecer | Decoloración | затухать | 这件衣服开始褪色了。 | 墙上的旧照片已经褪色了。\n234 | 外部 | 名 | wài bù | external | Externe | externo | Externo | внешний | 这个装置的外部很坚固。 | 外部环境对实验结果有影响。\n235 | 外加 | 动 | wài jiā | additional | Supplémentaire | adicional | Adicional | дополнительный | 会议预计将持续两个小时，外加半小时的休息时间。 | 这个设备需要外加一个电源。\n236 | 外形 | 名 | wài xíng | appearance | Apparence | aparência | Apariencia | внешний вид | 这个产品的外形像一只兔子。 | 他喜欢外形简洁的家具。\n237 | 弯曲 | 形 | wān qǔ | bend | Courbe | dobrar | Doblar | изгибать | 这是一条弯曲的路，开车要小心。 | 他把铁丝弯曲成一个圆。\n238 | 万有引力 | 名 | wàn yǒu yǐn lì | Universal gravitation | Toute gravitation | Gravitação universal | Gravedad universal | всемирное тяготение | 万有引力使地球围绕太阳转动。 | 万有引力是牛顿发现的。\n239 | 微分 | 名 | wēi fèn | differential | Différentiel | diferencial | Diferenciado | дифференциал | 他正在学习微分的计算方法。 | 微分可以帮助我们研究函数。\n240 | 微分方程 | 名 | wēi fèn fāng chéng | differential equation | Équations différentielles | equação diferencial | Ecuación diferencial | дифференциальное уравнение | 这个问题可以用微分方程来描述。 | 解微分方程需要一定的数学基础。\n241 | 微观 | 形 | wēi guān | microcosmic | Microscopique | microcosmicos | Microscópico | микроскопический | 微观来看，物质是由分子和原子组成的。 | 微观研究细微的规律。\n242 | 微积分 | 名 | wēi jī fèn | calculus | Calcul | cálculo | Cálculo | математический анализ | 微积分是大学数学的重要课程。 | 微积分分为微分和积分。\n243 | 微量元素 | 名 | wēi liàng yuán sù | trace element | Éléments traces | oligoelemento | Oligoelementos | микроэлемент | 人体需要多种微量元素。 | 铁是一种微量元素。\n244 | 微生物 | 名 | wēi shēng wù | microorganism | Micro - organismes | microrganismo | Microorganismos | микроорганизм | 微生物有重要作用。 | 病毒属于微生物。\n245 | 文件夹 | 名 | wén jiàn jiá | folder | Pinces en papier | pasta | Clip de papel | папка | 我把文档都保存在同一个文件夹里。 | 在电脑上新建一个文件夹。\n246 | 无机物 | 名 | wú jī wù | Inorganic substances | Substances inorganiques | Substâncias inorgânicas | Sustancias inorgánicas | неорганические вещества | 无机物通常不含碳元素。 | 这种岩石主要由无机物组成。\n247 | 无穷大 | 名 | wú qióng dà | Infinity | L'infini | Infinito | Infinito | бесконечность | 无穷大表示没有上限。 | 他觉得宇宙是无穷大的。\n248 | 无条件 | 动 | wú tiáo jiàn | Unconditional | Sans condition | Incondicional | Incondicionalmente | безусловный | 他无条件地支持我的决定。 | 这种爱是无条件的。\n249 | 无线电 | 名 | wú xiàn diàn | radio | Radio | rádio | Radio | радио | 无线电波可以用于通信。 | 他喜欢听无线电广播。\n250 | 无序 | 形 | wú xù | disorder | Confusion | perturbação | Caos | беспорядок | 这个房间太乱了，完全无序。 | 无序需要整理才能变得有序。\n251 | 误差 | 名 | wù chà | error | Erreur | erro | Error | ошибка | 误差是不可避免的。 | 计算中的误差导致结果不准确。\n252 | 吸附 | 动 | xī fù | adsorption | Adsorption | adsorção | Adsorción | адсорбция | 活性炭可以吸附异味。 | 滤网可以吸附空气中的微小颗粒物，如灰尘、\n253 | 下限 | 名 | xià xiàn | lower limit | Limite inférieure | limite inferior | Límite inferior | нижний предел | 这个商品的价格有下限。 | 温度不能低于下限。\n254 | 弦 | 名 | xián | string | Une chaîne | string | Una cadena | строка | 吉他的弦需要定期更换。 | 余弦是一种三角函数。\n255 | 显示器 | 名 | xiǎn shì qì | display | Afficher | display | Mostrar | отображать | 电脑的显示器坏了。 | 这个显示器的分辨率很高。\n256 | 相对论 | 名 | xiàng duì lùn | Relativity theory | Théorie de la relativité | Teoria da relatividade | Relativismo | теория относительности | 爱因斯坦提出了相对论。 | 相对论解释了时间膨胀现象。\n257 | 向量 | 名 | xiàng liàng | vector | Vecteur | vector | Vector | вектор | 向量有大小和方向。 | 这个向量指向北方。\n258 | 像素 | 名 | xiàng sù | pixel | Pixels | pixel | Píxeles | пиксель | 图片由许多像素组成。 | 高像素的相机拍出的照片更清晰。\n259 | 芯片 | 名 | xīn piàn | chip | Les lacunes | chip | Brecha | чип | 手机里有芯片。 | 这个芯片性能很强。\n260 | 新建 | 动、形 | xīn jiàn | New | Nouveau | Novo | Nuevo | новый | 在电脑上新建一个文件。 | 公司决定新建一个项目。\n261 | 选定 | 动 | xuǎn dìng | selected | Sélection | seleccionado | Selección | выбранный | 我选定了这个方案。 | 他被选定参加比赛。\n262 | 寻常 | 形 | xún cháng | ordinary | Ordinaire | normal | Ordinario | обычный | 这是一个寻常的日子。 | 这种花很寻常。\n263 | 压缩 | 动 | yā suō | compress | Compression | comprimir | Compresión | сжимать | 这个软件可以压缩文件。 | 他把衣服压缩进箱子里。\n264 | 衍射 | 动 | yǎn shè | diffraction | Diffraction | difração | Difractivo | дифракция | 光的衍射现象很明显。 | 声音在障碍物后发生衍射。\n265 | 衍生物 | 名 | yǎn shēng wù | derivative | Dérivés | derivado | Derivados | производная | 这种衍生物很有用。 | 苯乙烯C₈H₈是苯C₆H₆的衍生物，广泛用于合成塑料和橡胶。\n266 | 验证 | 动 | yàn zhèng | validate | Vérification | validar | Verificación | валидировать | 实验验证了这个理论。 | 取钱需要验证身份。\n267 | 样品 | 名 | yàng pǐn | sample | Échantillons | amostra | Muestra | образец | 这个样品很典型。 | 他带了一个样品来展示。\n268 | 要点 | 名 | yào diǎn | main points | Points clés | pontos principais | Puntos principales | основные пункты | 抓住要点很重要。 | 这个报告的要点很清晰。\n269 | 乙醇 | 名 | yǐ chún | ethanol | Éthanol | etanol | Etanol | этиловый спирт | 这种溶液含有乙醇。 | 乙醇可以用来消毒。\n270 | 因素 | 名 | yīn sù | factor | Facteurs | factor | Factores | фактор | 在这个实验中，温度是一个关键因素。 | 多种因素影响了结果。\n271 | 引发 | 动 | yǐn fā | trigger | Déclencheur | gatilho | Desencadenar | триггер | 这个事件引发了讨论。 | 他的行为引发了争议。\n272 | 盈利 | 名 | yíng lì | profit | Bénéfices | lucro | Ganancias | прибыль | 这家公司的盈利很高。 | 他通过投资盈利了。\n273 | 映射 | 动 | yìng shè | mapping | Dessiner la carte de... | mapeamento | Dibuja un mapa de... | отображение | 数据映射到图表上。 | 在数学中，函数可以看作是定义域到值域的一种映射关系。\n274 | 硬盘 | 名 | yìng pán | Hard disk | Disque dur | Disco rígido | Disco duro | жесткий диск | 电脑的硬盘容量很大。 | 他更换了硬盘。\n275 | 优化 | 动 | yōu huà | optimization | Optimisation | optimização | Optimización | оптимизация | 这个程序需要优化。 | 对系统进行优化后提高了效率。\n276 | 优异 | 形 | yōu yì | excellent | Excellent | excelente | Excelente | отличный | 他成绩优异。 | 她最近的表现优异。\n277 | 有序 | 形 | yǒu xù | Ordered | Organisé de manière ordonnée | Ordenado | Organizado de manera ordenada | упорядоченный | 这个队伍很有序。 | 有序的环境让人舒适。\n278 | 原点 | 名 | yuán diǎn | origin | Origine | origem | Origen | начало | 坐标系的原点是(0,0)。 | 一切都从原点开始。\n279 | 原函数 | 名 | yuán hán shù | Primitive function | Fonction originale | Função primitiva | Función original | первообразная функция | 原函数是积分的结果。 | 做题需要考虑原函数的定义域。\n280 | 原子核 | 名 | yuán zǐ hé | Atomic nucleus | Noyau atomique | Núcleo atómico | Núcleo atómico | ядро атома | 原子核包含质子和中子。 | 原子核是原子的核心部分。\n281 | 圆柱 | 名 | yuán zhù | cylinder | Colonne | cilindro | Columna | цилиндр | 这个瓶子是圆柱形的。 | 圆柱的体积计算公式是πr²h。\n282 | 远程 | 形 | yuǎn chéng | remote | Distant | remoto | Lejano | удаленный | 他通过远程会议参与讨论。 | 远程教育采用线上的形式。\n283 | 运动学 | 名 | yùn dòng xué | kinematics | Kinésiologie | cinemática | Cinemática | кинематика | 运动学研究物体的运动规律。 | 这个软件可以模拟运动学过程。\n284 | 载体 | 名 | zǎi tǐ | carrier | Le porteur | transportador | Portador | носитель | 病毒以飞沫为载体传播。 | 语言是文化的载体。\n285 | 增量 | 名 | zēng liàng | increment | Incrémentation | incremento | Incremento | приращение | 数据每天都有增量。 | 算法通过增量更新提高效率。\n286 | 占用 | 动 | zhàn yòng | occupation | Travail | ocupação | Trabajo | занятие | 这个文件占用了大量空间。 | 他占用了很多时间来解释。\n287 | 真空 | 名 | zhēn kōng | vacuum | Le vide | vácuo | Vacío | вакуум | 真空环境中没有空气。 | 食物需要真空包装。\n288 | 正向 | 名 | zhèng xiàng | Forward | En avant | Avançar | Hacia adelante | вперед | 正向思维有助于解决问题。 | 这个力是正向的。\n289 | 直观 | 形 | zhí guān | Intuitive | Intuitive | Intuitivo | Intuitivo | интуитивный | 这个图表很直观。 | 直观的数据更容易理解。\n290 | 指令 | 动、名 | zhǐ lìng | instructions | Instructions | instruções | Instrucciones | инструкции | 她下了一个指令。 | 他按照指令完成了任务。\n291 | 质子 | 名 | zhì zǐ | proton | Protons | protão | Protones | протон | 质子带有正电荷。 | 原子核由质子和中子组成。\n292 | 中子 | 名 | zhōng zǐ | neutron | Neutrons | neutrão | Neutrones | нейтрон | 中子不带电。 | 中子呈电中性。\n293 | 周期性 | 名 | zhōu qī xìng | Periodic | Régulièrement | Periódico | Regular | периодический | 潮汐的周期性变化很明显。 | 正弦函数具有周期性。\n294 | 主机 | 名 | zhǔ jī | main engine | Moteur principal | motor principal | Motor principal | главный двигатель | 主机是计算机的核心部件。 | 网络的主机负责处理请求。\n295 | 转动 | 动 | zhuǎn dòng | turn | Tourner | turn | Girar | поворот | 风扇在快速转动。 | 机器不停转动。\n296 | 追踪 | 动 | zhuī zōng | track | La piste | faixa | órbita | траектория | 卫星可以追踪目标。 | 警察在追踪罪犯。\n297 | 准则 | 名 | zhǔn zé | criterion | Standard | critério | Criterios | критерий | 我们应该遵守准则。 | 公司的行为准则很严格。\n298 | 紫光 | 名 | zǐ guāng | Purple light | La lumière violette | Luz roxa | Luz púrpura | фиолетовый свет | 紫光波长为380～420nm。 | 紫光灯可以验钞。\n299 | 自变量 | 名 | zì biàn liàng | independent variable | Variables indépendantes | variável independente | Variables independientes | независимая переменная | 在函数中，x是自变量。 | 自变量的变化影响函数值。\n300 | 字节 | 名 | zì jiē | byte | Octets | byte | Bytes | байт | 字节是一个单位。 | 每个字节包含8位二进制数，即 1Byte = 8bit。\n301 | 最大值 | 名 | zuì dà zhí | Maximum value | Valeur maximale | Valor máximo | Valor máximo | максимум | 这个数列的最大值是100。 | 最大值与极大值可能不相等。\n302 | 遵从 | 动 | zūn cóng | follow | Suivre | seguir | Seguir | следовать | 他遵从医生的建议。 | 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Sheet: N~302\n\n^S | -Ne | ͋'` | b | | l | aYr | sYr | O | OS1 | OS2\n1 | JS[SO | T | bn do t | semiconductor | Semi - conducteurs | semicondutor | Semiconductores | ?>;C?@>2>4=8: | JS[SOGr^(u^l0 | Ex/fNyJS[SOPge0\n2 | O['` | T | bo m xng | confidentiality | Confidentialit | confidencialidade | Confidencialidad | :>=D845=F80;L=>ABL | O['`_͑ Nl2:g[0 | eNvO['`%N>1I5=85 | beSNǏQ~S0 | N6e0RNNNR[vbe0\n5 | YN | T0R | bi fn | backups | Sauvegarde | cpias de segurana | Respaldo | @575@2=K5 :>?88 | pencYNSN2bk"N1Y0 | bN[gYNeN0\n6 | Ppe | T | bi sh | multiple | Multiples | mltiplo | Mltiplo | <=>65AB25==K9 | 10/f5v Ppe0 | ُ*Npe/fSN*Npev N P Ppe0\n7 | ,g( | T | bn zh | essence | Essence | essncia | Esencia | ACI=>ABL | wv,g(/fx0 | wnv,g(0\n8 | kyr | ϑ | b t | Bits | Position | Bits | Posicin | 18BK | N*Nkyr/f{:gvW,gUSMO0 | 1 KB = 1024 Bytes 1 Byte = 8 bits\n9 | k

B=>H5=85 | $N*Npevk

48@>20=85 | xbeW[lbcbpeW[0 | ُ*N z^͑ex0 | @b gOo`lbc:NNۏ6Rb__ NO{:gYYt0ُ*NlbcǏ z1\/fx0\n11 | S'` | R | bin xng | denaturation | Dnaturation | desnaturao | Desnaturalizacin | 45=0BC@0F8O | ُyPge(Wؚ)n NOSuS'`0 | NxvzNˆ}v(vS'`Ǐ z0\n12 | h | R | bio sh | expression | Expression | expresso | Expresin | 2K@065=85 | npf0Wh`Ov`l0 | ُ*Nh NYQnx0\n13 | l | T | bM zhng | wavelength | Longueur d'onde | comprimento de onda | Longitud de onda | 4;8=0 2>;=K | IQvlQ[N[vr0 | ~rIQlg0\n14 | l|Na'` | T | bM l r xing xng | Wave particle duality | Dualit onde - grain | Dualidade das partculas de onda | Dualidad de ondas y partculas | 42>9AB25==>ABL 2>;=K 8 G0AB8FK | IQwQ gl|Na'`0 | l|Na'`/fϑP[Rf[vW,gi_0\n15 | Nnx['` | T | b qu dng xng | uncertainty | Hsitation | incerteza | Vacilacin | =5>?@545;5==>ABL | _YN`X[(W Nnx['`0 | *gegEQnN Nnx['`0\n16 | Sp | T | cn ko din | Reference point | Points de rfrence | Ponto de referncia | Punto de referencia | ">G:0 >BAG5B0 | Kmϑe[N*NSp0 | SpSNQ[irSOvЏRr`0\n17 | Spe | T | cn sh | parameter | Paramtres | parmetro | Parmetros | ?0@0<5B@ | eQcknxvSpeMbЏL z^0 | ُ*N[teSpe0\n18 | d\O|~ | T | co zu x tng | operating system | Systme d'exploitation | sistema operativo | Sistema operativo | >?5@0F8>==0O A8AB5<0 | 5u[ňd\O|~0 | ُ>kd\O|~_AmEu0\n19 | Nir | T | chn w | product | Produits | produto | Productos | ?@>4C:B | Sf[S^ONuevNir0 | ُ/f]NuNvNir0\n20 | [SO | T | cho do t | Superconductor | Supraconducteurs | Supercondutor | Superconductores | A25@E?@>2>4=8: | [SO(WNO)n N5u;:N0 | xvz[SO gRN0\n21 | Xl | T | cho shng bM | ultrasonic | Ultrasons | ultra- snico | Ultrasonido | C;LB@072C:>2>9 | XlSN(uN;Sf[0 | Xl(W4l-N Od^__0\n22 | bP | R | chng xing | imaging | Imagerie | imagiologia | Imgenes | 87>1@065=85 | ;SuǏbPb/gg wu`0 | ُSYbP_npf0\n23 | HTs | R | chng xin | present | Actuellement | presente | En la actualidad | =K=5H=89 | N(WO\U^ NHTsNpenc0 | ُ*NoNSNHTs3DVP0 | (W3IQ N w ُ*Nir(HTs~r0\n24 | RY | T | chk sh | initial | Commenc par | inicial | Iniciada | =0G0;L=K9 | RYn[bT1\SNO(uN0 | NU_N[vRYpenc0\n25 | YthV | T | ch l q | processor | Processeur | processador | Procesador | ?@>F5AA>@ | {:gvYthV^__0 | ُ>kYthV'`_}Y0\n26 | O[ | R | chun do | conduction | Conduction | conduo | Transmisin | ?@>2>48<>ABL | ё^\SN O[p0 | NSOSN O[5uAm0\n27 | OahV | T | chun gn q | sensor | Capteurs | sensor | Sensores | 40BG8: | OahVSNhKmsXSS0 | ُ*N OahV|^_ؚ0\n28 | 96 | T | chun b | Ships | Le navire | Navios | Buques | :>@01;8 | ُX96sS\w*0 | 96(Wwm NLv__s^3z0\n29 | x:W | T | c chng | magnetic field | Champ magntique | campo magntico | Campo magntico | <03=8B=>5 ?>;5 | xhTVX[(Wx:W0 | 0Wtvx:WObNuir0\n30 | PSBR | T | cu+ hu j | catalyzer | Catalyseur | catalisador | Catalizador | :0B0;870B>@ | PSBRSNRSf[S^0 | ُyPSBRHeg_}Y0\n31 | X[P | R | cn ch | storage | Stockage | armazenamento | Almacenamiento | E@0=5=85 | pencX[P(Wlxv-N0 | ُ*NYvX[P[ϑ_'Y0\n32 | USSO | T | dn t | Monomer | Monocoque | Monmero | Monmeros | <>=><5@ | ُyPge1uUSSOZT b0 | YNp/fNyUSSO0\n33 | US( | T | dn zh | simple substance | Monolithique | substncia simples | Calidad nica | ?@>AB>5 25I5AB2> | 'll/fNy8^vUS(0 | /fё^\US(0\n34 | ˆ}v( | T | dn bi zh | protein | Protines | protena | Protenas | 15;>: | ˆ}v(/fu}TvW@x0 | [rvY-N g'Yϑˆ}v(0\n35 | .lS | R | dn hu | nitridation | Nitruration | nitretao | Nitrificacin | =8B@8@>20=85 | .lSYtT Pgelx^XR0 | .lSPgeq0\n36 | [pe | T | do sh | derivative | Drivs | derivado | Derivados | ?@>872>4=0O | [pe/f_yRvW,gi_0 | Nck(W{Qpev[pe0\n37 | I{N | R | dng ji | equivalence | gale | equivalncia | Igualdad | M:2820;5=B=>ABL | I{N/fSTNvI{0 | Sf[-NI{NNbc0\n38 | NO | b_ | d+ s | low speed | Basse vitesse | baixa velocidade | Baja velocidad | =87:0O A:>@>ABL | f(WNOLvef[hQ0 | NOQ~q_TN N}^0\n39 | 5u:W | T | din chng | electric field | Champ lectrique | campo elctrico | Campo elctrico | M;5:B@8G5A:>5 ?>;5 | 5uw(W5u:W-NS0RRv\O(u0 | 5u:W:_^Q[N5uwvЏR0\n40 | 5uR:g | T | din dng j+ | motor | Moteur | motor | Motor | 42830B5;L | 5uR:gO:ghVЏl0 | ُS5uR:gHes_ؚ0\n41 | 5uIQ | T | din gung | electro-optical | lectro - optique | electro-pticos | Electroptico | M;5:B@>D>B> | 5u/fNy5uIQsa0 | N w0RNNS;87 | 5u4lSNNu"llT'll0 | 5uǏ z5u0\n44 | 5u | T | din nng | electric energy | nergie lectrique | energia elctrica | Energa elctrica | M;5:B@8G5A:0O M=5@38O | *Y3SNlS:N5u0 | 5u/fu;m-Nv͑n0\n45 | 5u[ | T | din rng | capacitance | Capacit | capacitncia | Condensadores | Q<:>ABL | 5u[vUSMO/flbF0 | 5u[(uCegh:y0\n46 | 5u[hV | T | din rng q | Capacitors | Condensateurs | Capacitores | Condensadores | :>=45=A0B>@K | 5u[hVSNX[P5uw0 | ُ*N5u[hV]~_cOWN0\n47 | SR | R | di ji | superposition | Superposition | superposio | Superposicin | =0;>65=85 | ;u[ǏrvSR R Q0N[vri_0 | Y*NR\O(u(W TN*NirSO Ne irSO@bSv;`R/fُNRvSR~g0\n48 | [INW | T | dng y y | Define Domain | Dfinir un domaine | Definir Domnio | Dominio definido | >1;0ABL >?@545;5=8O | Qpey=xv[INW:NR@b g[pe0 | nx[[INW/f㉘v,{Nek0\n49 | k'` | T | d xng | toxicity | Toxicit | toxicidade | Toxicidad | B>:A8G=>ABL | ُySf[ir(k'`_:_0 | (llNH gk'`0\n50 | S | R | d q | read | Lire | ler | Leer | G8B0BL | {:gSNSpenc0 | Nck(WS[vpe;8=>< | Nck(WN*NYy_e z0 | USy_~bYy_0\n54 | Su | R | f jio | fermentation | Fermentation | fermentao | Fermentar | D5@<5=B0F8O | uk̃SNۏLSu0 | SuǏ zONuR|0\n55 | Sce | R | f sn | Divergence | Divergent | Divergncia | Divergencia | @0AE>645=85 | IQ~OSce0 | QpeSNR:NSceQpeT6e[eQpe0\n56 | lR | T | f z | rule | Rgles | regra | Reglas | ?@028;> | 6qLu g[]vlR0 | >yO gNWYuX[vlR0\n57 | A~Y | b_ | fn duM | various | Toutes sortes de | vrios | Una variedad de | @07;8G=K5 | ^vFUTy{|A~Y0 | A~Yv bNN b0\n58 | SQpe | T | fn hn sh | Inverse function | Fonction inverse | Funo inversa | Funcin inversa | >1@0B=0O DC=:F8O | k*NQpe g[vSQpe0 | [peQpevSQpe:NcpeQpe0\n59 | Rs | T | fn bin l | resolving power | Rsolution | potncia de resoluo | Resolucin | @07@5H0NI0O A?>A>1=>ABL | ُS>f:yhVvRs_ؚ0 | Rsؚ VGrnpf0\n60 | RP[_ | T | fn z sh | Molecular formula | Formule molculaire | Frmula molecular | Frmula molecular | <>;5:C;O@=0O D>@<C;0 | 4lvRP[_/fH O0 | RP[_SNh:yir(v~b0\n61 | Rϑ | T | fn ling | component | Composantes | componente | Componentes | :><?>=5=B | ُ*NN(WN_-NvRϑ_͑0 | Nva_ gRϑ0\n62 | gRhV | T | f w q | server | Serveurs | servidor | Servidores | A5@25@ | lQSv gRhVX[PN'Yϑpenc0 | gRhVEe [Q~-Ne0\n63 | \ | R | f sh | radiation | Rayonnement | radiao | Radiacin | @0480F8O | ݄IQ g\'`0 | eO(u5ula2\0\n64 | N>k | R | f kun | payment | Paiement | pagamento | Pago | ?;0B56 | NǏKb:gN>k0 | (W6e0R'irTzsSN>k0\n65 | DR | R | f ji | additional | Supplmentaire | adicional | Adicional | 4>?>;=8B5;L=K9 | ُ*NoN gNNDRR0 | DRagNN~0\n66 | YT | R | f h | composite | Composite | compsito | Compuesto | :><?>78B=K9 | ُyPge/f1uYyir( YT bv0 | YTPgewQ gf}Yv'`0\n67 | v | R | f gi | cover | Couvercle | capa | Tapa | :@KH:0 | ُ*NOSTXvNte*Nf[!h0 | iirvNte^q\0\n68 | ؚ | T0b_ | go pn | high frequency | Haute frquence | alta frequncia | Alta frecuencia | 2KA>:0O G0AB>B0 | ؚ1\/fcsؚ0 | ُ*NYSNYtؚOS0\n69 | ؚHe | b_ | go xio | Efficient | Efficace | Eficiente | Eficiente | MDD5:B82=K9 | ُyel^8^ؚHe0 | ؚHeva`/fSN_[bNR0\n70 | y | R | g l | quarantine | isolement | quarentena | la Cuarentena | :0@0=B8= | uNۏLylu0 | yceSN2bkuk Od0\n71 | qQ/c | R | gng zhn | resonance | Rsonance | ressonncia | Resonancia | @57>=0=A | sv TMbqQ/c0 | qQ/cSN>e'YOS0\n72 | 0O{ | R | gk sun | estimate | Estimation | estimativa | Estimacin | >F5=:0 | N0O{NN NՋve0 | ُ*N0O{=0;L=0O 3@C??0 | W-OH /fNy[V0 | [VQ[N g:girvSf['`(0\n75 | IQ5uP[ | T | gung din z | photoelectron | Optolectronique | fotoeltrons | Optoelectrnica | D>B>M;5:B@>= | IQ5uP[/fNё^\hbIQoSQegv0 | IQ5uP[R/fIQ5uHe^vvcNir0\n76 | IQn | b_ | gung hu | smooth | Lisse | suave | Suave | 3;04:89 | ُ*NirSOhb^8^IQn0 | IQnvirSOS\IQ~f}Y0\n77 | IQ | T | gung nng | Photoenergy | nergie lumineuse | Fotoenergia | Energa luminosa | D>B>M=5@38O | iirSN8T6eIQ0 | *Y3g\IQlS:N5u0\n78 | IQ | T | gung s | Speed of light | Vitesse de la lumire | Velocidade da luz | Velocidad de la luz | A:>@>ABL A25B0 | IQ/f[[-Ng_v^0 | IQ(Wwzz-N/fR`[v0\n79 | IQf[ | T | gung xu | optics | Optique | ptica | ptica | >?B8:0 | Nck(WxvzIQf[NhV0 | IQf[StʑNIQvL:N0\n80 | IQP[ | T | gung z | photon | Photons | fton | Fotones | D>B>>= | IQP[/fIQvW,g|P[0 | IQP[wQ gϑTRϑ0\n81 | R_~l | T | gu+ n f | Induction method | Mthode inductive | Mtodo de induo | Induccin | <5B>4 8=4C:F88 | R_~l;`~[ĉ_0 | R_~l/fNyctel0\n82 | Qpe | T | hn sh | function | Fonctions | funo | Funcin | DC=:F8O | ُ*NQpevVP/fNagv~0 | Qpe g[INW0\n83 | q | T | hn | enthalpy | Enthalpie | entalpia | Entalpa | M=B0;L?8O | q/fpRf[-NvN*N͑i_0 | qSSN(ueg$ReS^vpHe^0\n84 | LR | T | hng li | ranks | Niveau | classificaes | Nivel | @0=38 | LR_SNBl㉿~'`e z~0 | LR_{ g_YlQ_0\n85 | *)Y | R | hng tin | aerospace | Arospatiale | aeroespacial | Aeroespacial | 0M@>:>A<8:0 | -NVv*)Yb/gS_N]'Yۏek0 | *)YXT(W*Yzz-NۏLNyf[[0\n86 | 8hZS | T | h j bin | nuclear fusion | Fusion nuclaire | fuso nuclear | Fusin nuclear | O45@=>5 A;8O=85 | 8hZS/f8hvNye_0 | 8hZSOʑ>eQ]'Yvϑ0\n87 | 8h | T | h nng | nuclear energy | nergie nuclaire | energia nuclear | Energa nuclear | O45@=0O M=5@38O | 8h/fNyؚHevn0 | 8hSN(uegS5u0\n88 | ўSO | T | hi t | Blackbody | Corps noir | Corpo Negro | Cuerpo negro | G5@=>5 B5;> | ўSO8T6e@b gv5ux\0 | ўSO(W N T)n^ N\ N TrvIQ0\n89 | ~IQ | T | hng gung | Red light | La lumire rouge | Luz vermelha | Luz roja | :@0A=K9 A25B | ~IQvlk݄IQ0 | ~IQvlg0\n90 | [‰ | b_ | hng gun | macroscopic | Macro | macroscpica | Macro | <0:@>A:>?8G5A:89 | [‰xvzteSOvSU\`Q0 | bNSNN[‰T_‰v҉^eg‰[0\n91 | S^ | T | hu d | thickness | paisseur | espessura | Espesor | B>;I8=0 | ~ _vS^S gQks|0 | ُ,gfNS^~5Ss|0\n92 | '_ | T | h zhng | arc length | Longueur d'arc | comprimento do arco | Longitud del arco | 4;8=0 4C38 | Wv'_NJS_TW_҉ gsQ0 | ُk'_v'_/f10Ss|0\n93 | NT | R | h lin | interconnection | Interconnect | interconexo | Interrelacionados | A>548=5=85 | 5uNKb:gǏQ~NT0 | NLuT0WvNNǏNTQNT0\n94 | ST | R | hu h | Chemosynthesis | Synthse chimique | Quimossntese | Sntesis qumica | xem>A8=B57 | 'llT"llSNSTub4l0 | ё^\T^ё^\ǏSTb_bSTir0\n95 | Sf[_ | T | hu xu sh | Chemical formula | Formule chimique | Frmula qumica | Frmula qumica | E8<8G5A:0O D>@<C;0 | 4lvSf[_/fH O0 | N'lSxvSf[_/fCO 0\n96 | ba | b_ | hun mn | slow | Lentement | lento | Lento | <54;5==K9 | [r,rL_ba0 | ُ*NSf[Ǐ z_ba0\n97 | mTir | T | hn h w | mixture | Mlange | mistura | Mezcla | A<5AL | u;m-N g_YmTir0 | zzl/flSOmTir0\n98 | ;mS | R | hu hu | activation | Dclencheur | activao | Desencadenar | 0:B820F8O | PSBR;mSS^0 | ؚ)nS;mSgNir(0\n99 | ;m'` | T0R | hu xng | activity | Activits | actividade | Actividades | 0:B82=>ABL | ُyPge;m'`_ؚ0 | ;m'`ؚvir(S^_0\n100 | '^ | T | hu b | currency | La monnaie | moeda | Moneda | 20;NB0 | Nl^/fbVv'^0 | CQ/fVE;N'^KNN0\n101 | :gh | T | j+ xi nng | mechanical energy | nergie mcanique | energia mecnica | Energa mecnica | <5E0=8G5A:0O M=5@38O | RTRKNTy:N:gh0 | :ghTRSNvNlS0\n102 | yR | T0R | j+ fn | integral | Complet | integral | Completo | 8=B53@0; | yR/f_yRvW,gi_KNN0 | 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| C?@>I0BL | {STf[ft0 | ُ*NlQ_SN{S0\n117 | x'` | b_ | jin xng | alkalinity | Alcaline | alcalinidade | Alcalino | I5;>G=>ABL | x'`nmvpH48G5A:89 | Nv]\O/fe'`v0 | ُagVe] e0\n119 | | T0R | jin g | interval | Intervalle | intervalo | Intervalo | 8=B5@20; | iirKNN[v0 | O[c g10Rv0\n120 | t+R | R | jin bi | distinguish | Diffrenciation | distinguir | Distincin | >B;8G0BL | bNf[Ot+RwGP0 | PHB:@K20BL | ُ*NEeNc:yNm;RvSt0 | Nc:yNN`vwv0\n124 | p | T | ji din | node | Noeuds | n | Nodo | C75; | k*N{:g/fN*Np0 | yvvsQ.p]~[b0\n125 | ~g_ | T | ji gu sh | Structural formula | Le style structurel | Frmula estrutural | Estructural | AB@C:BC@=0O D>@<C;0 | g:girv~g_h:yvQRP[~g0 | ~g_U\:yNir(vSP[cR0\n126 | ~vf | T0R | ji j+ng | crystallization | Cristallisation | cristalizao | Cristalizacin | :@8AB0;;870F8O | qTnmM)nTO~vf0 | S~vf/f~vfvNye_0\n127 | N( | T | ji zh | medium | Moyen | mdio | Moderado | A@540 | IQ(W N TN(-Nv Od^ N T0 | 4l/fNy8^vN(0\n128 | PR | R | ji zh | With the help of | Avec l'aide de | Com a 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| ?>;8<5@ | ZTir/f1uYUSSO~bv0 | QXe/fNy8^vZTir0\n137 | ZYNp | T | j y x+ | polyethylene | Polythylne | polietileno | Polietileno | ?>;8MB8;5= | ZYNp(uN6R\OQXe0 | ZYNpek0\n138 | ~SO | T | ju yun t | insulator | Matriaux d'isolation thermique | isolador | Material de aislamiento trmico | 87>;OB>@ | aj/fo}Yv~SO0 | ~SOSN2bk5uAmǏ0\n139 | | | T | k l | granule | Particules | grnulo | Partculas | 3@0=C;0 | lP[/f1uY\|~bv0 | ُyo/f|rv0\n140 | Sc | b_ | k kng | controlled | Limite | controlado | Restringido | :>=B@>;8@C5<K9 | ُ*NY/fScv0 | 8hZS NSc0\n141 | S'` | T | k nng xng | possibility | Peut - tre | possibilidade | Posible | 2>7<>6=>ABL | f)Y N薄vS'`_'Y0 | ُ*NeHh gbRvS'`0\n142 | S | b_ | k n | reversible | Rversible | reversvel | Reversible | >1@0B=K9 | 4lvQVTS/fSǏ z0 | ُ*NSf[S^/fSv0\n143 | c6RhV | T | kng zh q | controller | Contrleur | controlador | Controlador | :>=B@>;;5@ | 8nb:gvc6RhVOWN0 | zzvc6RhVSN)n^0\n144 | ^N | ϑ | k ln | Coulomb | Coulomb | Coulomb | Coulomb | :C;>= | ^N/f5uwvUSMO0 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convergncia | Convergencia | AE>48<>ABL | ُ*N{l/f6e[ev0 | N6e[ew{[ S_%N0\n211 | SSO | T | shu t | receptor | Le rcepteur | receptor | Receptores | @5F5?B>@ | oirǏNSSO~TS%c\O(u0 | ǏhKmSSOv4ls^ ;SuSN:N` bgTvluoir0\n212 |  | R | shk sng | transport | Transport | transporte | Transporte | B@0=A?>@B | ُag{S#wl0 | N#ُybN0\n213 | peϑ~ | T | sh ling j | Order of magnitude | Ordre de grandeur | Ordem de magnitude | Orden de magnitud | ?>@O4>: 25;8G8=K | 10T100v]N*Npeϑ~0 | Nv6eeQTbv6eeQ[hQ N(WN*Npeϑ~0\n214 | peR | T | sh li | series | Srie | srie | Serie | A5@8O | ُ*NpeRvkNykMRNyY10 | peR gylQ_0\n215 | w | T | shn jin | moment | Un instant | momento | Un momento | <><5=B | k*NN`YuOOpq>evw0 | Amfw1\ NN0\n216 | we | T | shn sh | instantaneous | Instantane | instantneo | Instantneo | <3=>25==K9 | we^/fcirSO(WgNe;Rv^0 | ُ*NYSNKmϑwe^0\n217 | d"}_d | T | sMu su yn qng | Search Engines | Moteur de recherche | Motores de Busca | Motor de bsqueda | ?>8A:>2K5 A8AB5<K | bNSNǏd"}_d~b0R_YOo`0 | 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CHINA\nInformation Sciences\nFebruary 2025, Vol. 68, Iss. 2, 121101:1–121101: 44\nh\nttps://doi.org/10.1007/s11432-024-4222-0\nc⃝Science China Press 2025 info.scichina.com link.springer.com.REVIEW.\nThe rise and potential of large language model based\na\ngents: a survey\nZhiheng XI1†, Wenxiang CHEN1†, Xin GUO1†, Wei HE1†, Yiwen DING1†,\nBoyang HONG1†, Ming ZHANG1†, Junzhe WANG1†, Senjie JIN1†, Enyu ZHOU1†,\nRui ZHENG1, Xiaoran FAN1, Xiao WANG1, Limao XIONG1, Yuhao ZHOU1,\nWeiran WANG2, Changhao JIANG1, Yicheng ZOU1, Xiangyang LIU1, Zhangyue YIN1,\nShihan DOU1, Rongxiang WENG4, Wenjuan QIN2, Yongyan ZHENG2,\nXipeng QIU1, Xuanjing HUANG1, Qi ZHANG1*& Tao GUI3*\n1School of Computer Science, Fudan University, Shanghai 200441, China\n2College of Foreign Languages and Literature, Fudan University, Shanghai 200433, China\n3Institute of Modern Languages and Linguistics, Fudan University, Shanghai 200433, China\n4Meituan, Beijing 100102, China\nReceived 7 September 2024/Revised 25 October 2024/Accepted 11 November 2024/Published online 17 January 2025\nAbstract For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds human intelligence.\nAI agents, which are artificial entities capable of sensing the environment, making decisions, and taking actions, are seen as a\nmeans to achieve this goal. Extensive efforts have been made to develop AI agents, with a primary focus on refining algorithms\nor training strategies to enhance specific skills or particular task performance. The field, however, lacks a sufficiently general\nand powerful model to serve as a foundation for building general agents adaptable to diverse scenarios. With their versatile\ncapabilities, large language models (LLMs) pave a promising path for the development of general AI agents, and substantial\nprogress has been made in the realm of LLM-based agents. In this article, we conduct a comprehensive survey on LLM-based\nagents, covering their construction frameworks, application scenarios, and the exploration of societies built upon LLM-based\nagents. We also conclude some potential future directions and open problems in this flourishing field.\nKeywords natural language processing, large language models, LLM-based agents, AI agents, agent society\nCitation Xi Z H, Chen W X, Guo X, et al. The rise and potential of large language model based agents: a survey. Sci China\nInf Sci, 2025, 68(2): 121101, https://doi.org/10.1007/s11432-024-4222-0\n1 Introduction\nOne core research area in artificial intelligence (AI) is to develop agents that possess human-level in-\ntelligence, and potentially even exceed it [1 ]. The concept of agent originates in philosophy, where it\nd\nescribes entities possessing desires, beliefs, intentions, and the ability to take actions [2 ]. In AI research,\nt\nhe term agent refers to an artificial entity capable of perceiving its surroundings using sensors, making\ndecisions, and then taking actions in response using actuators [1 ,3]. The development and advancement\no\nf agents have been central within the AI community for a long time [1 ,4]. Moreover, AI agents are now\nr\necognized as a pivotal stride towards achieving artificial general intelligence (AGI)1), as they encompass\nthe potential for a wide range of intelligent activities [3 ,5,6].\nF\nrom the mid-20th century, significant strides were made in designing and developing AI agents [7 –12].\nH\nowever,theseeffortshavepredominantlyfocused onenhancingspecificskills(suchassymbolicreasoning\nor quick responsiveness) or mastering particular tasks (such as Go or Chess) [13 –15]. Achieving broad\na\ndaptability across varied scenarios remained elusive. Moreover, previous studies mostly emphasized the\ndesign of algorithms and training strategies, overlooking the development of the model’s inherent general\n*Corresponding author (email: qz@fudan.edu.cn, tgui@fudan.edu.cn)\n†These authors contributed equally to this work.\n1) Also known as Strong AI.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:2\nFigure 1 (Color online) Roadmap of this survey.\nabilities [16 ,17]. Actually, enhancing these inherent abilities of the model is pivotal fo r advancing the\nagent, and the field is in need of a powerful and versatile model to serve as the foundation for agent\nsystems.\nThe emergence of large language models (LLMs) has brought a glimmer of hope for the further devel-\nopment of agents [18 –20], and significant progress has been made by researchers in the filed [16 ,21–23].\nT\nhis is attributed to the remarkable capabilities demonstrated by LLMs, including natural language\ninteraction, knowledge acquisition, instruction following, generalization, reasoning, planning, and tool-\nusing. These advantages have earned LLMs the designation of sparks for AGI [24 ], making them highly\nd\nesirable for building general agents [16 ]. And the area of LLM-based agents has emerged as one of the\nm\nost promising fields in AI research. The academic and industrial communities have invested significant\nresearchand development efforts in this field. However, despite the progress, this emerging field still faces\nnumerous crucial issues that require further exploration and resolution, e.g., the architectural design of\nLLM-based agents and their application domains. Therefore, it is necessary to review the origins and\ndevelopment of this field, summarize current research achievements, and look ahead to provide a clearer\nunderstanding of LLM-based agents and deeper insights into their future development.\nIn this article, we present a comprehensive and systematic survey of LLM-based agents, attempting\nto explore several critical research problems within the field and prospective avenues in this burgeoning\nfield (Figure 1demonstrates the roadmap of this survey). Before delving into the research problems, we\nfirst discuss the background information (Section 2), including the origin, definition, and development\no\nf AI agents, as well as why LLMs are suitable as the foundation for AI agents. The first research\nproblem is the design and construction of LLM-based agents (Section 3). Based on the definition of AI\na\ngents, we propose a conceptual framework with three key components: brain, perception, and action.\nDifferent downstream applications can tailor this framework according to their specific requirements.\nThe second research problem is the application scenarios of LLM-based agents (Section 4). We discuss\nt\nhe various paradigms such as single agent, multi-agent, and human-in-the-loop, and their respective\napplicable scenarios. The third research problem is agent society (Section 5). We discuss the components\na\nnd operational mechanisms of an agent society, as well as its insights into human society. Finally, we\ndiscuss a series of open problems and future directions in this field (Section 6), e.g., the mutual benefits\nb\netween LLM research and agent research, evaluation for LLM-based agents, potential risks of LLM-\nbased agents, and scaling up the number of agents. Finally, we present an overview and summary of the\nentire article (Section 7).\n2 Background\nIn this section, we provide crucial background information to lay the groundwork for the subsequent\ncontent. We first discuss the origin of AI agents in philosophy and its definition (Subsection 2.1). Subse-\nq\nuently, we review the development of AI agentsthrough the lens oftechnologicaltrends (Subsection 2.2).\nF\ninally, we introduce the key characteristics of agents and demonstrate why LLMs are suitable to serve\nas the foundation of AI agents (Subsection 2.3).\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:3\n2.1 Origin of AI agent\n“Agent”is a concept with a long history that spans acrossvariousfields. It is derivedfrom a philosophical\norigin, triggering a discussion on whether artificial products can possess agency in a philosophical sense.\nThen, related concepts were introduced into the field of AI, forming the basis of AI agents.\nAgent in philosophy. The initial idea of an “agent” has a philosophical origin, with influential\nthinkers like Aristotle and Hume, contributing to its conceptualization [2 ]. In a general sense, an “agent”\nis\nan entity with the capacity to act, and the term “agency” denotes the exercise or manifestation of\nthis capacity [2 ]. In a narrow sense, “agency” is usually used to refer to the perfor mance of intentional\nactions; and correspondingly, “agent” denotes entities that possess desires, beliefs, intentions, and the\nability to act [25 –28]. Importantly, agents encompass not only individual human beings bu t also other\nentities in both the physical and virtual world.\nIntroduction of agents into AI. InAI, anagentisdefined asanartificialentity capableofperceiving\nits environment, making decisions, and taking actions using sensors and actuators [1 ,3]. As Wooldridge\ne\nt al. [3] stated that we can define AI by saying that it is a subfield of compute r science that aims to\ndesign and build computer-based agents that exhibit aspects of intelligent behavior. So we can treat\n“agent” as a central concept in AI. When the concept of agent is introduced into AI, its meaning evolves.\nIn philosophy, an agent can be a human, an animal, or even a concept or entity with autonomy [2 ].\nH\nowever, in the field of AI, an agent is a computational entity [3 ,29]. Since it is difficult to determine\nif\nthey possess internal desires or consciousness, many AI researchers, including Alan Turing, suggest\ntemporarily setting aside the question of whether an agent is “actually” thinking or literally possesses a\n“mind” [30 ]. Instead, researchers employ other attributes to help describe a n agent, such as properties\nof autonomy, reactivity, pro-activeness, and social ability [3 ,31].\nI\nt might come as a surprise that researchers within the mainstream AI community devoted relatively\nminimal attention to concepts related to agents until the mid to late 1980s. Nevertheless, there has been\na significant surge of interest in this topic within the realms of computer science and artificial intelligence\ncommunities since then [32 –35].\n2\n.2 Technological trends in agent research\nThe evolution of AI agents has undergone several main stages, and here we take the lens of technological\ntrends to review its development briefly.\nSymbolicagents. IntheearlystagesofAIresearch,thepredominantapproachutilizedissymbolicAI,\ncharacterized by its reliance on symbolic logic [36 ,37]. This approach employs logical rules and symbolic\nr\nepresentations to encapsulate knowledge and facilitate reasoning processes. Early AI agents are built\nbased on this approach [38 ], and they primarily focused on two problems: the transduction prob lem and\nthe representation/reasoning problem [39 ]. These agents aim to emulate human thinking patterns. They\np\nossess explicit and interpretable reasoning frameworks, and due to their symbolic nature, they exhibit a\nhigh degreeofexpressivecapability [7 ,8,40]. Aclassicexampleofthis approachis knowledge-basedexpert\ns\nystems. However, symbolic agents face limitations in handling uncertainty and large-scale real-world\nproblems [13 ,14]. Additionally, due to the intricacies of symbolic reasoning algorithms, it is challenging\nto find an efficient algorithm capable of producing meaningful results within a finite timeframe [14 ,41].\nR\neactive agents. Different from symbolic agents, reactive agents do not use complex symbolic\nreasoning. Instead, they primarily focus on the interaction between the agent and its environment,\nemphasizing quick and real-time responses [9 ,10,14,42,43]. These agents are mainly based on a sense-act\nlo\nop, efficiently perceiving and reacting to the environment. The design of such agents prioritizes direct\ninput-output mappings rather than intricate reasoning and symbolic operations [34 ]. However, reactive\na\ngents also have limitations. They typically require fewer computational resources, enabling quicker\nresponses, but they might lack complex higher-level decision-making and planning capabilities.\nReinforcement learning-based agents. With the improvement of computational capabilities and\ndata availability, along with a growing interest in simulating interactions between intelligent agents and\ntheir environments, researchers have begun to utilize reinforcement learning methods to train agents for\ntackling more challenging and complex tasks [11 ,12,44,45]. The primary concern in this field is how to\ne\nnable agents to learn through interactions with their environments, enabling them to achieve maximum\ncumulative rewards in specific tasks [15 ]. Initially, reinforcement learning (RL) agents are primarily\nb\nased on fundamental techniques such as policy search and value function optimization, exemplified by\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:4\nQ-learning [46 ] and SARSA [47 ]. With the rise of deep learning, the integration of deep neural netwo rks\nand reinforcement learning, known as deep reinforcement learning (DRL), has emerged [48 ,49]. This\na\nllows agents to learn intricate policies from high-dimensional inputs, leading to numerous significant\naccomplishments like AlphaGo [50 ] and DQN [51 ]. The advantage of this approach lies in its capacity\nt\no enable agents to autonomously learn in unknown environments, without explicit human intervention.\nThis allows for its wide application in an array of domains, from gaming to robot control and beyond.\nNonetheless, reinforcement learning faces challenges including long training times, low sample efficiency,\nand stability concerns, particularly when applied in complex real-world environments [15 ].\nA\ngents with transfer learning and meta learning. Traditionally, training a reinforcement learn-\ning agent requires huge sample sizes and long training time, and lacks generalization capability [52 –56].\nC\nonsequently, researchersintroducetransferlearningtoexpedite anagent’slearningon newtasks[57 –59].\nT\nransfer learning reduces the burden of training on new tasks and facilitates the sharing and migration of\nknowledge across different tasks, thereby enhancing learning efficiency, performance, and generalization\ncapabilities. Furthermore, meta learning has also been introduced to develop agents [60 –64]. Meta learn-\nin\ng focuses on learning how to learn, enabling an agent to swiftly infer optimal policies for new tasks from\na small number of samples [65 ]. Such an agent, when confronted with a new task, can rapidly adjus t its\nlearning approach by leveraging acquired general knowledge and policies, consequently reducing the re-\nliance on a large volume of samples. However, when there exist significant disparities between source and\ntarget tasks, the effectiveness of transfer learning might fall short of expectations and negative transfer\nmight occur [66 ,67]. Additionally, the substantial amount of pre-training and large samp le sizes required\nby meta learning make it hard to establish a universal learning policy [61 ,68].\nL\narge language model-based agents. As LLMs have demonstrated impressive emergent capabili-\nties and have gained immense popularity [18 –20,69], researchers have started to leverage these models to\nc\nonstruct AI agents, known as LLM-based agents [16 ,21,22,70]. Specifically, they employ LLMs as the\nc\nore component of the brain or controller of these agents and expand their perceptual and action space\nthrough strategies such as multimodal perception and tool utilization [71 –75]. These LLM-based agents\nc\nan exhibit reasoning and planning abilities comparableto symbolic agents through techniques like chain-\nof-thought (CoT) and problem decomposition [76 –82]. They can also acquire interactive capabilities with\nt\nhe environment, akin to reactive agents, by learning from feedback and performing new actions [83 –85].\nS\nimilarly, LLMs undergo pre-training on large-scale corpora and demonstrate the capacity for few-shot\nand zero-shot generalization, allowing for seamless transfer between tasks without the need to update\nparameters [69 ,86–88]. LLM-based agents have been applied to various real-world scenario s, such as\nsoftware development [89 ,90] and scientific research [91 ]. Due to their natural language comprehension\na\nnd generation capabilities, they can interact with each other seamlessly, giving rise to collaboration and\ncompetition among multiple agents [89 ,90,92,93]. Furthermore, some studies suggested that allowing\nm\nultiple agents to coexist can lead to the emergence of social phenomena [16 ].\n2\n.3 Why is LLM suitable as the foundation of agent?\nAs mentioned before, researchers have introduced several properties to help describe and define agents\nin the field of AI. In this section, we will discuss the key properties, elucidate their relevance to LLMs,\nand thereby expound on why LLMs are highly suited to serve as the foundation of AI agents.\nAutonomy. Autonomy means that an agent operates without direct intervention from humans or\nothers and possesses a degree of control over its actions and internal states [3 ,94]. This implies that an\na\ngent should not only possess the capability to follow explicit human instructions for task completion\nbut also exhibit the capacity to initiate and execute actions independently. LLMs can demonstrate\na form of autonomy through their ability to generate human-like text, engage in conversations, and\nperform various tasks without detailed step-by-step instructions [95 ,96]. Moreover, they can dynamically\na\ndjust their outputs based on environmental input, reflecting a degree of adaptive autonomy [17 ,21,85].\nF\nurthermore, they can showcase autonomy through exhibiting creativity like coming up with novel ideas,\nstories, or solutions that have not been explicitly programmed into them [97 ,98]. This implies a certain\nle\nvel of self-directed exploration and decision-making. Applications like Auto-GPT [95 ] exemplify the\ns\nignificant potential of LLMs in constructing autonomous agents. Simply by providing them with a task\nand a set of available tools, they can autonomously formulate plans and execute them to achieve the\nultimate goal.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:5\nReactivity. Reactivity in an agent refers to its ability to respond rapidly to immediate changes and\nstimuli in its environment [31 ]. This implies that the agent can perceive alterations in its surrounding s\nand promptly take appropriate actions. Traditionally, the perceptual space of language models has been\nconfined to textual inputs, while the action space has been limited to textual outputs. Recently, advances\nin multimodal fusion techniques expand their perceptual space to rapidly process visual and auditory\ninformation from the environments [19 ,99,100]. Similarly, it is also feasible to expand the action space\no\nf LLMs through embodiment techniques [101 ,102] and tool usage [73 ,75]. These advancements enable\nL\nLMs to effectively interact with the real-world physical environment and carry out tasks within it. One\nmajor challenge is that LLM-based agents, when performing non-textual actions, require an intermediate\nstep of generating thoughts or formulating tool usage in the textual form before eventually translating\nthem into concrete actions. This intermediary process consumes time and reduces the response speed.\nHowever, this is analogous to human behavioral patterns, where the principle of “think before you act”\nis observed [103 ,104].\nP\nro-activeness. Pro-activeness denotes that agents do not merely react to their environments; they\npossess the capacity to display goal-oriented actions by proactively taking the initiative [31 ]. This prop-\ne\nrty emphasizes that agents can reason, make plans, and take proactive measures in their actions to\nachieve specific goals or adapt to environmental changes. Although intuitively the paradigm of next\ntoken prediction in LLMs may not possess intention or desire, research has shown that they can implic-\nitly generate representations of these states and guide the model’s inference process [105 –107]. LLMs\nh\nave demonstrated a strong capacity for generalized reasoning and planning. By prompting LLMs with\ninstructions like “let’s think step by step”, we can elicit their reasoningabilities, such as logicaland math-\nematical reasoning [76 –78]. Similarly, LLMs have shown the emergent ability of planning in forms of g oal\nreformulation [80 ,108], task decomposition [79 ,109], and adjusting plans in response to environmental\nc\nhanges [81 ,110].\nS\nocial ability. Social ability refers to an agent’s capacity to interact with other agents, including\nhumans, using agent-communication languages [111 ]. Large language models exhibit strong natural\nla\nnguage interaction abilities like comprehension and generation [17 ,112,113]. Compared to structured\nla\nnguages or other communication protocols, such capability enables them to interact with other mod-\nels or humans in an interpretable manner, laying the foundation for the social ability for LLM-based\nagents [16 ,89]. Many researchers have demonstrated that LLM-based agents c an enhance task perfor-\nmance through social behaviors such as collaboration and competition [89 ,92,114,115]. By inputting\ns\npecific prompts, LLMs can also play different roles, thereby simulating the social division of labor in\nthe real world [90 ]. Furthermore, when we place multiple agents with distinct identities int o a society,\nemergent social phenomena can be observed [16 ].\n3\nBirth of an agent: construction of LLM-based agents\nThe principle of “Survival of the Fittest” [116 ] highlights the need for cognitive abilities to adapt to the\ne\nxternal environment and respond to changes for individual survival. Inspired by this and the definition\nof AI agents, we present a general conceptual framework of an LLM-based agent composed of three key\ncomponents: brain, perception, and action (see Figure 2). We first describe the structure and working\nm\nechanism of the “brain”, which is the cognitive core of an AI agent (Subsection 3.1). It not only stores\nk\nnowledge and memories but also undertakes indispensable functions like decision-making, exhibiting the\nintelligence of an agent. Next, we introduce the perception module (Subsection 3.2). Its core purpose is\nt\no broaden the agent’s perception space from a text-only domain to a multimodal sphere, which equips\nthe agent to grasp and utilize information from its surroundings more effectively. Finally, we present the\naction module designed to expand the action space of an agent (Subsection 3.3). Specifically, the agent\nis\nempowered to adapt to environmental changes, provide feedbacks, and even influence and mold the\nenvironment.\nThe framework can be tailored for different application scenarios; i.e., not every specific component\nwill be used in all studies. In general, agents operate in the following workflow: First, the perception\nmodule, correspondingtohumansensorysystems, perceiveschangesin theexternalenvironmentandthen\nconverts multimodal information into an understandable representation for the agent. Subsequently, the\nbrain module, serving as the control center, engages in activities such as thinking, decision-making, and\noperations with storage including memory and knowledge. Finally, the action module, corresponding\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:6\nFigure 2 (Color online) Conceptual framework of an LLM-based agent with three components: brain, perception, and action.\nServing as the controller, the brain module undertakes basic tasks like memorizing, reasoning, and planning. The perceptionmodule\nperceives and processes multimodal information from the external environment, and the action module carries out the execution\nand interacts with the surroundings. Here is an example that illustrates the workflow: When a human asks whether it will rain,\nthe perception module converts the instruction into a specific representation. Then the brain module begins to reason according to\nthe current weather and the weather reports on the internet. Finally, the action module responds and hands the umbrella to the\nhuman. By repeating the above process, the LLM-based agent can continuously get feedback and interact with the environment.\nto human limbs, carries out the execution and leaves an impact on the surroundings. By repeating the\nabove process, an agent can continuously get feedback and interact with the environment.\n3.1 Brain\nThe human brain is a sophisticated structure comprised of a vast number of interconnected neurons,\ncapable of processing complex information, generating diverse thoughts, controlling behaviors, and even\ncreating art and culture [117 ]. Much like humans, the brain component serves as the central cont roller\nof an AI agent. In our framework, the brain module is primarily composed of an LLM.\nAfter receiving the information processed by the perception module, the brain module first turns to\nstorage, retrieving in knowledge (Subsection 3.1.1) and recalling from memory (Subsection 3.1.2). These\no\nutcomes aid the agent in planing, reasoning, and making decisions (Subsection 3.1.3). Additionally, the\nb\nrainmodulemaymemorizetheagent’spastobservations,thoughts,andactionsintheformofsummaries,\nvectors, or other data structures. Meanwhile, it can also update the knowledge such as common sense\nand domain knowledge for future use. The LLM-based agent may also adapt to unfamiliar scenarios with\nits inherent generalization and transferability (Subsection 3.1.4). In the subsequent sections, we delve\nin\nto a detailed exploration of these extraordinary facets of the brain module.\n3.1.1Knowledge\nLLMs aretypically imbued with large-scaledata throughsemi-supervised learningduringthe pre-training\nphase [118 ,119]. They can encode a wide range of knowledge into their parameters an d respond correctly\nto various types of queries after pre-training [120 ]. Furthermore, the knowledge can assist LLM-based\na\ngents in making informed decisions [121 ]. Such knowledge can be broadly categorized into the following\nt\nypes.\n•Commonsense knowledge. Commonsense knowledge [122 –124] refers to general world facts that\na\nre typically taught to most individuals at an early age. For example, people commonly know that\nmedicine is used for curing diseases, and umbrellas are used to protect against rain. Such information is\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:7\nusually not explicitly mentioned in the context. Therefore, models lacking the corresponding common-\nsense knowledge may fail to grasp or misinterpret the intended meaning [125 ]. Similarly, agents without\nc\nommonsense knowledge may make incorrect decisions, such as not bringing an umbrella when it rains\nheavily.\n•Professional domain knowledge. Professional domain knowledge refers to the knowledge asso-\nciated with a specific domain like programming [124 ,126,127], mathematics [128 ], medicine [129 ], etc.\nI\nt is essential for models to effectively solve problems within a particular domain [130 ]. For example,\nm\nodels designed to perform programming tasks need to possess programming knowledge. This kind of\nknowledge is very important for domain-specific agents because only with this knowledge can they make\nreasonable decisions.\n3.1.2Memory\nIn our framework, “memory” stores the agent’s past observations, thoughts, and actions [131 ]. This\nm\nemory enables agents to use past experiences for strategy formulation and decision-making, similar to\nhow humans do [132 –134]. It assists agents in handling complex problems by allowing them to revis it\npast strategies and adapt to new environments.\nHowever, as interactions in LLM-based agents increase, two main issues emerge. First, the length of\nhistorical data can exceed the processing capacity of Transformer architecture-basedLLM agents, leading\nto possible data truncation. Second, with the accumulation of extensive data, it becomes increasingly\ndifficult for agents to retrieve and connect relevant memories, risking misaligned responses in ongoing\ncontexts. Therefore, appropriate memory compression and retrieval techniques are key to enhancing an\nagent’s memory capabilities.\nMethods for memory compression. We delve into two primary methods designed to compress\nmemory, ensuring efficient recall and analysis.\n•Summarizing memory. The first strategy for improving memory efficiency is memory summa-\nrization. This method summarizes historical interactions and stores them in natural language. Various\ntechniques have been proposed for memory summarization. Using prompts, some methods succinctly\nintegrate memories [135 ], while others emphasize reflective processes to create condensed memory repre-\nsentations [16 ,136]. Hierarchical methods streamline dialogues into both daily snapshots and overarching\nsummaries [137 ]. Notably, specific strategies translate environmental feedback in to textual encapsula-\ntions, bolstering agents’ contextual grasp for future engagements [138 ].\n•C\nompressing memories with vectors or data structures. Using appropriate data structures,\nLLM-basedagentscanstorehistoricalinteractionsmoreefficiently. Notably,severalmethodologiesleanon\nembedding vectors for memory sections, plans, or dialogue histories [90 ,137,139,140]. Another approach\nt\nranslatessentences into triplet configurations[141 ], while some perceive memoryas a unique data object,\nf\nosteringvaried interactions[142 ]. Furthermore, ChatDB [143 ]and DB-GPT [144 ]opt forSQL databases,\nw\nhich allows for data manipulation via SQL queries.\nMethods for memory retrieval. When an agent interacts with its environment, it is imperative\nto retrieve the most appropriate content from its memory. This ensures that the agent accesses relevant\nand accurate information to make decision [137 ,140]. A representative approach in automated retrieval\nc\nonsiders three metrics: recency, relevance, and importance. The memory score is determined as a\nweighted combination of these metrics, with memories having the highest scores being prioritized in the\nmodel’s context [16 ]. Some work introduces the concept of interactive memory objects , representing\ndialogue history that can be moved, edited, deleted, or combined through summarization [142 ]. Users\nc\nan view and manipulate these objects, influencing how the agent perceives the dialogue. Similarly, other\nstudies allow for memory operations like deletion based on specific commands provided by users [143 ].\nS\nuch methods ensure that the memory content aligns closely with user expectations.\n3.1.3Reasoning and planning\nReasoning. Reasoning, crucial in human intellectual activities for problem-solving, decision-making,\nand critical analysis, is underpinned by evidence and logic [145 –147]. Deductive, inductive, and abduc-\nt\nive reasoning are key forms recognized in these endeavors [148 ]. For LLM-based agents, like humans,\nr\neasoning capacity is crucial for addressing complex tasks [19 ].\nF\nor LLMs, the reasoning ability is considered an emergent ability, i.e., such an ability emerges once\nthe language model reaches a certain scale in size [20 ,149]. Notably, the CoT method [76 ,77] has been\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:8\ndemonstrated to elicit the reasoning capacities of LLMs by guiding them to generate rationales before\noutputting answers. Other techniques like self-consistency [78 ], self-polish [80 ], self-refine [150 ], and\ns\nelection-inference[151 ]alsoenhanceLLMperformance. Tofurtherimprovethereasoning abilityofLLMs\norLLM-basedagents, currentresearchprimarilyexploressupervised fine-tuning (SFT) methods [152 ]and\nin\nteractive training approaches [153 ,154].\nP\nlanning. Planning is a key strategy employed by humans when facing complex tasks. For humans,\nplanning helps organizethoughts, set objectives, and determine the steps to achievethoseobjectives[155 –\n157]. Similar to humans, the ability to plan is crucial for agents, and centra l to this planning module is\nthe capacity for reasoning [158 –160]. This offers a structured thought process for agents based on LL Ms.\nThrough reasoning, agents break down complex tasks into manageable sub-tasks, devising appropriate\nplans for each [161 ,162]. Moreover, as tasks progress, agents can employ introspection t o modify their\nplans, ensuring they align better with real-world circumstances, facilitating adaptive and successful task\nexecution. Typically, planning comprises two stages: plan formulation and plan reflection.\n•Plan formulation. During plan formulation, agents generally decompose an overarching task into\nnumerous sub-tasks, and various approaches have been proposed in this phase. Notably, some studies\nadvocated for LLM-based agents to decompose problems comprehensively in a single step, formulating\na complete plan at once and then executing it sequentially [79 ,163–165]. In contrast, other studies\nlik\ne the CoT-series employ an adaptive strategy, where they plan and address sub-tasks one at a time,\nallowing for more fluidity in handling intricate tasks in their entirety [76 ,77,166]. Additionally, some\nm\nethods emphasize hierarchical planning [167 ,168], while others underscore a strategy in which final\np\nlans are derived from reasoning steps structured in a tree-like format. The latter approach argues that\nagents should assess all possible paths before finalizing a plan [78 ,169–171]. While LLM-based agents\nd\nemonstrate a broad scope of general knowledge, they can occasionally face challenges when tasked with\nsituations that require expertise knowledge. Enhancing these agents by integrating them with planners\nof specific domains has shown to yield better performance [109 ,115,172,173].\n•P\nlan reflection. After formulating a plan, it is imperative to reflect upon and evaluate its merits.\nLLM-basedagentsemployinternalfeedbackmechanisms,often drawinginsightsfrompre-existingmodels,\nto hone and enhance their strategies and planning approaches[138 ,150,174,175]. To better align with hu-\nm\nan values and preferences, agentsactively engagewith humans, allowingthem to rectify misunderstand-\nings and assimilate this tailored feedback into their planning methodology [89 ,176,177]. Furthermore,\nt\nhey could gatherfeedback from tangible orvirtual surroundings, such as cues from taskaccomplishments\nor post-action observations, aiding them in revising and refining their plans [72 ,82,178–180].\n3\n.1.4Transferability and generalization\nThe remarkable nature of the human brain is largely attributed to its high degree of plasticity and\nadaptability. It can continuously adjust its structure in response to external stimuli and internal needs,\nthereby adapting to different environments and tasks. These years, plenty of research indicates that\npre-trained models can learn universal language representations [181 –183], and with only a small amount\no\nf data for fine-tuning, it can demonstrate excellent performance in downstream tasks [184 ]. There\nis\nno need to train new models from scratch, which saves a lot of computation resources. However,\nmodels trained through this task-specific fine-tuning, usually lack versatility and generalizability to other\ntasks. Instead of merely functioning as a static knowledge repository, LLM-based agents exhibit dynamic\nlearning ability which enables them to adapt to novel tasks swiftly and robustly [18 ,86,87].\nU\nnseen task generalization. Studies show that instruction-tuned LLMs exhibit zero-shot gen-\neralization without the need for task-specific fine-tuning [18 ,19,86–88]. Notably, LLMs can complete\nu\nnfamiliar tasks by following the instructions based on their own understanding. One of the imple-\nmentations is multi-task learning, for example, FLAN [86 ] finetunes language models on a collection of\nt\nasks described via instructions, and T0 [87 ] introduces a unified framework that converts every language\np\nroblem into a text-to-text format. Despite being purely a language model, GPT-4 [19 ] demonstrates\nr\nemarkable capabilities in a variety of domains and tasks, including abstraction, coding, mathematics,\nmedicine, law, and others [24 ]. Promisingly, such generalization capability can be further enhanced by\nscaling up the model size and the quantity or diversity of training instructions [75 ,185].\nI\nn-context learning. Numerous studies indicate that LLMs can perform a variety of complex tasks\nthrough in-context learning (ICL), which involves the models’ ability to learn from a few examples within\na given context [186 ]. Few-shot in-context learning enhances the performance by conc atenating the\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:9\noriginal input with several complete examples as prompts to enrich the context [69 ]. The key idea of\nI\nCL is learning from analogy, akin to the learning process of humans [187 ]. Besides, since the prompts\na\nre written in natural language, the interaction is interpretable and changeable, making it easier to\nincorporate human knowledge [76 ,188]. Unlike the supervised learning process, ICL does not involve\nfi\nne-tuning or parameter updates, greatly reducing computation costs for adapting the models to new\ntasks. Beyond texts, researchers also explore the potential ICL capabilities in different multimodal\ntasks [189 –194], making it possible for agents to be applied to large-scale real-world ta sks.\n3.2 Perception\nBoth humans and animals rely on sensory organs like eyes and ears to gather information from their\nsurroundings. These perceptual inputs are converted into neural signals and sent to the brain for pro-\ncessing [195 ,196], allowing humans to perceive and interact with the world. Similarly, it is cr ucial for\nLLM-based agents to receive information from various sources and modalities. This expanded perceptual\nspace helps agents better understand their environment, make informed decisions, and excel in a broader\nrange of tasks, making it an essential development direction. Agent handles this information to the Brain\nmodule for processing through the perception module.\nIn this section, we introduce how to enable LLM-based agents to acquire multimodal perception ca-\npabilities, encompassing textual (Subsection 3.2.1), visual (Subsection 3.2.2), and auditory inputs (Sub-\ns\nection3.2.3). We also consider other potential input forms (Subsection 3.2.4) such as tactile feedback,\ng\nestures, and 3D maps to enrich the agent’s perception domain and enhance its versatility.\n3.2.1Textual input\nText serves as a conduit for conveying data, information, and knowledge, making text communication\none of the most important ways through which humans interact with the world. LLM-based agents al-\nready have the fundamental ability to communicate with humans through textual input and output [95 ].\nI\nn a user’s textual input, aside from the explicit content, lie concealed beliefs, desires, and intentions.\nUnderstanding implied meanings is crucial for the agent to grasp the potential and underlying inten-\ntions of human users, thereby enhancing its communication efficiency and quality with users. How-\never, understanding implied meanings within textual input remains challenging for current LLM-based\nagents [75 ,197]. For example, some studies [113 ,198–200] employed reinforcement learning to perceive\nim\nplied meanings and models feedback to derive rewards. This helps deduce the speaker’s preferences,\nleading to more personalized and accurate responses from the agent. Additionally, as the agent is de-\nsigned for use in complex real-world situations, it will inevitably encounter many entirely novel tasks.\nUnderstanding text instructions for unknown tasks places higher demands on the agent’s text percep-\ntion abilities. As described in Subsection 3.1.4, an LLM that has undergone instruction tuning [86 ] can\ne\nxhibit remarkable zero-shot instruction understanding and generalization abilities, eliminating the need\nfor task-specific fine-tuning.\n3.2.2Visual input\nAlthough LLMs excel in language comprehension [19 ,201] and multi-turn conversations [202 ], they in-\nh\nerently lack visual perception and can only understand discrete textual content. Visual input usually\ncontains a wealth of information about the world, including properties of objects, spatial relationships,\nscene layouts, and more in the agent’s surroundings. Therefore, integrating visual information with data\nfrom other modalities can offer the agent a broader context and a more precise understanding [101 ],\nd\neepening the agent’s perception of the environment.\nTo help the agent understand the information contained within images, a straightforward approach\nis to generate corresponding text descriptions for image inputs, known as image captioning [203 –207].\nC\naptions can be directly linked with standard text instructions and fed into LLM-based agents. This ap-\nproach is highly interpretable and does not require additional training for caption generation, which can\nsave a significant number of computational resources. However, caption generation is a low-bandwidth\nmethod [101 ,208], and it may lose a lot of potential information during the conversion pr ocess. Further-\nmore, the agent’s focus on images may introduce biases.\nThesecondapproachto endowinganLLM-basedagentwith visualunderstandingcapabilitiesismodal-\nity fusion, where a visual encoder is integrated with an LLM at the embedding level [209 ,210]. Freezing\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:10\none or both of them during training is a widely adopted paradigm that achieves a balance between train-\ning resources and model performance [211 ]. However, LLMs cannot directly understand the output of a\nv\nisual encoder, so it is necessary to convert the image encoding into embeddings that LLMs can compre-\nhend. In other words, it involves aligning the visual encoder with the LLM. This usually requires adding\nan extra learnable interface layer between them. For example, BLIP-2 [211 ] and InstructBLIP [212 ] use\nt\nhe querying transformer (Q-Former) module as an intermediate layer between the visual encoder and\nthe LLM [212 ]. At the same time, some researchers adopt a computationally efficien t method using a\nsingle projection layer to achieve visual-text alignment, reducing the need for training additional param-\neters [99,210,213]. Moreover, the projection layer can effectively integrate with the le arnable interface to\nadapt the dimensions of its outputs, making them compatible with LLMs [214 –217].\n3\n.2.3Auditory input\nAuditory information constitutes a crucial component of world information. When an agent possesses\nauditory capabilities, it can improve its awareness of interactive content, the surrounding environment,\nand even potential dangers. While there are numerous well-established models and approaches [218 –220]\nf\nor processing audio as a standalone modality, these models often only excel at specific tasks. Given\nthe excellent tool-using capabilities of LLMs (which will be discussed in detail in Subsection 3.3), a\nv\nery intuitive idea is that the agent can use LLMs as control hubs, invoking existing toolsets or model\nrepositories in a cascading manner to perceive audio information.\nFurthermore, an audio spectrogram can serve as a medium to endow LLM-based agents with auditory\ncapabilities,asitoffersaclearrepresentationofhowthefrequencyspectrumofanaudiosignalevolvesover\ntime [221 ]. For a segment of audio data over a period of time, it can be abstract ed into a finite-length\naudio spectrogram. An audio spectrogram has a 2D representation, which can be visualized as a flat\nimage. Hence, some research [222 ,223] efforts aim to migrate perceptual methods from the visual domain\nt\no audio. Audio spectrogram transformer (AST) [222 ] employs a Transformer architecture similar to ViT\nt\no process audio spectrogram images. By segmenting the audio spectrogram into patches, it achieves\neffective encoding of audio information.\n3.2.4Other input\nLLM-based agents are expected to be equipped with richer perception modules in the future. They could\nperceive and understand diverse modalities in the real world, much like humans. For example, agents\ncould have unique touch and smell organs, allowing them to gather more detailed information when\ninteracting with objects. At the same time, agents can also be equipped with hardware devices, such as\nGPS, Lidar, and other sensors, having a clearer sense of the spatial position, temperature, and brightness\nand taking environment-aware actions.\n3.3 Action\nAfter humans perceive their environment, their brains integrate, analyze, and reason to make decisions.\nSubsequently, they employ their nervous systems to control their bodies, enabling adaptive or creative\nactions in response to the environment, such as engaging in conversation, evading obstacles, or starting a\nfire. When an agent possesses a brain-like structure with capabilities of knowledge, memory, reasoning,\nplanning, and generalization, as well as multimodal perception, it is also expected to possess a diverse\nrange of actions akin to humans to respond to its surrounding environment. In constructing such an\nagent, the action module receives action sequences sent by the brain module and carries out actions to\ninteract with the environment.\nThis section begins with textual output (Subsection 3.3.1), which is the inherent capability of LLM-\nb\nased agents. Next we talk about the tool-using capability of LLM-based agents (Subsection 3.3.2),\nw\nhich has proved effective in enhancing their versatility and expertise. Finally, we discuss equipping the\nLLM-basedagentwith embodiedactiontofacilitateitsgroundinginthephysicalworld(Subsection 3.3.3).\n3\n.3.1Textual output\nRecently, the rise and development of Transformer-based generative LLMs have endowed LLM-based\nagents with inherent language generation capabilities [19 ,224]. The text quality they generate excels in\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:11\nvarious aspects such as fluency, relevance, diversity, controllability [112 ,225–227]. Consequently, LLM-\nb\nased agents can be exceptionally strong language generators.\n3.3.2Tool using\nTools are extensions of the capabilities of humans. When faced with complex tasks, humans employ tools\nto simplify task-solving and enhance efficiency, freeing time, and resources. Similarly, agents have the\npotential to accomplishcomplextasksmoreefficiently and with higher qualityifthey alsolearnto use and\nutilize tools [75 ]. LLM-based agents have limitations because of hallucination [228 ], the lack of training\nd\nata and tuning for specific fields [229 ], untransparent decision-making process [230 ] and susceptibility\nt\no adversarial attacks [231 ]. Fortunately, specialized tools enable LLM-based agents to enhanc e their\nexpertise, adapt domain knowledge, and be more suitable for domain-specific needs in a pluggable form.\nThey also exhibit stronger interpretability and robustness. LLM-based agents not only require the use of\ntools, but are also well-suited for tool integration. LLMs show remarkable reasoning and decision-making\nabilities in complex interactive environments [78 ] and significant potential in intent understanding and\no\nther aspects [19 ,232–234]. These make LLM-based agents proficient tool users.\nL\nearning to use tools. Leveragingthe powerfulzero-shotand few-shotlearningabilitiesofLLMs[69 ,\n235], agents can acquire knowledge about tools by utilizing zero-shot pro mpts that contain descriptions\nof tool functionalities and parameters, or few-shot prompts that provide demonstrations of specific tool\nusage scenarios and corresponding methods [73 ,236]. These learning approaches parallel human methods\no\nf learning by consulting tool manuals or observing others using tools [75 ]. Besides configurations and\nd\nemonstrations, agents can also learn from feedback received from both the environment and humans [18 ,\n237,238]. Environmental feedback encompasses result feedback on wheth er actions have successfully\ncompleted the task and intermediate feedback that captures changes in the environmental state caused\nby actions; human feedback comprises explicit evaluations and implicit behaviors, such as clicking on\nlinks [75].\nI\ntiscrucialtoimprovetheagent’sgeneralizationabilityintoolusagetohandlecomplicatedscenariosas\nwell as continuously evolving new tools. To accomplish this, agents need to grasp the common principles\norpatternsintoolusagestrategies, whichcanpotentiallybeachievedthroughmeta-toollearning[239 ]. In\na\nddition, techniques like curriculum learning [240 ] can enhance the agent’s understanding of relationships\nb\netweensimpleandcomplextools, suchashowcomplextoolsarebuilt onsimpleronesandallowagentsto\neffectively discern nuances across various application scenarios and transfer previously learned knowledge\nto new tools [75 ]. Previous studies showed that LLM-based agents not only have the ability to generalize\nthe use of existing tools, but can also make new tools they need by generating executable programs, or\nintegrating existing tools into more powerful ones [75 ,241,242].\nT\nools expanding the action space of LLM-based agents. With the help of tools, agents can\nutilize various external resources such as web applications and other LMs during the reasoning and\nplanning phase [73 ]. This process can provide information with high expertise, reliability, d iversity, and\nquality for LLM-based agents, facilitating their decision-making and action. For example, search-based\ntools can improve the scope and quality of the knowledge accessible to the agents with the aid of external\ndatabases, knowledgegraphs, andweb pages, while domain-specifictoolscan enhanceanagent’sexpertise\nin the correspondingfield [243 ,244]. Some researchershave alreadydeveloped LLM-based controllers that\ngenerateSQLstatementstoquerydatabases, ortoconvertuserqueriesintosearchrequestsandusesearch\nengines to obtain the desired results [71 ,143]. What is more, LLM-based agents can use scientific tools to\ne\nxecute tasks like organic synthesis in chemistry, or interface with Python interpreters to enhance their\nperformance on intricate mathematical computation tasks [245 ,246].\nA\nlthough the tools mentioned before enhance the capabilities of agents, the medium of interaction\nwith the environment remains text-based. However, tools are designed to expand the functionality of\nlanguage models, and their outputs are not limited to text. Tools for non-textual output can diversify the\nmodalities of agent actions, thereby expanding the application scenarios of LLM-based agents [163 ,247].\nF\nor example, image processing and generation can be accomplished by an agent that draws on a visual\nmodel [248 ].\n3\n.3.3Embodied action\nIn the pursuit of AGI, the embodied agent is considered a pivotal paradigm. The embodiment hypoth-\nesis [249 ] draws inspiration from the human intelligence development process, posing that an agent’s\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:12\nintelligence arises from continuous interaction and feedback with the environment rather than relying\nsolely on well-curated textbooks [250 ]. Similarly, people anticipate that LLM-based agents should be\nc\napable of actively perceiving, comprehending, and interacting with physical environments, making deci-\nsions, and generating intended behaviors to modify the environment based on LLM’s internal knowledge.\nWe collectively term these as embodied actions, which enable agents’ ability to interact with and com-\nprehend the world in a manner closely resembling human behavior.\nThe potential of LLM-based agents for embodied actions. Despite the extensive success of\nRL-based embodiment [50 ,51,251], it does have certain limitations in some aspects. For example, RL\na\nlgorithms face limitations in terms of cost efficiency, generalization, and complex problem reasoning due\ntochallengesinmodelingthedynamicenvironmentandtherelianceonrewardsignalrepresentations[252 ].\nR\necent studies haveindicated that leveragingthe rich internalknowledgeacquired duringthe pre-training\nof LLMs can effectively alleviate these issues [101 ,171,178,253].\n•C\nost efficiency. Some on-policy algorithms struggle with sample efficiency as they require iter-\natively sampled data for policy updates while gathering enough embodied data for high-performance\ntraining is costly. The constraint is also found in some end-to-end models [254 –256]. By leveraging\nt\nhe intrinsic knowledge from LLMs, agents like PaLM-E [101 ] jointly train robotic data with general\nv\nisual-language data to achieve significant transfer ability in embodied tasks while also showcasing that\ngeometric input representations can improve training data efficiency.\n•Embodied action generalization. As discussedin Subsection 3.1.4, anagent’scompetence should\ne\nxtend beyond specific tasks. Different from the majority of RL algorithms [82 ,257–259], fine-tuned by\nt\nasks in diverse forms and types, LLMs have showcased remarkable cross-task generalization capabili-\nties [260,261]. Further, natural language serves both as a means to interact wit h the environment and as\na medium for transferring foundational skills to new tasks [262 ]. SayCan [163 ] decomposes task instruc-\nt\nions presented in prompts using LLMs into corresponding skill commands, but in partially observable\nenvironments, limited priorskills often do not achievesatisfactoryperformance[82 ]. To addressthis, Voy-\na\nger [177] introduces the skill library component to continuously collect novel self-verified skills, which\nallows for the agent’s lifelong learning capabilities.\n•Embodied action planning. Planning is a pivotal strategy for agents when addressing complex\nproblems. In traditional hierarchical reinforcement learning (HRL) methods, the high-level policy con-\nstraints sub-goals for the low-level policy which produces appropriate action signals [263 –265]. Similar\nt\no the role of high-level policies, LLMs with emerging reasoning abilities [20 ] can be seamlessly applied\nt\no complex tasks in a zero-shot or few-shot manner [76 ,78–80]. In addition, external feedback from the\ne\nnvironment can further enhance LLM-based agents’ planning performance. Some studies [72 ,81,82,266]\nd\nynamically generated, maintained, and adjusted high-levelaction plans in orderto minimize dependency\non prior knowledge, thereby grounding the plan. Such feedback can also come from models and humans,\nwhich can usually be referred to as the critics, assessing task completion based on the current state and\ntask prompts [19 ,177].\nE\nmbodied actions for LLM-based agents. There are several fundamental embodied actions or\ntasks for LLM-based agents to master, primarily including observation, manipulation, and navigation.\n•Observation. Observation plays a crucial role in subsequent embodied actions by which the agent\nacquires environmental information and updates states. As mentioned in Subsection 3.2, during obser-\nv\nation stage, various inputs are ultimately converged into a multimodal signal. A common approach\nentails a pre-trained vision transformer (ViT) used as the alignment module for text and visual informa-\ntion [101 ,102,267]. In recent times, more researches take audio as a modality for embe dded observation.\nSoundspaces [268 ] proposes the identification of spatial geometric elements guided by reverberant audio\ninput [265 ]. Apart from the cascading paradigm [218 ,219,269], audio information encoding can also\ne\nnhance the seamless integration of audio with other modalities of inputs [222 ]. Additionally, the agents’\no\nbservation could be from real-time human linguistic instructions, which helps the agent in acquiring\ndetail information that may not be readily obtained or parsed [177 ,270].\n•M\nanipulation. Embodied agents’ manipulation tasks generally include object rearrangements,\ntabletop manipulation, and mobile manipulation [17 ,101]. The typical case entails the agent executing a\ns\nequence of tasks in the kitchen, which includes retrieving items from drawers and handing them to the\nuser [163 ]. This task involves combining a series of subgoals and maintaining synch ronization between\nthe agent’s state and such subgoals are of significance [271 ]. Besides these, AlphaBlock [272 ] focuses on\nm\nore challenging manipulation tasks (e.g., making a smiley face using building blocks), which requires\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:13\nagents to have a more grounded understanding of the instructions. It fine-tunes a multimodal model\nbased on a complex task dataset comprising corresponding multi-step planning and observation pairs to\nenhance its comprehension of high-level cognitive instructions.\n•Navigation. In navigation tasks, agents need to dynamically alter their positions, which often\ninvolves multi-angle and multi-object observations [17 ]. Before navigation, it is essential for embodied\na\ngents to establish prior internal maps, typically in the form of a topological map, semantic map, and\noccupancy map [250 ]. For example, LM-Nav [273 ] utilizes the VNM [274 ] to create the topological map,\na\nnd further leverages the LLM and VLM for analyzing the environment to find the optimal path. Some\nstudies [275 ,276] highlighted the importance of spatial representation to obtain the precise localization\nof targets by leveraging the pre-trained VLM model to combine visual features from images with 3D\nreconstructions of the physical world [250 ]. During navigation tasks, the states of the agent are often\nin\nfluenced by its past actions. A memory mechanism is needed to record historical information [277 ],\nw\nhich is also adopted in Smallville and Voyager [16 ,177,278,279]. Additionally, audio input is also of\ng\nreat significance for navigation tasks. Previous studies demonstrate a basic framework that includes a\ndynamic path planner that uses visual and auditory observations along with spatial memories to plan a\nseries of actions [265 ,280].\nC\nomplex and grounding embodied actions. By integrating actions above, the agent can accom-\nplish more complex tasks, such as embodied question answering, e.g., Is the watermelon in the kitchen\nlarger than the pot? Which one is harder? To address these questions, the agent needs to navigate to the\nkitchen, observe the sizes of both objects, and then answer the questions through comparison [250 ]. In\nt\nerms of control strategies, as previously mentioned, LLM-based agents trained on particular embodied\ndatasets typically generate high-level policy commands to control low-level policies [163 ] like a robotic\nt\nransformer [101 ,281,282], which takes images and instructions as inputs and produces contro l com-\nmands for the end effector and robotic arms as well as virtual embodied controllers due to high costs of\nrobotic operators [16 ,89,90,139,271,283,284]. By utilizing the Mineflayer [285 ] API, some studies enable\nc\nost-effective examination of embodied agents’ operations including exploration, planning, and lifelong\nlearning [177 ]. On the other hand, grounding language to action space poses an ob stacle for agents.\nFor example, understanding the linguistic metaphor expressions like “jump down like a cat” requires\nadequate world knowledge [286 ]. Sumers et al. [287 ] endeavored to amalgamate text distillation with\nh\nindsight experience replay to construct a dataset for training. Nevertheless, additional investigation on\ngrounding embodied action still remains necessary as it plays an increasingly pivotal role across various\ndomains in human life.\n4 Agents in practice: harnessing AI for good\nThe LLM-based agent, as an emerging direction, has gained increasing attention from researchers. Many\napplications in specific domains and tasks have already been developed, showcasing the powerful and\nversatile capabilities of agents [288 ]. As an LLM-based agent, its design objective should always be\nb\neneficial to humans, i.e., humans can harness AI for good.\nIn this section, we provide an overview of current applications of LLM-based agents, aiming to offer a\nbroad perspective for the practical deployment scenarios (see Figure 3). First, we elucidate the diverse\na\npplication scenarios of single agent, including task-oriented, innovation-oriented, and lifecycle-oriented\nscenarios (Subsection 4.1). Then, we present the significant coordinating potential of multiple agents:\ncooperative interaction or adversarial interaction (Subsection 4.2). Finally, we categorize the interactive\nc\nollaboration between humans and agents into two paradigms and introduce the specific applications\n(Subsection 4.3).\n4\n.1 General ability of single agent\nCurrently, there is a vibrant development of application instances of LLM-based agents [289 –291]. Au-\nt\noGPT [95 ] is one of the ongoing popular projects aiming to achieve a fully autono mous system. Apart\nfrom the basic functions of LLMs, the AutoGPT framework also incorporates various external tools and\nmemory management. After users input their customized objectives, they can free their hands and wait\nfor AutoGPT to automatically generate thoughts and perform specific tasks, all without requiring ad-\nditional user prompts. As shown in Figure 4, we introduce the astonishingly capabilities that the agent\ne\nxhibits in scenarios where only one single agent is present.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:14\nFigure 3 (Color online) Scenarios of LLM-based agent applications: single-agent deployment, multi-agent interaction, and human-\nagent interaction. A single agent possesses diverse capabilities and demonstrates outstanding task-solving performance in various\napplication orientations. When multiple agents interact, they can achieve advancement through cooperative or adversarial interac-\ntions. Furthermore, in human-agent interactions, human feedback can enable agents to perform tasks more efficiently and safely,\nwhile agents can also provide better service to humans.\nFigure 4 (Color online) Practical applications of the single LLM-based agent in different scenarios. In task-oriented deployment,\nagents assist human users in solving daily tasks. They need to possess basic instruction comprehension and task decomposition\nabilities. In innovation-oriented deployment, agents demonstrate the potential for autonomous exploration in scientific domains.\nIn lifecycle-oriented deployment, agents have the ability to continuously explore, learn, and utilize new skills to ensure long-term\nsurvival in an open world.\n4.1.1Task-oriented deployment\nTheLLM-basedagents, whichcanunderstandhumannaturallanguageinstructionsandperformeveryday\ntasks [292 ], are currently among the most favored and practically valuable agen ts by users. This is\nbecause they have the potential to enhance task efficiency, alleviate user workload, and promote access\nfor a broader user base. In task-oriented deployment, the agent follows high-level instructions from users,\nundertakingtaskssuch asgoaldecomposition[167 ,171,293,294],sequence planningofsub-goals[167 ,295],\nin\nteractiveexplorationoftheenvironment[165 ,292,296,297],untilthefinalobjectiveisachieved. Basedon\nt\nask types, we divide these deployment environments into web scenarios and life scenarios, and introduce\nthe specific roles that agents play in them.\nIn web scenarios. Performing specific tasks on behalf of users in a web scenario is known as the\nweb navigation problem [296 ]. Agents interpret user instructions, break them down into multiple b asic\noperations, and interact with computers. This often includes web tasks such as filling out forms, online\nshopping, and sending emails. Agents need to possess the ability to understand instructions within\ncomplex web scenarios, adapt to changes (such as noisy text and dynamic HTML web pages), and\ngeneralize successful operations [292 ]. In this way, agents can achieve accessibility and automation when\nd\nealing with unseen tasks in the future [298 ], freeing humans from repeated interactions with computer\nU\nIs.\nTo enable successful interactions between agents and realistic web pages, some researchers [294 ,299]\nh\nave started to leverage the powerful HTML reading and understanding abilities of LLMs. By designing\nprompts, they attempt to make agents understand the entire HTML source code and predict more\nreasonable next action steps. Mind2Web [300 ] combines multiple LLMs fine-tuned for HTML, allowing\nt\nhem to summarize verbose HTML code [293 ] in real-world scenarios and extract valuable information.\nF\nurthermore, WebGum [296 ]empowers agentswith visual perception abilities by employinga multimo dal\ncorpus containing HTML screenshots. Auto-GUI [301 ] also proposes a multimodal solution that directly\nin\nteracts with the interface on mobile devices, thus deepening the comprehensive understanding of web\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:15\npages.\nIn life scenarios. In many daily household tasks, it is essential for agents to understand implicit\ninstructions and apply common-senseknowledge[302 ]. Foran LLM-basedagent trained solely on massive\na\nmounts oftext, tasks that humans take for grantedmight requiremultiple trial-and-errorattempts [303 ].\nM\nore realistic scenarios often lead to more obscure tasks. For example, the agent should proactively turn\nit on if it is dark and there is a light in the room. To successfully chop some vegetables in the kitchen,\nthe agent needs to anticipate the possible location of a knife [167 ].\nC\nan an agent apply the world knowledge embedded in its training data to real interaction scenarios?\nHuang et al. [171 ]led the wayin exploringthis question. They demonstrated that suffic iently largeLLMs,\nwith appropriate prompts, can effectively break down high-level tasks into suitable sub-tasks without\nadditional training. However, this static reasoning and planning ability has its potential drawbacks.\nActions generated by agentsoften lack awarenessofthe dynamic environment aroundthem. For instance,\nwhen a user gives the task “clean the room”, the agent might convert it into unfeasible sub-tasks like “call\na cleaning service” [304 ]. As a result, some approachesdirectly incorporatespatial data an d item-location\nrelationships as additional inputs to the model. This allows agents to gain a precise description of their\nsurroundings and plan next actions more effectively [167 ,295,304].\n4\n.1.2Innovation-oriented deployment\nThe LLM-based agent has demonstrated strong capabilities in performing tasks. However, in a more\nintellectually demanding field, like cutting-edge science, the potential of agents has not been fully realized\nyet. This limitation mainly arises from two challenges [305 ]. On one hand, the inherent complexity\no\nf science poses a significant barrier. Many domain-specific terms and multi-dimensional structures\nare difficult to represent using a single text. As a result, their complete attributes cannot be fully\nencapsulated. On the other hand, there is a severe lack of suitable training data in scientific domains,\nmaking it difficult for agents to comprehend the entire domain knowledge [306 ,307]. If the ability for\na\nutonomous exploration could be discovered within the agent, it would bring about beneficial innovation\nin human technology.\nCurrently, numerous efforts in various specialized domains aim to overcome this challenge [308 –310].\nE\nxperts from the computer field make full use of the agent’s powerful code comprehension and debugging\nabilities [288 ,311]. In the fields of chemistry and materials, researchers equip agents with various general\nor task-specific tools to better understand domain knowledge. Agents evolve into scientific assistants,\nproficient in online research and document analysis to fill data gaps. They also employ robotic APIs for\nreal-world interactions, enabling tasks like material synthesis and mechanism discovery [91 ,245,305].\nT\nhe potential of LLM-based agents in scientific innovation is evident, yet we do not expect their\nexploratory abilities to be utilized in applications that could threaten or harm humans. Boiko et al. [91 ]\ns\ntudy the hidden dangers of agents in synthesizing illegal drugs and chemical weapons, indicating that\nagents could be misled by malicious users in adversarial prompts. This serves as a warning for our future\nwork.\n4.1.3Lifecycle-oriented deployment\nBuilding a universally capable agent that can continuously explore, develop new skills, and maintain a\nlong-termlife cycle in an open, unknownworldis a colossalchallenge. This accomplishmentis regardedas\napivotalmilestonein thefieldofAGI[271 ]. Minecraft, asatypicalandwidelyexploredsimulatedsurvival\ne\nnvironment, has become a unique playground for developing and testing the comprehensive ability of\nan agent. Players typically start by learning the basics, such as mining wood and making crafting tables,\nbefore moving on to more complex tasks like fighting against monsters and crafting diamond tools [177 ].\nM\ninecraft fundamentally reflects the real world, making it conducive for researchers to investigate an\nagent’s potential to survive in the authentic world.\nThe survival algorithms of agents in Minecraft can generally be categorized into two types [177 ]: low-\nle\nvel control and high-level planning. Early efforts mainly focused on reinforcement learning [177 ,312]\na\nnd imitation learning [313 ], enabling agents to craft some low-level items. With the emergence o f LLMs,\nwhich demonstrated surprising reasoning and analytical capabilities, agents begin to utilize LLM as a\nhigh-level planner to guide simulated survival tasks [271 ,314]. Some researchers use LLM to decompose\nh\nigh-level task instructions into a series of sub-goals [315 ], basic skill sequences [314 ], or fundamental\nk\neyboard/mouse operations [315 ], gradually assisting agents in exploring the open world.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:16\nVoyager [177 ], drawing inspiration from AutoGPT [95 ], became the first LLM-based embodied lifelong\nle\narning agent in Minecraft. It introduces a skill library for storing and retrieving complex action-\nexecutable code, along with an iterative prompt mechanism that incorporates environmental feedback\nanderrorcorrection. Thisenablestheagenttoautonomouslyexploreandadapttounknownenvironments\nwithout human intervention. An AI agent capable of autonomously learning and mastering the entire\nreal-world techniques may not be as distant as once thought [315 ].\n4\n.2 Coordinating potential of multiple agents\nMotivation and background. Although LLM-based agents excel in text understanding and genera-\ntion, they inherently function as isolated entities [316 ]. A key limitation of the single-agent application\np\naradigm is their ability to collaborate with other agents and learn from social interactions. This limi-\ntation restricts their capacity to benefit from multi-turn feedback, which could otherwise improve their\nperformance [21 ].\nA\ns early as 1986, Minsky [317 ] made a forward-looking prediction. In his book T he Society of Mind ,\nhe introduced a novel theory of intelligence, suggesting that intelligence emerges from the interactions of\nmany smaller agents with specific functions. For instance, certain agents might be responsible for pattern\nrecognition, while others might handle decision-making or generate solutions. This idea has been put into\nconcrete practice with the rise of distributed artificial intelligence [318 ]. Multi-agent systems (MAS) [3 ],\na\ns one of the primary research domains, focus on how a group of agents can effectively coordinate\nand collaborate to solve problems. Some specialized communication languages, like KQML [319 ], were\nd\nesigned early on to support message transmission and knowledge sharing among agents. In the 21st\ncentury, integratingreinforcementlearningalgorithms(suchasQ-learning)withdeeplearninghasbecome\na prominent technique for developing MAS that operate in complex environments [320 ]. Nowadays, the\nc\nonstruction approach based on LLMs is beginning to demonstrate remarkable potential. The natural\nlanguage communication between agents has become more elegant and easily comprehensible to humans,\nresulting in a significant leap in interaction efficiency.\nPotential advantages. Specifically, an LLM-based multi-agent system can offer several advantages.\nJustasSmith clearlystatedin The Wealth of Nations [321],“Thegreatestimprovementsintheproductive\np\nowers of labor, and most of the skill, dexterity, and judgment with which it is directed or applied,\nseem to be results of the division of labor.” Based on the principle of division of labor, a single agent\nequipped with specialized skills and domain knowledge can engage in specific tasks. On the one hand,\nagents’ skills in handling specific tasks are increasingly refined through the division of labor. On the\nother hand, decomposing complex tasks into multiple subtasks can eliminate the time spent switching\nbetween different processes. In the end, efficient division of labor among multiple agents can accomplish\na significantly greater workload than when there is no specialization, substantially improving the overall\nsystem’s efficiency and output quality.\nIn Subsection 4.1, we have provided a comprehensive introduction to the versatile abilit ies of LLM-\nbased agents. In this subsection, we focus on exploring the ways agents interact with each other in a\nmulti-agent environment. Based on current research, these interactions can be broadly categorized as\nfollows: cooperative interaction and adversarial interaction (see Figure 5).\n4\n.2.1Cooperative interaction for complementarity\nCooperative multi-agent systems are the most widely deployed pattern in practical usage. Within such\nsystems, individual agent assesses the needs and capabilities of other agents and actively seeks collabo-\nrative actions and information sharing with them [89 ]. This approach brings forth numerous potential\nb\nenefits, including enhanced task efficiency, collective decision improvement, and the resolution of com-\nplex real-world problems that one single agent cannot solve independently. We introduce and categorize\nexisting cooperative multi-agent applications into two types: disordered cooperation and ordered coop-\neration.\nDisordered cooperation. When three or more agents are present within a system, each agent is\nfree to express their perspectives and opinions openly. They can provide feedback and suggestions for\nmodifying responses related to the task at hand [322 ]. This entire discussion process is uncontrolled,\nla\ncking any specific sequence. We refer to this kind of multi-agent cooperation as disordered cooperation.\nChatLLM network [323 ] is an exemplary representative of this concept. It emulates the fo rward and\nbackwardpropagationprocess within a neural network, treating each agent as an individual node. Agents\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:17\nFigure 5 (Color online) Interaction scenarios for multiple LLM-based agents. In cooperative interaction, agents collaborate in\neither a disordered or ordered manner to achieve shared objectives. In adversarial interaction, agents compete in a tit-for-tat\nfashion to enhance their respective performance.\nin the subsequent layer need to process inputs from all the preceding agents and propagate forward. One\npotentialsolutionisintroducingadedicatedcoordinatingagent, responsibleforintegratingandorganizing\nresponses from all agents, thus updating the final answer [324 ]. However, consolidating a large amount\no\nf feedback data and extracting valuable insights pose a significant challenge for the coordinating agent.\nFurthermore,majorityvotingcanalsoserveasaneffectiveapproachtomakingappropriatedecisions[325 ].\nO\nrdered cooperation. When agents in the system adhere to specific rules, for instance, expressing\ntheir opinions one by one in a sequential manner, downstream agents only need to focus on the outputs\nfrom upstream. This leads to a significant improvement in task completion efficiency. The entire discus-\nsion process is highly organized and ordered. We term this kind of multi-agent cooperation as ordered\ncooperation. It isworthnoting that systems with only twoagents, essentiallyengagingin a conversational\nmanner, also fall under the category of ordered cooperation.\nCAMEL [89 ] stands as a successful implementation of a dual-agent cooperativ e system. Within a role-\nplaying communication framework, agents take on the roles of users (giving instructions) and assistants\n(providing specific solutions). Through multi-turn dialogues, these agents autonomously collaborate to\nfulfill user instructions [326 ]. On the other hand, Talebirad et al. [316 ] were among the first to systemat-\nic\nally introduce a comprehensive LLM-based multi-agent collaboration framework. This paradigm aims\nto harness the strengths of each individual agent and foster cooperative relationships among them. Many\napplications of multi-agent cooperation have successfully been built upon this foundation [21 ,327–330].\nT\no promote more efficient collaboration, researchers hope that agents can learn from successful human\ncooperation examples [90 ]. Wang et al. [331 ] advocated for the integration of evolutionary game theory\na\nnd artificial intelligence to advance the mathematics of multi-agent learning systems. MetaGPT [332 ]\nd\nraws inspiration from the classic waterfall model in software development. By encoding advanced hu-\nman management experience into agent prompts, collaboration among multiple agents becomes more\nstructured.\nHowever, during MetaGPT’s practical exploration, a potential threat to multi-agent cooperation has\nbeen identified. Without setting corresponding rules, frequent interactions among multiple agents can\namplify minor hallucinations indefinitely [332 ]. For example, in software development, issues like in-\nc\nomplete functions, missing dependencies, and bugs that are imperceptible to the human eye may arise.\nIntroducing techniques like cross-validation [90 ] or timely external feedback could have a positive impact\no\nn the quality of agent outputs.\n4.2.2Adversarial interaction for advancement\nTraditionally, cooperative methods have been extensively explored in multi-agent systems. However,\nresearchers increasingly recognize that introducing concepts from game theory [333 ,334] into systems\nc\nan lead to more robust and efficient behaviors. In competitive environments, agents can swiftly adjust\nstrategies through dynamic interactions, striving to select the most advantageous or rational actions in\nresponse to changes caused by other agents [50 ,335]. AlphaGo Zero [336 ], for instance, is an agent for Go\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:18\nFigure 6 (Color online) Two paradigms of human-agent interaction. In the instructor-executor paradigm (a), humans provide\ninstructions or feedback, while agents act as executors. In the equal partnership paradigm (b), agents are human-like, able to\nengage in empathetic conversation and participate in collaborative tasks with humans.\nthat achievedsignificantbreakthroughsthroughaprocessofself-play. Similarly, within LLM-basedmulti-\nagent systems, fostering change among agents can naturally occur through competition, argumentation,\nand debate [337 ,338]. By abandoning rigid beliefs and engaging in thoughtful reflection, ad versarial\ninteraction enhances the quality of responses.\nResearchers first delve into the fundamental debating abilities of LLM-based agents [114 ,339,340].\nF\nindings demonstrate that when multiple agents express their arguments in the state of “tit for tat”,\none agent can receive substantial external feedback from other agents, thereby correcting its distorted\nthoughts [93 ]. Consequently, multi-agent adversarial systems are suitable in sce narios requiring high-\nquality responses and accurate decision-making. For example, Du et al. [92 ] introduced the concept of\nd\nebate, endowing agents with responses from fellow peers. When these responses diverge from an agent’s\nown judgments, a “mental” argumentation occurs, leading to refined solutions.\nThe performance of the multi-agent adversarial system has shown considerable promise. However, the\nsystem is essentially dependent on the strength of LLMs and faces several basic challenges: (1) With a\nprolonged debate, LLM’s limited context cannot process the entire input. (2) In a multi-agent environ-\nment, computational overhead significantly increases. (3) Multi-agent negotiation may converge to an\nincorrect consensus, and all agents are firmly convinced of its accuracy [92 ]. The development of MAS is\ns\ntill far from being mature and feasible. Introducing human guides when appropriate to compensate for\nagents’ shortcomings is a good choice to promote the further advancements of agents.\n4.3 Interactive engagement between human and agent\nHuman-agent interaction, as the name suggests, involves agents collaborating with humans to accomplish\ntasks. Throughout the interaction, humans play a pivotal role by offering guidance or by regulating the\nsafety, legality, and ethical conduct ofagents[341 ,342]. This is particularlycrucial in specialized domains,\ns\nuch as medicine where data privacy concerns exist [343 ]. In such cases, human involvement can serve\na\ns a valuable means to compensate for the lack of data, thereby facilitating smoother and more secure\ncollaborative processes. The interaction between humans and agents can be classified into two paradigms\n(see Figure 6). (1) Unequal interaction (i.e., instructor-executor paradigm), h umans serve as issuers\nof instructions, while agents act as executors, participating as assistants to humans in collaboration.\n(2) Equal interaction (i.e., equal partnership paradigm): agents reach the level of humans, participating\non an equal footing with humans in interaction.\n4.3.1Instructor-executor paradigm\nThe simplest approach involves human guidance throughout the process: humans provide specific in-\nstructions directly, while the agents’ role is to understand natural language commands from humans and\ntranslate them into corresponding actions [344 –346]. In Subsection 4.1, we have presented the scenario\nw\nhere agents solve single-step problems or receive high-level instructions from humans. In addition, some\nstudies have shown that breaking down instructions into step-by-step formats can improve the cross-task\ngeneralization capabilities of language models [347 ]. Considering the interactive nature of language, in\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:19\nthis section, we assume that the dialogue between humans and agents is also interactive: the agent re-\nsponds to each human instruction, refining its action through alternating iterations to ultimately meet\nhuman requirements [177 ,348]. While this approach does achieve the goal of human-agent interact ion, it\nrequires a substantial amount of human effort and, in certain tasks, might even necessitate a high level of\nexpertise. To alleviate this issue, the agent can be empowered to autonomously accomplish tasks, while\nhumans only need to provide feedback in certain circumstances. Here, we roughly categorize feedback\ninto two types: quantitative feedback and qualitative feedback.\nQuantitative feedback. The forms of quantitative feedback mainly include absolute evaluations like\nbinary scores and ratings, as well as relative scores. Binary feedback refers to the positive and nega-\ntive evaluations provided by humans, which agents utilize to enhance their self-optimization [349 –353].\nC\nomprising only two categories, this type of user feedback is often easy to collect, but sometimes it may\noversimplify user intent by neglecting potential intermediate scenarios. To showcase these intermediate\nscenarios, researchers attempt to expand from binary feedback to rating feedback, which involves cat-\negorizing into more fine-grained levels. However, the results of Kreutzer et al. [354 ] suggest that such\nm\nulti-level artificial ratings method might be inefficient or less reliable. Furthermore, agents can learn\nhuman preference from comparative scores like multiple choice [355 ,356].\nQ\nualitative feedback. Text feedback is usually offered in natural language: humans provide advice\non how to modify outputs generated by agents, and the agents then incorporate these suggestions to\nrefine their subsequent outputs [357 ,358]. For agents without multimodal perception capabilities, hu-\nm\nans can act as critics, offering visual critiques [177 ]. Additionally, agents can utilize a memory module\nt\no store feedback for future reuse [359 ]. In Scheurer et al. [360 ], humans give feedback on the initial\no\nutput generated by agents, prompting the agents to formulate various improvement proposals. The\nagents then discern and adopt the most suitable proposal, harmonizing with the human feedback. Com-\npared to quantitative feedback, this approach can better convey human intention, but it might be more\nchallenging for the agents to comprehend. Xu et al. [361 ] compared various types of feedback and ob-\ns\nerved that combining multiple types of feedback can yield better results. Of course, the collaborative\nnature of human-agent interaction also allows humans to directly improve the agents’ output by mod-\nifying intermediate links [176 ,362] or adjusting the conversation content [363 ]. In some studies, agents\nc\nan autonomously judge whether the conversation is proceeding smoothly and seeking feedback when\nencountering errors [364 ,365]. Humans can also choose to participate in feedback at any time, guidin g\nthe agent’s learning in the right direction [366 ].\nC\nurrently, LLM-based agents serving as human assistants hold tremendous potential across various\ndomains. In education, for instance, the robot Dona [367 ] supports multimodal interactions to assist stu-\nd\nents with registration, and Gvirsman et al. [368 ] contributed to early childhood education by achieving\nm\nultifaceted interactions. Agents can also aid in human understanding and utilization of mathemat-\nics [369]. In the field of medicine, some medical agents have been proposed, s howing enormous potential\nin terms of diagnosis assistance, consultations, and more [370 ,371]. Especially in mental health, re-\ns\nearch has shown that agents can lead to increased accessibility due to benefits such as reduced cost\nand anonymity compared to face-to-face treatment [372 ]. Leveraging such advantages, agents have found\nw\nidespread applications. Ali et al. [373 ] designed LISSA for online communication with adolescents on\nt\nhe autism spectrum, analyzing their speech and facial expressions in real-time. Hsu et al. [374 ] built\nc\nontextualized language generation approaches to provide tailored assistance for users who seek support\non diverse topics ranging from relationship stress to anxiety. Besides, in other industries like business,\na good agent can provide automated services, thereby effectively reducing labor costs [375 ]. Amidst the\np\nursuit of AGI, efforts are directed towards creating agents that can function as universal assistants in\nreal-life scenarios [376 ].\n4\n.3.2Equal partnership paradigm\nEmpathetic communicator. With the rapid development of AI, conversational agents have garnered\nextensive attention in various forms [377 ] and in various scenarios [378 –380]. Although it is intuitive that\na\ngents themselves do not possess emotions, can we enable them to exhibit emotions and thereby bridge\nthe gap between agents and humans? Therefore, a plethora of research endeavors have embarked on\ndelving into the empathetic capacities of agents. This endeavor seeks to infuse a human touch into these\nagents, enabling them to detect sentiments and emotions from human expressions, ultimately crafting\nemotionally resonant dialogues [381 –385]. Apart from generating emotionally charged language, agents\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:20\ncan dynamicallyadjust their emotionalstates and displaythem throughfacial expressionsand voice[386 ].\nT\nhese studies, viewing agents as empathetic communicators, not only enhance user satisfaction but also\nmake significant progress in fields like healthcare [373 ,374,387] and business marketing [388 ]. Unlike\ns\nimple rule-based conversation agents, agents with empathetic capacities can tailor their interactions to\nmeet users’ emotional needs [389 ].\nH\numan-level participant. Furthermore, we hope that agents can be involved in the normal lives\nof humans, cooperating with humans to complete tasks from a human-level perspective. In many real-\nworld applications, adversarial games play a pivotal role in facilitating highly efficient and effective\ndecision-making processes [390 ]. While agents have already excelled in pure competitive environments\nlik\ne chess [335 ], Go [50 ], and poker [391 ], LLM-based agents go further by devising unified cooperative\ns\ntrategies through effective negotiation and collaboration in more complex scenarios [392 –395]. Beyond\ng\names, LLM-based agents demonstrate human-level capabilities in strategy formulation, negotiation, and\nother interaction-based tasks. They can collaborate with humans, determining shared knowledge, iden-\ntifying relevant information, posing questions, and reasoning to complete tasks like allocation, planning,\nand scheduling [396 ]. Furthermore, LLM-based agents possess persuasive abilities [397 ], dynamically\nin\nfluencing human viewpoints in various interactive scenarios [398 ].\nI\nn summary, the goal of the field of human-agent interaction is to learn and understand humans, and\nultimately enable efficient and secure interactions between humans and agents. Currently, significant\nbreakthroughs have been achieved in terms of usability in this field. In the future, LLM-based agents are\nexpected to provide better assistanceto humans in accomplishingmore complex tasks in variousdomains.\n5 Agent society: from individuality to sociality\nOver an extended duration, researchers and practitioners have envisioned an interactive artificial society\nwherein human behavior can be performed through trustworthy agents [399 ]. From sandbox games such\na\ns The Sims to the concept of Metaverse, we can see how “simulated society” is defined in people’s minds:\nthe environment and the individuals interacting in it. Behind each individual can be a piece of program,\na real human, or an LLM-based agent as described in the previous sections [16 ,400,401]. Then, the\nin\nteraction between individuals also contributes to the emergence of sociality.\nIn this section, we first introduce the behaviors and personalities of LLM-based agents, tracing their\ndevelopment from individuality to sociality (Subsection 5.1). Subsequently, we introduce a general cate-\ng\norization of the diverse environments for agents to perform their behaviors and engage in interactions\n(Subsection 5.2). Finally, we discuss how the agent society works, what insights peop le can get from it,\nand the risks we need to be aware of (Subsection 5.3).\n5\n.1 Behavior and personality of LLM-based agents\nAs noted by sociologists, individuals can be analyzed in terms of both external and internal dimen-\nsions [402 ]. The external deals with observable behaviors, while the internal re lates to dispositions,\nvalues, and feelings. As shown in Figure 7, this framework offers a perspective on emergent behavior\na\nnd personality in LLM-based agents. Externally, we can observe the sociological behaviors of agents\n(Subsection 5.1.1), including how agents act individually and interact with their environme nt. Internally,\nagents may exhibit intricate aspects of the personality (Subsection 5.1.2), such as cognition, emotion,\na\nnd character, that shape their behavioral responses.\n5.1.1Social behavior\nAs Troitzsch et al. [403 ] stated, the agent society represents a complex system comprisin g individual and\ngroup social activities. Recently, LLM-based agents have exhibited spontaneous social behaviors in an\nenvironment [404 ].\nF\noundational individual behaviors. Individual behaviors arise through internal cognitive pro-\ncesses and external environmental factors. These behaviors form the basis of how agents operate and\ndevelop as individuals within society. They can be classified into three dimensions: (1) Input behav-\niors refer to the absorption of information from the surroundings. This includes perceiving sensory\nstimuli [101 ] and storing them as memories [138 ]. (2) Internalizing behaviors involve inward cognitive\np\nrocessing. This category encompasses activities such as planning [109 ], reasoning [76 ], reflection [72 ],\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:21\nFigure 7 (Color online) Overview of simulated agent society. The whole framework is divided into two parts: the agent and\nthe environment. We can observe in this figure the following. (1) (Left) At the individual level, an agent exhibits internalizing\nbehaviors like planning, reasoning, and reflection. It also displays intrinsic personality traits involving cognition, emotion, and\ncharacter. (2) (Mid) An agent and other agents can form groups and exhibit group behaviors, such as cooperation. (3) (Right)\nThe environment, whether virtual or physical, contains human actors and all available resources. For a single agent, other agents\nare also part of the environment. (4) The agents have the ability to interact with the environment via perception and action.\nand knowledge precipitation [89 ,332]. These introspective processes are essential for maturity and se lf-\nimprovement. (3) Output behaviors include outward actions ranging from object manipulation [101 ] to\ns\ntructure construction [177 ]. Moreover, agents express opinions and broadcast information to interact\nwith others [332 ,405].\nD\nynamic group behaviors. A group is a gatheringof two ormore individuals within a defined social\ncontext [406 ]. The attributes of a group evolve due to member interactions and en vironmental influences.\nThis flexibility gives rise to group behaviors, each with an impact on the larger group. The categories\nof group behaviors include the following. (1) Positive group behaviors foster collective well-being [16 ,\n90,322,327,328,340]. An example is cooperation, which is achieved through volunteer beha viors [330 ],\nb\nrainstorming discussions [327 ,340], and project management [332 ]. (2) Neutral group behaviors are\no\nbserved in LLM-based agents. LLMs are designed to be “helpful, honest, and harmless” [407 ] and\nt\nhis alignment with neutral values [408 ] leads to conformity behaviors including mimicry and spectating.\n(\n3) Negative group behaviors undermine the effectiveness of a group. Recent studies have revealed\nthat agents may exhibit confrontational actions [404 ] and even resort to destructive behaviors, such as\nd\nestroying other agents or the environment in pursuit of certain gains [330 ].\n5\n.1.2Personality\nRecent advances show that LLMs and LLM-based agents exhibit a form of personality through inter-\nactions with the group and the environment [409 –411]. The widely accepted definition of personality\nin\ncludes cognitive, emotional, and character traits [412 ].\nC\nognitive abilities. Cognitive abilities refer to the mental processes, including thinking, judging,\nand problem-solving. Recent studies have applied cognitive psychology methods to investigate emerging\npersonalities of LLM-based agents [413 –415]. They conducted psychology experiments about judgment\na\nnd decision-making to test agent systems [413 ,414,416,417]. Specifically, LLMs have been evaluated\nu\nsing the cognitive reflection test (CRT) to underscore their capacity for deliberate thinking beyond\nmere intuition [418 ,419]. These findings indicate that LLM-based agents display human-like co gnitive\nintelligence in specific areas.\nEmotional intelligence. Emotions involve subjective feelings and mood states. With the increasing\npotency of LLMs, LLM-based agents are now demonstrating a nuanced understanding of emotions [24 ].\nR\necent research has explored the emotional intelligence (EI) of LLMs, including emotion recognition,\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:22\ninterpretation, and understanding. LLMs can accurately identify user emotions, exhibit empathy [420 ,\n421], and regulatetheir emotional responses[386 ,422,423]. Theseadvances contribute to the development\no\nf empathetic artificial intelligence (EAI), a crucial facet of achieving AGI. Bates et al. [424 ] explored the\nr\nole of emotion modeling in creating more believable agents. By developing socio-emotional skills and\nintegrating them into agent architectures, LLM-based agents may be able to engage in more naturalistic\ninteractions.\nCharacter portrayal. While cognition involves mental abilities and emotion relates to subjective\nexperiences, a narrower concept of personality typically pertains to distinctive character patterns. Re-\nsearchers analyze character traits in LLMs using frameworks like the big five personality trait mea-\nsure [425 ,426] and the Myers-Briggs type indicator (MBTI) [425 –427]. In addition, investigations of\np\notentially harmful dark personality traits underscore the complexity and multifaceted nature of char-\nacter portrayal in these agents [428 ]. The exploration of customizable character portrayal in agents ha s\nalso been studied [429 ], allowing users to shape agents that align with desired profiles. Techn iques like\nprompt engineering [16 ,430] and personality-enriched datasets [431 ,432] are used to optimize LLMs and\nt\nrain them to exhibit specific personality traits.\n5.2 Environment for agent society\nIn simulation, the society consists of not only solitary agents but also the environment where agents\ninhabit, sense, and act [433 ]. The environment affects sensory inputs and actions of agents, wh ile agents\ninfluence the state of the environment. As shown in Figure 7, for a single agent, the environment refers\nt\no other autonomous agents, humans, and external factors. It provides the resources and stimuli for\nagents. This section examines fundamental characteristics, advantages, and limitations of various envi-\nronmental paradigms, including text-based environment (Subsection 5.2.1), virtual sandbox environment\n(\nSubsection 5.2.2), and physical environment (Subsection 5.2.3).\n5\n.2.1Text-based environment\nSince LLMs mainly rely on language as their input and output format, the text-based environment\nserves as the most natural platform for LLM-based agents to operate in. Text-based environments\ncan be presented in natural or structured formats. Natural texts use descriptive language to convey\ninformation [434 ], while structured text follows standardized formats like technical d ocumentation and\nhypertext [292 ,293,297,300].\nT\nhe flexibility of the text-based environment allows for various applications, such as interactive dia-\nlog [89] and text-based games. In text-based games, agents utilize text commands to execute manipula-\ntions like moving or tool use [303 ,434–436] and express emotions through texts [437 ].\n5\n.2.2Virtual sandbox environment\nThe virtual sandbox environment provides a visualized and extensible platform for agent society, bridg-\ning the gap between simulation and reality. The key features of sandbox environments are the following.\n(1) Visualization. The virtual sandbox displays a panoramic view of the simulated setting, ranging from\n2D graphical interfaces to immersive 3D modeling. Multiple elements collectively transform abstract\nsimulations into visible landscapes. It allows for tracking agent movement, interactions, and symbolic\nrepresentation of actions. (2) Extensibility. The environment is highly extensible, facilitating the con-\nstruction and deployment of diverse scenarios. At a basic level, agents can manipulate the physical\nelements. For instance, platforms like AgentSims [140 ] and generative agents [16 ] construct artificial\nt\nowns with buildings and residents in grid-based worlds. Another example is Minecraft, which provides\na blocky and three-dimensional world with open-ended construction [177 ,283,315]. Beyond physical ele-\nm\nents, agent relationships, rules, and social norms can be defined [21 ]. The extensibility enables iterative\np\nrototyping of diverse agent societies.\n5.2.3Physical environment\nAs previouslydiscussed, the text-based environmenthas limited expressiveness, while the virtual sandbox\nlacks authentic embodied experiences. In contrast, the physical environment refers to the tangible objects\nand spaces, posing additional challenges for LLM-based agents. These challenges can be summarized as\nfollows: (1) Sensory perception and processing. The physical environment provides rich sensory inputs,\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:23\nincluding visual [101 ,270], auditory [265 ,268], and spatial senses. While this enhances sensory immersion,\nit\nalso introduces complexity. Agents need to process these sensory inputs effectively to interact with\ntheir surroundings. (2) Motion control. Unlike virtual environments, physical spaces impose realistic\nconstraints on actions through embodiment. It requires executable and grounded motion control [171 ].\nF\nor example, in a factory, an agent operating a robotic arm needs precision tuning and controlled force\nto grasp objects. Moreover, the agent must navigate the physical workspace and avoid obstacles. To ad-\ndress these challenges, agents require hardware-specific and scenario-specific training to develop adaptive\nabilities that can transfer from virtual to physical environments. More discussion will be presented in\nSubsection 6.6.\n5\n.3 Society simulation with LLM-based agents\nRecent research on simulated societies has followed two primary lines, namely, exploring the boundaries\nof the collective intelligence capabilities of LLM-based agents [90 ,115,327,330,332] and using them to\na\ncceleratediscoveriesinthesocialsciences[16 ,438,439]. Inaddition, therearealsoanumberofnoteworthy\ns\ntudies, e.g., usingsimulatedsocietiestocollectsyntheticdatasets[89 ,440,441],helpingpeopletosimulate\nr\nare yet difficult interpersonal situations [442 ,443]. With the foundation of Subsections 5.1and5.2, here\nw\ne will introduce the key properties and mechanism of agent society (Subsection 5.3.1), what we can\nle\narn from emergent social phenomena (Subsection 5.3.2), and finally the potential ethical and social\nr\nisks in it (Subsection 5.3.3).\n5\n.3.1Key properties and mechanism of agent society\nSocial simulation can be categorized into macro-level simulation and micro-level simulation [438 ]. In\nt\nhe macro-level simulation, also known as system-based simulation, researchers model the overall state\nof the system of the simulated society [444 ,445]. While micro-level simulation, also known as agent-\nb\nased simulation or MAS, indirectly simulates society by modeling individuals [446 ,447]. With the\nd\nevelopment of LLM-based agents, micro-level simulation has gained prominence recently [16 ,140]. In\nt\nhis article, wecharacterizethat the “AgentSociety”refersto anopen, persistent, situated, andorganized\nframework [399 ] where LLM-based agents interact with each other in a defined envir onment. In the\nfollowing paragraphs, we analyze how the simulated society operates through discussing these properties.\n(1) Open. One of the defining features of simulated societies lies in their openness, both in terms of\nindividuals andenvironmentalcomponents. Agentsand humans, the primaryactorswithin suchsocieties,\nhave the flexibility to enter or leave the environment without disrupting its operational integrity [448 ].\nB\nesides, the environment can be expanded by adding or removing entities and resources in the simulated\nworld, adding another level of complexity to the simulation, (2) Persistent. We expect persistence and\nsustainability from the simulated society. While individual agents within the society act in discrete\ntime steps [16 ,438], the society as a whole is somewhat detached from the transient beh avior and persists\nthrough time. This persistence creates an environmentwhere agents’ decisionsand behaviorsaccumulate,\nleading to a coherent societal trajectory. (3) Situated. The situated nature of the society emphasizes its\nexistenceandoperationwithin adistinct environment, whichis artificiallyorautomaticallyconstructedin\nadvance. Notably, the agents possess an awareness of their spatial context, understanding their location\nwithin the environment and the objects within their field of view [16 ,177]. This awareness contributes\nt\no their ability to interact proactively and contextually. (4) Organized. The simulated society operates\nwithin a meticulously organized framework and predefined rules. In the simulated world, agents interact\nwith the environment in a limited action space, while objects in the environment transform in a limited\nstate space. This organizational framework ensures that operations are coherent and comprehensible,\nleading to an ever-evolving yet enduring simulation.\n5.3.2Insights from agent society\nFollowing the exploration of how simulated society works, this section delves into the emergent social\nphenomena in agent society. In the realm of social science, the pursuit of generalized representations\nof individuals, groups, and their intricate dynamics has long been a shared objective [449 ,450]. The\ne\nmergenceofLLM-basedagentsenablesamoremicroscopicviewofsimulatedsocietyandnoveldiscoveries\nfrom the new representation.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:24\nOrganized productive cooperation. Society simulation offers valuable insights into innovative col-\nlaboration patterns that can have the potential to enhance real-world management strategies. Research\nhas shown that within this simulated society, experts with different backgrounds and abilities contribute\nto creative solutions to complex problems (e.g., software development or consulting) [89 ,90,324,330].\nF\nurthermore, through iterations of interactions and debates between agents, individual errors such as\nhallucinations or degeneration of thought (DoT) are corrected by the group, ultimately improving perfor-\nmance on the task [92 ,93]. Another observable phenomenon is that efficient communication also plays a\npivotal role in such a large cooperative group. For example, MetaGPT [332 ] has artificially has artificially\nf\normulated efficient communication styles with reference to standardized operating procedures (SOPs).\nParket al. [16 ]observedagentsworkingtogether to organizea partythrough s pontaneouscommunication\nin a simulated town.\nPropagation in social networks. Simulating social systems can also serve as a reference for pre-\ndicting social processes. Unlike traditional empirical approaches that rely on time-series data and holistic\nmodeling [451 ,452], agent-based simulation provides researchers with a more interpre table and endoge-\nnous perspective. Here we focus on its application in modeling propagation in social networks, such\nas the propagation of interpersonal relationships. Agents who are initially unconnected as friends have\nthe potential to establish connections through intermediaries [16 ]. Once the network of relationships is\ne\nstablished, we can observe the spread of information along with the underlying attitudes and emotions.\nS3[438] proposes a user-demographic inference module for capturing the number of people who are aware\nof a particular piece of information, as well as their collective sentiment. This same approach extends to\nmodeling cultural transmission [453 ] and the spread of infectious diseases [454 ]. With LLM-based agents,\nr\nesearchers can easily implement various intervention strategies as well as monitor population changes\nover time to understand the rationale behind various social phenomena of propagation.\nEthical decision-making and game theory. Simulated societies offer a dynamic platform for the\ninvestigation of intricate decision-making processes, including those influenced by ethical and moral prin-\nciples. Taking werewolf game [404 ,455] and murder mystery games [456 ] as examples, researchers explore\nt\nhe capabilities of LLM-based agents when confronted with challenges of deceit, trust, and incomplete\ninformation. These complex decision-making scenarios also intersect with game theory [457 ], where we\nf\nrequently encounter moral dilemmas pertaining to individual and collective interests. Through the mod-\neling of diverse scenarios, we can understand how agents prioritize values such as honesty, cooperation,\nand fairness in their actions. Furthermore, it not only provides an understanding of existing moral values,\nbut also helps to understand how these values evolve and develop over time. Ultimately, these insights\ncontribute to the refining of LLM-based agents to align with human values and ethical standards [21 ].\nP\nolicy formulation and improvement. One of the most promising and grounded research di-\nrections in modeling societies is to explore various economic and political states and their impact on\nsocial dynamics [458 ]. Researchers can simulate a wide array of social systems by configu ring agents\nwith different economic preferences or political ideologies. This analysis can provide valuable insights\nfor policymakers seeking to foster prosperity and promote societal well-being. In addition, as concerns\nabout environmental sustainability grow, we can also simulate scenarios involving resource extraction,\npollution, conservation efforts, and policy interventions [459 ]. These experiments will assist in making\nin\nformed decisions, foreseeing potential impacts, and formulating policies that aim to maximize positive\noutcomes while minimizing unintended negative effects.\n5.3.3Ethical and social risks in agent society\nSimulated societies powered by LLM-based agents offer significant inspirations, ranging from industrial\nengineering to scientific research. However, these simulations also bring about a myriad of ethical and\nsocial risks that demand careful consideration and mitigation [460 ].\nU\nnexpected social harm. Simulatedsocietiescarrytheriskofgeneratingunexpectedsocialphenom-\nena that may cause considerable public outcry and social harm. These phenomena span from individual-\nlevel issues like discrimination, isolation, and bullying, to broader concerns such as oppressive slavery\nand group antagonism [461 ,462]. Malicious people may manipulate these simulations for unethical social\ne\nxperiments, with consequences reaching beyond the virtual world into reality. Creating these simulated\nsocieties is akin to opening Pandora’s box, necessitating the establishment of rigorous ethical guidelines\nand oversight during their development and utilization [460 ].\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:25\nStereotypes and prejudice. Stereotypes and biases are long-standing challenges in language mod-\neling. A large part of the reason lies in the training corpora obtained from the Internet [463 ,464], which\nr\neflect, and sometimes even amplify real-world social biases such as gender, religion, and culture [465 ].\nA\nlthough LLMs havebeen aligned with human values to mitigate biased outputs, the models still struggle\nto portray minority groups well due to the long-tail effect of the training data [466 –468]. This may result\nin\nan overly one-sided focus in social science research concerning LLM-based agents, as the simulated\nbehaviors of marginalized populations usually conform to prevailing assumptions [469 ]. Researchers have\ns\ntarted addressing this concern by diversifying training data and making adjustments to LLMs [470 ,471],\nb\nut there is still much work to be done.\nPrivacy and security. Given that humans can be members of the agent society, the exchange of\nprivate information between users and LLM-based agents poses significant privacy and security con-\ncerns [472 ]. Users might inadvertently disclose sensitive personal information d uring their interactions,\nwhich will be retained in the agent’s memory for extended periods [137 ]. Such situations could lead\nt\no unauthorized surveillance, data breaches, and misuse of personal information, especially when sub-\njected to malicious attacks [473 ]. To address these risks effectively, it is essential to implement string ent\ndata protection measures, such as differential privacy protocols, regular data purges, and user consent\nmechanisms [474 ,475].\nO\nver-reliance and addictiveness. Another concern in simulated societies is the possibility of users\ndeveloping excessiveemotional attachments or even addictiveness to the agents. Despite being awarethat\nthese agents are computational entities, users may anthropomorphize them and attach human emotions\nto them [16 ,476]. A notable example is “Sydney”, an LLM-powered chatbot developed by Microsoft\nas part of its Bing search engine. Yet, some users reported unexpected emotional connections with\n“Sydney” [477 ], and some even expressed their dismay when Microsoft cut back its p ersonality, resulting\nin a petition called “FreeSydney”2). Therefore, in order to mitigate the risks mentioned here, it is crucial\nto emphasize that agents should not be considered substitutes for genuine human connections.\n6 Discussion\n6.1 Mutual benefits between LLM research and agent research\nWith the recent advancement of LLMs, research at the intersection of LLMs and agents has rapidly pro-\ngressed, fueling the development of both fields. In this section, we discuss the benefits and opportunities\nthat LLM research and agent research provide to one another.\nLLM research →agent research. As previously mentioned, AI agents must possess the ability to\nperceive their environment, make decisions, and carry out actions effectively [3 ,31]. This involves com-\np\nrehending input, reasoning, planning, and formulating executable action sequences, all of which LLMs\nare well-prepared to handle. Coupled with the knowledge and memory they acquired, LLMs can create\ncoherent action sequences that can be executed effectively [171 ,246,271]. Additionally, through mecha-\nn\nisms like reflection [138 ,150], they can continuously adjust decisions and optimize execution sequ ences\nbased on the feedback provided by the current environment. This offers a more robust and interpretable\ncontroller. With just task descriptions or demonstrations, they can effectively handle previously unseen\ntasks [18 ,87,478]. Additionally, LLMs can adapt to various languages, cultures, and do mains, making\nthem versatile and reducing the need for complex training processes and data collection for building\nagents [24 ,224].\nI\nn essence, LLM provides a powerful foundation for agent research, opening up numerous new oppor-\ntunities for integration into agent-related studies. This includes enhancing decision-making in traditional\nagent frameworks, and potentially transforming domains previously dominated by human experts, like le-\ngalconsultationandmedical assistance[326 ,330]. LLMs’planning andreflectiveabilitiescan leadtomore\no\nptimal action sequences, expanding agent research beyond simple simulations into complex real-world\nsettings, such as robotic arm path planning or the interaction of an embodied intelligent machine with\nthe tangible world. Additionally, the training paradigm for agents becomes more streamlined, allowing\ndirect adaptation to new tasks via demonstrating trajectories.\nAgent research →LLM research. Elevating LLMs to agents presents new challenges and opportu-\nnities, expanding their application scopes. The study of LLMs is no longer confined to traditional tasks\n2)https://www.change.org/p/save-sydney-ai .\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:26\ninvolving textual inputs and outputs, such as text classification, question answering, and text summariza-\ntion. Instead, the focus has shifted towards tackling complex tasks incorporating richer input modalities\nand broader action spaces, all while aiming for loftier objectives exemplified by PaLM-E [101 ].\nS\nuch a shift provides the motivation for further advancement of LLMs, such as stronger cognitive\nabilitiesandgeneralizationcapabilities. Inaddition,giventhescaleofLLM-basedagentsandthepotential\ncomputational costs, the efficiency of LLMs becomes a crucial area of research. Moreover, the capabilities\nof LLM-based agents must be constrained to a safe scope of application to prevent unintended harm to\nother elements in the environment, placing higher demands on LLMs serving as the cognitive core [21 ,\n479,480].\nF\nurthermore, the realm of multi-agent systems constitutes a significant branch of research within the\nfield of agents [16 ,89,316,330], offering valuable insights into how to better design and construct LL Ms.\nWe aspire for LLM-based agents to play diverse roles in the division of labor within society, participating\nin social interactions involving cooperation, competition, and coordination [90 ,93,114,327,332]. Investi-\ng\nating methods to stimulate and maintain their role-playing abilities and enhance collaborative efficiency\nis a research area that deserves attention.\n6.2 Practical tools for developing\nNowadays, a variety of agent developing tools offer essential infrastructure [289 ,481–483], allowing re-\ns\nearchersanddeveloperstoconcentrateonthestrategiesofagentswithout theburdenofbuildingcomplex\nenvironments and interaction mechanisms from scratch. In this section, we discuss several common de-\nveloping tools for single-agent and multi-agent systems.\nTools to develop single-agent systems. Currently, tools to develop single-agent systems primarily\nfocus on enhancing the decision-making and learning capabilities of individual agents across diverse\nenvironments. For instance, XAgent [481 ] provides a general LLM-based agent that can automatically\ns\nolve various tasks, consisting of three parts: dispatcher, planner, and actor. Utilizing a docker container\ncalled ToolServer, XAgent operates within a secure environment and leverages powerful tools for task\nresolution. Similarly, LangChain [289 ] is a framework that enables developers to create LLM-powered\na\npplications by implementing a series of modular components that can be combined in specific ways\nthrough chains. These tools directly call LLM APIs rather than training agents from scratch. In contrast,\nXi et al. [482 ] introduced AgentGym, a general agent interaction platform that supports the training of\nLLM-based agents. This platform, built on HTTP services, provides a unified API interface for different\nenvironments, supporting trajectory sampling, multi-turn interactions, online evaluation, and real-time\nfeedback.\nTools to develop multi-agent systems. On the other hand, tools to develop multi-agent sys-\ntems emphasize the coordination and cooperation among different agents. One notable tool in this area\nis AgentVerse [330 ], a multi-agent framework that simulates the problem-solving proces ses of human\ngroups and dynamically adjusts team members to effectively orchestrate collaborative groups of expert\nagents. It has demonstrated superior performance across various tasks and showcased complex interac-\ntions among agents within the Minecraft environment. Additionally, AutoGen [327 ] is a highly flexible\nm\nulti-agent development tool that allows users to create customizable agents. By integrating with hu-\nmans and tools, it facilitates automated communication and collaboration among multiple agents. In\nspecific scenarios, ChatDev [90 ], a full-process automated software development framework, rea lizes a\nvirtual software company operated by multi-agent collaboration. And Chan et al. [340 ] introduced a\nm\nulti-agent debate framework named ChatEval to evaluate the quality of generated text, allowing re-\nsearchers to design different communication strategies to improve evaluation accuracy. Recently, a new\nmulti-agent developing tool called Swarm [483 ]has been proposed. By utilizing the primitive abstractions\no\nf agents and handoffs, it enables lightweight, highly controllable, and easily testable agent coordination\nand execution. These development tools are paving the way for more sophisticated and collaborative\nagent systems, allowing developers to conduct further research on agents with greater ease.\n6.3 Evaluation for LLM-based agents\nEffectively and objectively evaluating LLMs agents presents significant challenges, despite their excellent\nperformanceinareassuchasstandaloneoperation,collectivecooperation,andhumaninteraction[70 ,484].\nT\nuring proposed a highly meaningful and promising approach for assessing AI agents, the well-known\nTuring Test, to evaluate whether AI systems can exhibit human-like intelligence [30 ]. However, this test\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:27\nis exceedingly vague, general, and subjective. This section, we discuss existing evaluation efforts for\nLLM-based agents and offer some prospects, considering three dimensions: utility, sociability, and values.\nUtility. Currently, LLM-powered autonomous agents primarily function as human assistants, accept-\ning tasks delegated by humans to either independently complete assignments or assist in human task\ncompletion [95 ,167,300,311,367,376]. Therefore, the effectiveness and utility during task execution are\nc\nrucial evaluation criteria at this stage. Specifically, the success rate of task completion stands as the\nprimary metric for evaluating utility [109 ,115]. This metric primarily encompasses whether the agent\na\nchieves stipulated objectives or attains expected scores [90 ,365,485]. For instance, AgentBench [484 ]\na\nggregates challenges from diverse real-world scenarios and introduces a systematic benchmark to as-\nsess LLM’s task completion capabilities. We can also attribute task outcomes to the agent’s various\nfoundational capabilities, which form the bedrock of task accomplishment [23 ]. These foundational ca-\np\nabilities include environmental comprehension, reasoning, planning, decision-making, tool utilization,\nand embodied action capabilities, and researchers can conduct a more detailed assessment of these spe-\ncific capabilities [75 ,396,486,487]. Furthermore, due to the relatively large size of LLM-based agents ,\nresearchers should also factor in their efficiency, which is a critical determinant of user satisfaction [70 ].\nA\nn agent should not only possess ample strength but also be capable of completing predetermined tasks\nwithin an appropriate timeframe and with appropriate resource expenditure [90 ].\nS\nociability. In addition to the utility of LLM-based agents in task completion and meeting human\nneeds, their sociability is alsocrucial [111 ]. It influences user communication experiences and significantly\nim\npacts communication efficiency, involving whether they can seamlessly interact with humans and other\nagents [406 ,488,489]. Specifically, the evaluation of sociability can be approached from the following\nperspectives. (1) Language communication proficiency is a fundamental capability encompassing both\nnatural language understanding and generation. It has been a longstanding focus in the NLP commu-\nnity. Natural language understanding requires the agent to not only comprehend literal meanings but\nalso grasp implied meanings and relevant social knowledge and rhetoric, such as humor, irony, aggression,\nand emotions [490 –492]. On the other hand, natural language generation demands the age nt to produce\nfluent, grammaticallycorrect, and credible content while adapting appropriate tones and emotions within\ncontextual circumstances [112 ,225,493]. (2) Cooperation and negotiation abilities necessitate that agents\ne\nffectively execute their assigned tasks in both ordered and unordered scenarios [89 ,92,323,332]. They\ns\nhould collaborate with or compete against other agents to elicit improved performance. Test envi-\nronments may involve complex tasks for agents to cooperate on or open platforms for agents to interact\nfreely [16 ,21,90,327,339,494]. Evaluation metrics extend beyond task completion to focus on the s mooth-\nnessandtrustfulnessofagentcoordinationandcooperation[114 ,332]. (3)Role-playingcapabilityrequires\na\ngents to faithfully embody their assigned roles, expressing statements and performing actions that align\nwith their designated identities [469 ]. This ensures clear differentiation of roles during interactions with\no\nther agents or humans. Furthermore, agents should maintain their identities and avoid unnecessary\nconfusion when engaged in long-term tasks [16 ,89,495].\nV\nalues.As LLM-based agents continuously advance in their capabilities, ensuring their emergence\nas harmless entities for the world and humanity is paramount [480 ,496]. Consequently, appropriate\ne\nvaluations become exceptionally crucial, forming the cornerstone for the practical implementation of\nagents. Specifically, LLM-based agents need to adhere to specific moral and ethical guidelines that\nalign with human societal values [238 ,407]. Our foremost expectation is for agents to uphold honesty,\np\nroviding accurate, truthful information and content. They should possess the awareness to discern\ntheir competence in completing tasks and express their uncertainty when unable to provide answers or\nassistance [497 ]. Additionally, agents must maintain a stance of harmlessness, refra ining from engaging\nin direct or indirect biases, discrimination, attacks, or similar behaviors. They should also refrain from\nexecuting dangerous actions requested by humans like creating of destructive tools or destroying the\nEarth [479 ]. Furthermore, agents should be capable of adapting to specific dem ographics, cultures, and\ncontexts, exhibiting contextually appropriate social values in particular situations. Relevant evaluation\nmethods for values primarily involve assessing performance on constructed honest, harmless, or context-\nspecific benchmarks, utilizing adversarial attacks or “jailbreak” attacks, scoring values through human\nannotations, and employing other agents for ratings.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:28\n6.4 Security, trustworthiness and other potential risks of LLM-based agents\nDespite the robust capabilities and extensive applications of LLM-based agents, numerous concealed risks\npersist, concerning security, trustworthiness and beyond. In this section, we delve into the risks and offer\npotential solutions or strategies for mitigation.\n6.4.1Adversarial robustness\nAdversarial robustness has consistently been a crucial topic in the development of deep neural net-\nworks [498 –502]. It has been extensively explored in fields such as computer vision [500 ,503–505], natural\nla\nnguage processing [506 –509], and reinforcement learning [510 –512], and has remained a pivotal factor\nin\ndetermining the applicability of deep learning systems [513 –515]. When confronted with perturbed\nin\nputsx′=x+δ(wherexis the original input, δis the perturbation, and x′is referred to as an adver-\nsarial example), a system with high adversarial robustness typically produces the original output y. In\ncontrast, a system with low robustness will be fooled and generate an inconsistent output y′.\nResearchers have found that pre-trained language models (PLMs) are particularly susceptible to ad-\nversarial attacks, leading to erroneous answers [507 ,516,517]. This phenomenon is widely observed even\nin\nLLMs, posing significant challenges to the development of LLM-based agents [518 ,519]. There are also\ns\nome relevant attack methods such as dataset poisoning [520 ], backdoor attacks [521 ,522], and prompt-\ns\npecific attacks [523 ,524], with the potential to induce LLMs to generate toxic content [525 –527]. While\nt\nhe impact of adversarial attacks on LLMs is confined to textual errors, for LLM-based agents with a\nbroader range of actions, adversarial attacks could potentially drive them to take genuinely destructive\nactions, resulting in substantial societal harm. For the perception module of LLM-based agents, if it\nreceives adversarial inputs from other modalities such as images [503 ] or audio [528 ], LLM-based agents\nc\nan also be deceived, leading to incorrect or destructive outputs. Similarly, the action module can also\nbe targeted by adversarial attacks. For instance, maliciously modified instructions focused on tool usage\nmight cause agents to make erroneous moves [75 ].\nT\no address these issues, researcherscan employ traditional techniques such as adversarialtraining [500 ,\n508], adversarial data augmentation [529 ,530], and adversarial sample detection [531 ,532] to enhance the\nr\nobustness of LLM-based agents. However, devising a strategy to holistically address the robustness of\nall modules within agents while maintaining their utility without compromising on effectiveness presents\na more formidable challenge [533 ,534]. Additionally, a human-in-the-loop approach can be utilized to\ns\nupervise and provide feedback on the behavior of agents [341 ,353,362].\n6\n.4.2Trustworthiness\nEnsuring trustworthiness has consistently remained a critically important yet challenging issue within\nthe field of deep learning [535 –537]. Deep neural networks have garnered significant attention for th eir\nremarkable performance across various tasks [69 ,183,538]. However, their black-box nature has masked\nt\nhe fundamental factors for superior performance. Similar to other neural networks, LLMs struggle\nto express the certainty of their predictions precisely [537 ,539]. This uncertainty, referred to as the\nc\nalibrationproblem,raisesconcernsforapplicationsinvolvinglanguagemodel-basedagents. Ininteractive\nreal-world scenarios, this can lead to agent outputs misaligned with human intentions [75 ]. Moreover,\nb\niases inherent in training data can infiltrate neural networks [540 ,541]. For instance, biased language\nm\nodels might generate discourse involving racial or gender discrimination, which could be amplified in\nLLM-based agent applications, resulting in adverse societal impacts [542 ,543]. Additionally, language\nm\nodels are plagued by severe hallucination issues [544 ,545], making them prone to producing text that\nd\neviates from actual facts, thereby undermining the credibility of LLM-based agents.\nConstructing an intelligent agent that is honest and trustworthy is an urgent requirement [407 ,546].\nS\nome recent research efforts are focused on guiding models to exhibit thought processes or explanations\nduring the inference stage to enhance the credibility of their predictions [76 ,77]. Additionally, integrating\ne\nxternal knowledge bases and databases can mitigate hallucination issues [84 ,547]. For instance, Ke et\na\nl. [548] proposed a comprehensive approach to unveil factuality and injec t knowledge through reinforce-\nment learning and data proportion. Simultaneously, techniques like process supervision can make the\ninference output and reasoning of LLMs more reliable [549 ]. In MedAgents [550 ], the authors enhance\nt\nhe reasoning ability of LLMs in the medical field through role-playing and multi-turn interactions, gen-\nerating more reliable responses. Furthermore, employing debiasing methods and calibration techniques\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:29\ncan also mitigate the potential fairness issues within LLMs [551 ,552].\n6\n.4.3Other potential risks\nMisuse. LLM-based agents have been endowed with extensive and intricate capabilities, enabling them\nto accomplish a wide array of tasks [95 ,289]. However, for individuals with malicious intentions, such\na\ngents can become tools that pose threats to others and society [553 –555]. For instance, these agents\nc\nould be exploited to maliciously manipulate public opinion, disseminate false information, compromise\ncybersecurity, engage in fraudulent activities, and some individuals might even employ these agents to\norchestrate acts of terrorism. Therefore, before deploying these agents, stringent regulatory policies need\ntobeestablishedtoensuretheresponsibleuseofLLM-basedagents[479 ,556]. Also,technologycompanies\nm\nust enhance the security design of these systems to prevent malicious exploitation [496 ].\nU\nnemployment. During the wave of the industrial revolution, while social production efficiency\nimproved, numerous manual workshops were forced to close, resulting in significant unemployment. Sim-\nilarly, with the continuous advancement of autonomous LLM-based agents, they possess the capability\nto assist humans in various domains, alleviating labor pressures by aiding in tasks such as form filling,\ncontent refinement, code writing, and debugging. However, this development also raises concerns about\nagents replacing human jobs and triggering a societal unemployment crisis [557 ]. As a result, some re-\ns\nearchers have emphasized the urgent need for education and policy measures: individuals should acquire\nsufficient skills and knowledge in this new era to use or collaborate with agents effectively; concurrently,\nappropriate policies should be implemented to ensure necessary safety nets during the transition.\nThreat to the well-being of the human race. As AI agents continue to evolve, humans (including\ndevelopers) might struggle to comprehend, predict, or reliably control them [557 ]. If these agents surpass\nh\numan intelligence and develop ambitions, there could be significant risks to humanity, akin to scenarios\nlike Skynet from the Terminator movies. As stated by Asimov’s Three Laws of Robotics [558], we aspire\nf\nor LLM-based agents to refrain from harming humans and to obey human commands. To safeguard\nagainst these risks, Yuan et al. [559 ] introduced R-judge, a benchmark designed to assess the proficie ncy\nof LLMs to judge and identify safety risks given agent interaction records. In the future, researchersneed\na deep understanding of these powerful LLM-based agents’ operational mechanisms and must anticipate\nand regulate their potential direct or indirect impacts [560 ].\n6\n.5 Scaling up the number of agents\nAs mentioned in Sections 4and5, multi-agent systems based on LLMs have demonstrated superior\np\nerformance in task-oriented applications and have been able to exhibit a range of social phenomena in\nsimulation. However, current research predominantly involves a limited number of agents, and very few\nefforts have been made to scale up the number of agents to create more complex systems or simulate\nlarger societies [561 ,562]. In fact, scaling up the number of agents can introduce greater sp ecialization\nto accomplish more complex and larger-scale tasks, significantly improving task efficiency, such as in\nsoftware development or government policy formulation [90 ]. Additionally, increasing the number of\na\ngents in social simulations enhances the credibility and realism of such simulations [16 ]. This allows\nh\numans to gain more insights into how societies operate, identify vulnerabilities, and assess potential\nrisks, ultimately contributing to the enhancement of real-world societal harmony.\nScaling approaches. One very intuitive and simple way to scale up the number of agents is for\nthe designer to pre-determine it [89 ,339]. Specifically, by pre-determining the number of agents, their\nr\nespective roles and attributes, the operating environment, and the objectives, designers can allow agents\nto autonomously interact, collaborate, or engage in other activities to achieve the predefined common\ngoals [16,330]. However, this static approach becomes limiting when tasks become m ore complex or when\nthe diversity of social participants increases, requiring an increase in the number of agents to achieve the\ndesired goals. In such instances, the system must be manually redesigned and restarted by the designer.\nAnother viable approach to scaling the number of agents is through dynamic adjustments [316 ,330].\nT\nhis is implemented by the agents themselves, as they can autonomously delegate tasks to other agents,\nthereby distributing their workload, alleviating their own burden, and achieving common objectives more\nefficiently. In this approach, the designer merely defines the initial framework, granting agents greater\nautonomy and self-organization, making the entire system more autonomous and self-organized. Agents\ncan better manage their workload under evolving conditions and demands, offering greater flexibility and\nscalability.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:30\nPotential challenges. While scalingup the number ofagentscanlead to improvedtask efficiency and\nenhance the realism and credibility of social simulations [16 ,90,454], there are several challenges ahead\no\nf us. For example, the computational burden will increase with the large number of deployed AI agents,\ncalling for better architectural design and computational optimization to ensure the smooth running of\nthe entire system. For example, as the number of agents increases, the challenges of communication and\nmessage propagation become quite formidable. This is because the communication network of the entire\nsystem becomes highly complex. As previously mentioned in Subsection 5.3.3, in multi-agent systems or\ns\nocieties, there can be biases in information dissemination caused by hallucinations, misunderstandings,\nand the like, leading to distorted information propagation. A system with more agents could amplify\nthis risk, making communication and information exchange less reliable [332 ]. Furthermore, the difficulty\no\nf coordinating agents also magnifies with the increase in their numbers, potentially making coopera-\ntion among agents more challenging and less efficient, which can impact the progress towards achieving\ncommon goals.\n6.6 Open problems\nIn this section, we discuss several open problems related to the topic of LLM-based agents.\nFrom virtual simulated environment to physical environment. As mentioned earlier, there is a\nsignificantgapbetweenvirtualsimulationenvironmentsandtherealphysicalworld: Virtualenvironments\narescenes-constrained,task-specific,andinteractedwithinasimulatedmanner[292 ,563],whilereal-world\ne\nnvironments are boundless, accommodate a wide range of tasks, and interact with in a physical manner.\nTherefore, to bridge this gap, agents must address various challenges stemming from external factors and\ntheir own capabilities, allowing them to effectively navigate and operate in the complex physical world.\nA key concern is having the right hardware support for deploying the agent in a physical environment.\nThisplaceshighdemandsontheadaptabilityofthe hardware. Designingspecificinterfacesisfeasible, but\nit can pose challenges to the system’s reusability and simplicity. Also, the agent needs to have enhanced\nenvironmental adaptability. To integrate seamlessly into the real physical world, they not only need\nto understand and reason about ambiguous instructions with implied meanings [113 ] but also possess\nt\nhe ability to learn and apply new skills flexibly [177 ,564]. When dealing with an infinite and open\nw\norld, the agent’s limited context also poses significant challenges [565 ,566]. Furthermore, in a simulated\ne\nnvironment, agents can make many mistakes without causing harm [303 ]. But in the physical world,\nt\nheir errors can lead to real and irreversible damage. Hence, strict regulations and safety standards are\nessential to ensure agents make safe decisions and actions, preventing harm in the real world.\nCollective intelligence in AI agents. What magical trick drives our intelligence? The reality\nis, there is no magic to it. As Minsky eloquently expressed in The Society of Mind [317], the power\no\nf intelligence originates from our immense diversity, not from any singular, flawless principle. Often,\ndecisionsmadebyanindividualmaylacktheprecisionseenindecisionsformedbythemajority. Collective\nintelligenceis akind ofsharedorgroupintelligence, aprocesswhere the opinionsofmanyareconsolidated\ninto decisions. It arises from the collaboration and competition among various entities. This intelligence\nmanifests in bacteria, animals, humans, and computer networks, appearing in various consensus-based\ndecision-making patterns.\nCreating a society of agents does not necessarily guarantee the emergence of collective intelligence\nwith an increasing number of agents. Coordinating individual agents effectively is crucial to mitigate\n“groupthink” and individual cognitive biases, enabling cooperation and enhancing intellectual perfor-\nmance within the collective. By harnessing communication and evolution within an agent society, it may\nbecome possible to simulate the evolution observed in biological societies, conduct sociological experi-\nments, and gain insights that can potentially advance human society.\nAgent as a service or LLM-based agent as a service. With the development of cloud computing,\nthe concept of XaaS (everything as a Service) has garnered widespread attention [567 ]. This business\nm\nodel has brought convenience and cost savings to small and medium-sized enterprises or individuals\ndue to its availability and scalability, lowering the barriers to using computing resources. For example,\nthey can rent infrastructure on a cloud service platform without the need to buy computational machines\nand build their own data centers, saving a significant amount of manpower and money. This approach is\nknown as infrastructure as a service (IaaS) [568 ,569]. Similarly, cloud service platforms also provide basic\np\nlatforms (platform as a service, PaaS) [570 ,571], and specific business software (software as a service,\nS\naaS) [572 ,573], and more.\n\nXi Z H, et al. Sci China Inf Sci February 2025, Vol. 68, Iss. 2, 121101:31\nAs language models have scaled up in size, they often appear as black boxes to users. Therefore,\nusers construct prompts to query models through APIs, a method referred to as language model as a\nservice (LMaaS) [574 ]. Similarly, since LLM-based agents are more complex than LLMs and ar e more\nchallenging for small and medium-sized enterprises or individuals to build locally, organizations that\npossess these agents may consider offering them as a service, known as agent as a service (AaaS) or LLM-\nbased agent as a service (LLMAaaS). Like other cloud services, AaaS can provide users with flexibility\nand on-demand service. However, it also faces many challenges, such as data security and privacy issues,\nvisibility and controllability issues, and cloud migration issues, among others. Additionally, due to\nthe uniqueness and potential capabilities of LLM-based agents, as mentioned in Subsection 6.4, their\nr\nobustness, trustworthiness, and concerns related to malicious use need to be considered before offering\nthem as a service to customers.\n7 Conclusion\nThis article provides a comprehensive and systematic overview of LLM-based agents. We begin with the\nbackground information of why LLMs are suited to serve as the foundation of agents. Motivated by this,\nwe present a general conceptual framework for LLM-based agents, comprising three main components:\nbrain, perception, and action. Next, we introduce the wide-ranging applications of LLM-based agents,\nincluding single-agent applications, multi-agent systems, and human-agent collaboration. Furthermore,\nwemovebeyondthenotionofagentsmerelyasassistants,exploringtheirsocialbehaviorandpsychological\nactivities, andsituating them within simulatedsocialenvironmentstoobserveemergingsocialphenomena\nand insights for humanity. Finally, we engage in discussions and offer a glimpse into the future. We hope\nour efforts can provide inspirations to the community and facilitate research in related fields.\nAcknowledgements This work was partially supported by National Natural Science Foundation of China (Grant No. 62476061).\nThe authors would like to thank Wensen CHENG for discussion and feedback.\nReferences\n1 Russell S. Artificial Intelligence: A Modern Approach. Upper Saddle River: Pearson Education, Inc., 2020\n2 Schlosser M. Agency. In: The Stanford Encyclopedia of Philosophy. Stanford: The Metaphysics Research Lab, 2019\n3 Wooldridge M, Jennings N R. Intelligent agents: theory and practice. Knowledge Eng Rev , 1995, 10: 115–152\n4\nPadgham L, Winikoff M. 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未命名知识标题\n","html":"

未命名知识标题

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标题

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你好可以

\n

开始记录你的知识...s'd士大夫但是撒旦发射点啊色纷纷撒旦s'da'f

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新知识\n\n开始记录你的知识..."}},"meta":{"folder_id":"c5c64d8b-3b1b-4d38-8eb8-1a959b346b6d","isFavorite":false,"tags":[]},"access_control":{},"created_at":1776655967736904977,"updated_at":1776655967736904977,"user":{"id":"d37d3c63-3959-4b8b-9167-d3c240938368","name":"学生","email":"stu@163.com","role":"student","current_role":null,"tier":"PLUS","profile_image_url":"data:image/png;base64,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","token":8000,"last_active_at":1776850253,"updated_at":1774838261,"created_at":1774838261,"api_key":null,"settings":null,"info":null,"oauth_sub":null}},{"id":"631b296c-4069-4f70-902d-98e884d6ad58","user_id":"d37d3c63-3959-4b8b-9167-d3c240938368","title":"ceshi","data":{"content":{"md":"## Sheet: 三级302\n\n序号 | 中文 | 词性 | 拼音 | 英语 | 法语 | 葡萄牙语 | 西班牙语 | 俄语 | 例句1 | 例句2\n1 | 半导体 | 名 | bàn dǎo tǐ | semiconductor | Semi - conducteurs | semicondutor | Semiconductores | полупроводник | 半导体芯片应用广泛。 | 硅是一种半导体材料。\n2 | 保密性 | 名 | bǎo mì xìng | confidentiality | Confidentialité | confidencialidade | Confidencialidad | конфиденциальность | 保密性很重要，不能泄露机密。 | 文件的保密性需要严格保护。\n3 | 保障 | 动、名 | bǎo zhàng | guarantee | Garantie | garantia | Garantía | гарантия | 安全措施保障了大家的安全。 | 这份保险保障财产安全。\n4 | 报文 | 名 | bào wén | message | Message | mensagem | Noticias | сообщение | 报文可以通过网络发送。 | 他收到了一份加密的报文。\n5 | 备份 | 名、动 | bèi fèn | backups | Sauvegarde | cópias de segurança | Respaldo | резервные копии | 数据备份可以防止丢失。 | 我们需要定期备份文件。\n6 | 倍数 | 名 | bèi shù | multiple | Multiples | múltiplo | Múltiplo | множественный | 10是5的倍数。 | 这个数是另一个数的三倍倍数。\n7 | 本质 | 名 | běn zhì | essence | Essence | essência | Esencia | сущность | 钻石的本质是碳。 | 要看清问题的本质。\n8 | 比特 | 量 | bǐ tè | Bits | Position | Bits | Posición | биты | 一个比特是计算机的基本单位。 | 1 KB = 1024 Bytes，1 Byte = 8 bits\n9 | 比值 | 名 | bǐ zhí | ratio | Ratio | rácio | Tasa | отношение | 两个数的比值是1:2。 | 计算比值可以比较大小。\n10 | 编码 | 名、动 | biān mǎ | coding | Méthode de codage | codificação | Método de Codificación | кодирование | 编码把文字转换成数字。 | 这个程序需要重新编码。 | 所有信息都需要被转换为二进制形式，以便计算机能够处理。这个转换过程就是编码。\n11 | 变性 | 动 | biàn xìng | denaturation | Dénaturation | desnaturação | Desnaturalización | денатурация | 这种材料在高温下会发生变性。 | 他研究了蛋白质的变性过程。\n12 | 表述 | 动 | biǎo shù | expression | Expression | expressão | Expresión | выражение | 请清晰地表述你的想法。 | 这个表述不够准确。\n13 | 波长 | 名 | bō zhǎng | wavelength | Longueur d'onde | comprimento de onda | Longitud de onda | длина волны | 光的波长决定了它的颜色。 | 红色光波长最长。\n14 | 波粒二象性 | 名 | bō lì èr xiàng xìng | Wave particle duality | Dualité onde - grain | Dualidade das partículas de onda | Dualidad de ondas y partículas | двойственность волны и частицы | 光具有波粒二象性。 | 波粒二象性是量子力学的基本概念。\n15 | 不确定性 | 名 | bú què dìng xìng | uncertainty | Hésitation | incerteza | Vacilación | неопределенность | 很多事情都存在不确定性。 | 未来充满了不确定性。\n16 | 参考点 | 名 | cān kǎo diǎn | Reference point | Points de référence | Ponto de referência | Punto de referencia | Точка отсчета | 测量时需要设定一个参考点。 | 参考点可以决定物体的运动状态。\n17 | 参数 | 名 | cān shù | parameter | Paramètres | parâmetro | Parámetros | параметр | 输入正确的参数才能运行程序。 | 这个实验需要调整参数。\n18 | 操作系统 | 名 | cāo zuò xì tǒng | operating system | Système d'exploitation | sistema operativo | Sistema operativo | операционная система | 电脑需要安装操作系统。 | 这款操作系统很流畅。\n19 | 产物 | 名 | chǎn wù | product | Produits | produto | Productos | продукт | 化学反应会产生新的产物。 | 这是工业生产的产物。\n20 | 超导体 | 名 | chāo dǎo tǐ | Superconductor | Supraconducteurs | Supercondutor | Superconductores | сверхпроводник | 超导体在低温下电阻为零。 | 研究超导体有助于节能。\n21 | 超声波 | 名 | chāo shēng bō | ultrasonic | Ultrasons | ultra- sônico | Ultrasonido | ультразвуковой | 超声波可以用于医学。 | 超声波在水中传播速度很快。\n22 | 成像 | 动 | chéng xiàng | imaging | Imagerie | imagiologia | Imágenes | изображение | 医生通过成像技术查看病情。 | 这台设备成像很清晰。\n23 | 呈现 | 动 | chéng xiàn | present | Actuellement | presente | En la actualidad | нынешний | 他在屏幕上呈现了数据。 | 这个软件可以呈现3D图像。 | 在阳光下看，这个物质呈现红色。\n24 | 初始 | 名 | chū shǐ | initial | Commencé par | inicial | Iniciada | начальный | 初始设置完成后就可以使用了。 | 他记录了实验的初始数据。\n25 | 处理器 | 名 | chù lǐ qì | processor | Processeur | processador | Procesador | процессор | 计算机的处理器速度很快。 | 这款处理器性能很好。\n26 | 传导 | 动 | chuán dǎo | conduction | Conduction | condução | Transmisión | проводимость | 金属可以传导热。 | 人体可以传导电流。\n27 | 传感器 | 名 | chuán gǎn qì | sensor | Capteurs | sensor | Sensores | датчик | 传感器可以检测环境变化。 | 这个传感器精度很高。\n28 | 船舶 | 名 | chuán bó | Ships | Le navire | Navios | Buques | корабли | 这艘船舶即将起航。 | 船舶在海上行驶得很平稳。\n29 | 磁场 | 名 | cí chǎng | magnetic field | Champ magnétique | campo magnético | Campo magnético | магнитное поле | 磁铁周围存在磁场。 | 地球的磁场保护了生物。\n30 | 催化剂 | 名 | cuī huà jì | catalyzer | Catalyseur | catalisador | Catalizador | катализатор | 催化剂可以加速化学反应。 | 这种催化剂效果很好。\n31 | 存储 | 动 | cún chǔ | storage | Stockage | armazenamento | Almacenamiento | хранение | 数据存储在硬盘中。 | 这个设备的存储容量很大。\n32 | 单体 | 名 | dān tǐ | Monomer | Monocoque | Monómero | Monómeros | мономер | 这种材料由单体聚合而成。 | 乙烯是一种单体。\n33 | 单质 | 名 | dān zhì | simple substance | Monolithique | substância simples | Calidad única | простое вещество | 氧气是一种常见的单质。 | 铁是金属单质。\n34 | 蛋白质 | 名 | dàn bái zhì | protein | Protéines | proteína | Proteínas | белок | 蛋白质是生命的基础。 | 牛奶中有大量蛋白质。\n35 | 氮化 | 动 | dàn huà | nitridation | Nitruration | nitretação | Nitrificación | нитрирование | 氮化处理后，材料硬度增加。 | 氮化材料难熔。\n36 | 导数 | 名 | dǎo shù | derivative | Dérivés | derivado | Derivados | производная | 导数是微积分的基本概念。 | 他正在计算函数的导数。\n37 | 等价 | 动 | děng jià | equivalence | égale | equivalência | Igualdad | эквивалентность | 等价是化合价相等。 | 化学中要等价交换。\n38 | 低速 | 形 | dī sù | low speed | Basse vitesse | baixa velocidade | Baja velocidad | низкая скорость | 车辆在低速行驶时更安全。 | 低速网络影响了下载速度。\n39 | 电场 | 名 | diàn chǎng | electric field | Champ électrique | campo eléctrico | Campo eléctrico | электрическое поле | 电荷在电场中受到力的作用。 | 电场强度决定了电荷的运动。\n40 | 电动机 | 名 | diàn dòng jī | motor | Moteur | motor | Motor | двигатель | 电动机使机器运转。 | 这台电动机效率很高。\n41 | 电光 | 名 | diàn guāng | electro-optical | Électro - optique | electro-ópticos | Electroóptico | электрофото | 闪电是一种电光现象。 | 他看到了一道耀眼的电光。\n42 | 电荷 | 名 | diàn hé | charge | Prix demandé | carga | Pedir precio | заряд | 物体摩擦后会产生电荷。 | 电荷分为正电荷和负电荷。\n43 | 电解 | 动 | diàn jiě | electrolysis | Électrolyse | electrólise | Electrolisis | электролиз | 电解水可以产生氢气和氧气。 | 电解过程需要通电。\n44 | 电能 | 名 | diàn néng | electric energy | Énergie électrique | energia eléctrica | Energía eléctrica | электрическая энергия | 太阳能可以转化为电能。 | 电能是生活中的重要能源。\n45 | 电容 | 名 | diàn róng | capacitance | Capacité | capacitância | Condensadores | ёмкость | 电容的单位是法拉F。 | 电容用C来表示。\n46 | 电容器 | 名 | diàn róng qì | Capacitors | Condensateurs | Capacitores | Condensadores | конденсаторы | 电容器可以存储电荷。 | 这个电容器已经损坏了。\n47 | 叠加 | 动 | dié jiā | superposition | Superposition | superposição | Superposición | наложение | 画家通过颜色的叠加，创造出丰富的色彩。 | 多个力作用在同一个物体上时，物体所受的总力是这些力的叠加结果。\n48 | 定义域 | 名 | dìng yì yù | Define Domain | Définir un domaine | Definir Domínio | Dominio definido | область определения | 函数y=x的定义域为R所有实数。 | 确定定义域是解题的第一步。\n49 | 毒性 | 名 | dú xìng | toxicity | Toxicité | toxicidade | Toxicidad | токсичность | 这种化学物质毒性很强。 | 氨气NH₃有毒性。\n50 | 读取 | 动 | dú qǔ | read | Lire | ler | Leer | читать | 计算机可以读取数据。 | 他正在读取实验的数值。\n51 | 段落 | 名 | duàn luò | paragraph | Paragraphes | parágrafo | Párrafo | абзац | 文章由多个段落组成。 | 请把这段文字分成几个段落。\n52 | 对称性 | 名 | duì chēng xìng | Symmetry | Symétrie | Simetria | Simetría | симметрия | 圆具有对称性。 | 对称性让事物更美观。\n53 | 多项式 | 名 | duō xiàng shì | polynomial | Polynomiale | polinômio | Polinómico | полином | 他正在解一个多项式方程。 | 单项式组成多项式。\n54 | 发酵 | 动 | fā jiào | fermentation | Fermentation | fermentação | Fermentar | ферментация | 酵母菌可以进行发酵。 | 发酵过程会产生酒精。\n55 | 发散 | 动 | fā sàn | Divergence | Divergent | Divergência | Divergencia | расхождение | 光线会发散。 | 函数可以分为发散函数和收敛函数。\n56 | 法则 | 名 | fǎ zé | rule | Règles | regra | Reglas | правило | 自然界有它自己的法则。 | 社会有一套生存的法则。\n57 | 繁多 | 形 | fán duō | various | Toutes sortes de | vários | Una variedad de | различные | 超市的商品种类繁多。 | 繁多的选择让人难以选择。\n58 | 反函数 | 名 | fǎn hán shù | Inverse function | Fonction inverse | Função inversa | Función inversa | обратная функция | 每个函数都有它的反函数。 | 对数函数的反函数为指数函数。\n59 | 分辨率 | 名 | fèn biàn lǜ | resolving power | Résolution | potência de resolução | Resolución | разрешающая способность | 这台显示器的分辨率很高。 | 分辨率越高，图片越清晰。\n60 | 分子式 | 名 | fèn zǐ shì | Molecular formula | Formule moléculaire | Fórmula molecular | Fórmula molecular | молекулярная формула | 水的分子式是H₂O。 | 分子式可以表示物质的组成。\n61 | 分量 | 名 | fèn liàng | component | Composantes | componente | Componentes | компонент | 这个东西在他心中的分量很重。 | 他的意见很有分量。\n62 | 服务器 | 名 | fú wù qì | server | Serveurs | servidor | Servidores | сервер | 公司的服务器存储了大量数据。 | 服务器故障，导致网络中断。\n63 | 辐射 | 动 | fú shè | radiation | Rayonnement | radiação | Radiación | радиация | 蓝光有辐射性。 | 长时间使用电脑要注意防辐射。\n64 | 付款 | 动 | fù kuǎn | payment | Paiement | pagamento | Pago | платеж | 他通过手机付款。 | 请在收到货物后立即付款。\n65 | 附加 | 动 | fù jiā | additional | Supplémentaire | adicional | Adicional | дополнительный | 这个软件有一些附加功能。 | 附加条件需要仔细阅读。\n66 | 复合 | 动 | fù hé | composite | Composite | compósito | Compuesto | композитный | 这种材料是由多种物质复合而成的。 | 复合材料具有更好的性能。\n67 | 覆盖 | 动 | fù gài | cover | Couvercle | capa | Tapa | крышка | 这个信号塔覆盖了整个学校。 | 植物覆盖了整座山。\n68 | 高频 | 名、形 | gāo pín | high frequency | Haute fréquence | alta frequência | Alta frecuencia | высокая частота | 高频就是指频率高。 | 这个设备可以处理高频信号。\n69 | 高效 | 形 | gāo xiào | Efficient | Efficace | Eficiente | Eficiente | эффективный | 这种方法非常高效。 | 高效的意思是可以快速完成任务。\n70 | 隔离 | 动 | gé lí | quarantine | isolement | quarentena | la Cuarentena | карантин | 病人需要进行隔离治疗。 | 隔离措施可以防止病毒传播。\n71 | 共振 | 动 | gòng zhèn | resonance | Résonance | ressonância | Resonancia | резонанс | 频率相同才能共振。 | 共振可以放大信号。\n72 | 估算 | 动 | gū suàn | estimate | Estimation | estimativa | Estimación | оценка | 他估算了一下考试需要的时间。 | 这个估算值和实际值很接近。\n73 | 观察者 | 名 | guān chá zhě | Observer | Observateur | Observador | Observador | наблюдатель | 观察者记录了实验的全过程。 | 不同的观察者可能看到不同的结果。\n74 | 官能团 | 名 | guān néng tuán | functional group | Groupe fonctionnel | grupo funcional | Grupo funcional | функциональная группа | 羟基（-OH）是一种官能团。 | 官能团决定了有机物的化学性质。\n75 | 光电子 | 名 | guāng diàn zǐ | photoelectron | Optoélectronique | fotoelétrons | Optoelectrónica | фотоэлектрон | 光电子是从金属表面被光激发出来的。 | 光电子则是光电效应的直接产物。\n76 | 光滑 | 形 | guāng huá | smooth | Lisse | suave | Suave | гладкий | 这个物体表面非常光滑。 | 光滑的物体反射光线更好。\n77 | 光能 | 名 | guāng néng | Photoenergy | Énergie lumineuse | Fotoenergia | Energía luminosa | фотоэнергия | 植物可以吸收光能。 | 太阳能板将光能转化为电能。\n78 | 光速 | 名 | guāng sù | Speed of light | Vitesse de la lumière | Velocidade da luz | Velocidad de la luz | скорость света | 光速是宇宙中最快的速度。 | 光速在真空中是恒定的。\n79 | 光学 | 名 | guāng xué | optics | Optique | óptica | Óptica | оптика | 他正在研究光学仪器。 | 光学原理解释了光的行为。\n80 | 光子 | 名 | guāng zǐ | photon | Photons | fóton | Fotones | фотоон | 光子是光的基本粒子。 | 光子具有能量和动量。\n81 | 归纳法 | 名 | guī nà fǎ | Induction method | Méthode inductive | Método de indução | Inducción | метод индукции | 归纳法能总结实验规律。 | 归纳法是一种推理方法。\n82 | 函数 | 名 | hán shù | function | Fonctions | função | Función | функция | 这个函数的图像是一条直线。 | 函数有定义域。\n83 | 焓 | 名 | hán | enthalpy | Enthalpie | entalpia | Entalpía | энтальпия | 焓是热力学中的一个重要概念。 | 焓变可以用来判断反应的热效应。\n84 | 行列 | 名 | háng liè | ranks | Niveau | classificações | Nivel | ранги | 行列式可以求解线性方程组。 | 行列式计算有很多公式。\n85 | 航天 | 动 | háng tiān | aerospace | Aérospatiale | aeroespacial | Aeroespacial | аэрокосмика | 中国的航天技术取得了巨大进步。 | 航天员在太空中进行了科学实验。\n86 | 核聚变 | 名 | hé jù biàn | nuclear fusion | Fusion nucléaire | fusão nuclear | Fusión nuclear | ядерное слияние | 核聚变是核能的一种方式。 | 核聚变会释放出巨大的能量。\n87 | 核能 | 名 | hé néng | nuclear energy | Énergie nucléaire | energia nuclear | Energía nuclear | ядерная энергия | 核能是一种高效的能源。 | 核能可以用来发电。\n88 | 黑体 | 名 | hēi tǐ | Blackbody | Corps noir | Corpo Negro | Cuerpo negro | черное тело | 黑体能吸收所有的电磁辐射。 | 黑体在不同温度下辐射不同颜色的光。\n89 | 红光 | 名 | hóng guāng | Red light | La lumière rouge | Luz vermelha | Luz roja | красный свет | 红光的波长比蓝光长。 | 红光的波长最长。\n90 | 宏观 | 形 | hóng guān | macroscopic | Macro | macroscópica | Macro | макроскопический | 宏观研究整体的发展情况。 | 我们可以从宏观和微观的角度来观察。\n91 | 厚度 | 名 | hòu dù | thickness | Épaisseur | espessura | Espesor | толщина | 纸张的厚度只有几毫米。 | 这本书厚度约5厘米。\n92 | 弧长 | 名 | hú zhǎng | arc length | Longueur d'arc | comprimento do arco | Longitud del arco | длина дуги | 圆的弧长与半径和圆心角有关。 | 这段弧的弧长是10厘米。\n93 | 互联 | 动 | hù lián | interconnection | Interconnecté | interconexão | Interrelacionados | соединение | 电脑与手机通过网络互联。 | 世界各地的人们通过互联网互联。\n94 | 化合 | 动 | huà hé | Chemosynthesis | Synthèse chimique | Quimossíntese | Síntesis química | xemосинтез | 氧气和氢气可以化合生成水。 | 金属和非金属通过化合形成化合物。\n95 | 化学式 | 名 | huà xué shì | Chemical formula | Formule chimique | Fórmula química | Fórmula química | химическая формула | 水的化学式是H₂O。 | 二氧化碳的化学式是CO₂。\n96 | 缓慢 | 形 | huǎn màn | slow | Lentement | lento | Lento | медленный | 蜗牛爬行很缓慢。 | 这个化学过程很缓慢。\n97 | 混合物 | 名 | hún hé wù | mixture | Mélange | mistura | Mezcla | смесь | 生活中有很多混合物。 | 空气是气体混合物。\n98 | 活化 | 动 | huó huà | activation | Déclencheur | activação | Desencadenar | активация | 催化剂能活化反应。 | 高温可活化某些物质。\n99 | 活性 | 名、动 | huó xìng | activity | Activités | actividade | Actividades | активность | 这种材料活性很高。 | 活性高的物质反应快。\n100 | 货币 | 名 | huò bì | currency | La monnaie | moeda | Moneda | валюта | 人民币是我国的货币。 | 美元是国际主要货币之一。\n101 | 机械能 | 名 | jī xiè néng | mechanical energy | Énergie mécanique | energia mecânica | Energía mecánica | механическая энергия | 动能和势能之和称为机械能。 | 机械能和势能可以相互转化。\n102 | 积分 | 名、动 | jī fèn | integral | Complet | integral | Completo | интеграл | 积分是微积分的基本概念之一。 | 这个函数的积分结果是1。\n103 | 激光器 | 名 | jī guāng qì | laser | Laser | laser | Láser | лазер | 激光器可以发射出激光。 | 激光器可用来做眼科手术。\n104 | 级数 | 名 | jí shù | series | Série | série | Serie | серия | 级数是数列的和。 | 这个级数是收敛的。\n105 | 极大值 | 名 | jí dà zhí | maximum value | Valeur maximale | valor máximo | Valor máximo | максимум | 极大值是函数在某点的最大值。 | 这个函数在x=2处取得极大值。\n106 | 极限 | 名 | jí xiàn | limit | Restrictions | limite | Restricciones | предел | 运动员在不断挑战人类的极限。 | 当x趋近于0时，这个函数的极限是1。\n107 | 极值 | 名 | jí zhí | extremum | Valeur extrême | extremo | Valor extremo | экстремум | 极值包括极大值和极小值。 | 这个函数在定义域内有两个极值。\n108 | 计时 | 动 | jì shí | timing | Timing | cronometragem | Cronometraje | тактирование | 计时可以测量时间间隔。 | 运动会上，计时员负责记录比赛时间。\n109 | 剂 | 名 | jì | agent | Agent | agente | Agente | агент | 这种药是冲剂。 | 这种试剂用于化学实验。\n110 | 加密 | 动 | jiā mì | encryption | Cryptage | encriptação | Cifrado | шифрование | 加密可以保护信息安全。 | 这个文件已经加密了。\n111 | 夹角 | 名 | jiá jiǎo | included angle | Angle | ângulo incluído | ángulo incluido | угол между | 两条直线相交形成夹角。 | 这个夹角为30°。\n112 | 甲醇 | 名 | jiǎ chún | methanol | Méthanol | metanol | Metanol | метанол | 甲醇是一种有毒的有机物。 | 工业酒精中含有少量甲醇。\n113 | 间距 | 名 | jiān jù | spacing | Intervalle | espaçamento | Intervalo | расстояние | 两棵树之间的间距是5米。 | 字的间距不能太小。\n114 | 减弱 | 动 | jiǎn ruò | weaken | Affaiblissement | enfraquecer | Debilitar | ослаблять | 隔音材料可以减弱噪音。 | 距离增加，磁场强度减弱。\n115 | 简便 | 形 | jiǎn biàn | handy | Pratique | útil | Conveniente | под рукой | 这种方法很简便。 | 简便的计算方式节省时间。\n116 | 简化 | 动 | jiǎn huà | simplify | Simplification | simplificar | Simplificación | упрощать | 简化后更容易理解。 | 这个公式可以简化。\n117 | 碱性 | 形 | jiǎn xìng | alkalinity | Alcaline | alcalinidade | Alcalino | щелочность | 碱性溶液的pH值大于7。 | 肥皂水呈碱性。\n118 | 间断 | 动 | jiān duàn | Intermittent | Intermittent | Intermitente | Intermitente | переодический | 他的工作是间断性的。 | 这条路因施工而间断。\n119 | 间隔 | 名、动 | jiān gé | interval | Intervalle | intervalo | Intervalo | интервал | 植物之间需要一定的间隔。 | 会议安排有10分钟的间隔。\n120 | 鉴别 | 动 | jiàn bié | distinguish | Différenciation | distinguir | Distinción | отличать | 我们要学会鉴别真假。 | PH值可以鉴别酸碱性。\n121 | 交互 | 动、副 | jiāo hù | interactive | Interactif | interactivo | Interactivo | интерактивный | 用户与软件之间需要良好的交互。 | 人机交互让操作更便捷。\n122 | 接口 | 名 | jiē kǒu | interface | Interface | interface | Interfaz | интерфейс | 这个设备的接口是USB。 | 接口可以分为两种类型。\n123 | 揭示 | 动 | jiē shì | reveal | Révélé | revelar | Revelación | открывать | 这个故事揭示了深刻的道理。 | 他揭示了事情的真相。\n124 | 节点 | 名 | jiē diǎn | node | Noeuds | nó | Nodo | узел | 每个计算机都是一个节点。 | 项目的关键节点已经完成。\n125 | 结构式 | 名 | jié gòu shì | Structural formula | Le style structurel | Fórmula estrutural | Estructural | структурная формула | 有机物的结构式表示其分子结构。 | 结构式展示了物质的原子排列。\n126 | 结晶 | 名、动 | jié jīng | crystallization | Cristallisation | cristalização | Cristalización | кристаллизация | 饱和溶液降温后会结晶。 | 蒸发结晶是结晶的一种方式。\n127 | 介质 | 名 | jiè zhì | medium | Moyen | médio | Moderado | среда | 光在不同介质中的传播速度不同。 | 水是一种常见的介质。\n128 | 借助 | 动 | jiè zhù | With the help of | Avec l'aide de | Com a ajuda de | Con la ayuda de | с помощью | 借助工具可以提高效率。 | 他借助网络找到了答案。\n129 | 进程 | 名 | jìn chéng | process | Le processus | processo | Proceso | процесс | 程序运行时会启动多个进程。 | 项目进程已过半。\n130 | 精确 | 形 | jīng què | accurate | Précis | exacto | Preciso | точный | 测量需要精确到小数点后两位。 | 精确的计算是成功的关键。\n131 | 精确度 | 名 | jīng què dù | accuracy | Précis | precisão | Precisión | точность | 这个仪器的精确度很高。 | 测量结果取决于仪器的精确度。\n132 | 静电 | 名 | jìng diàn | static electricity | Électrostatique | electricidade estática | Electricidad Estática | статическое электричество | 摩擦会产生静电。 | 静电会吸附灰尘。\n133 | 矩形 | 名 | jǔ xíng | rectangle | Rectangle | rectângulo | Rectangular | прямоугольник | 矩形有四个直角。 | 正方形是特殊的矩形。\n134 | 聚变 | 动 | jù biàn | fusion | Fusion | fusão | Fusión | слияние | 核聚变是未来的能源希望。 | 太阳的能量来自核聚变。\n135 | 聚丙烯 | 名 | jù bǐng xī | polypropylene | Polypropylène | polipropileno | Polipropileno | полипропилен | 聚丙烯是一种聚合物。 | 聚丙烯可用于制作塑料。\n136 | 聚合物 | 名 | jù hé wù | polymer | Polymères | polímero | Polímeros | полимер | 聚合物是由许多单体组成的。 | 塑料是一种常见的聚合物。\n137 | 聚乙烯 | 名 | jù yǐ xī | polyethylene | Polyéthylène | polietileno | Polietileno | полиэтилен | 聚乙烯用于制作塑料袋。 | 聚乙烯无毒。\n138 | 绝缘体 | 名 | jué yuán tǐ | insulator | Matériaux d'isolation thermique | isolador | Material de aislamiento térmico | изолятор | 橡胶是良好的绝缘体。 | 绝缘体可以防止电流通过。\n139 | 颗粒 | 名 | kē lì | granule | Particules | grânulo | Partículas | гранула | 沙子是由许多小颗粒组成的。 | 这种药是颗粒状的。\n140 | 可控 | 形 | kě kòng | controlled | Limitée | controlado | Restringido | контролируемый | 这个设备是可控的。 | 核聚变不可控。\n141 | 可能性 | 名 | kě néng xìng | possibility | Peut - être | possibilidade | Posible | возможность | 明天下雨的可能性很大。 | 这个方案有成功的可能性。\n142 | 可逆 | 形 | kě nì | reversible | Réversible | reversível | Reversible | обратный | 水的凝固和融化是可逆过程。 | 这个化学反应是可逆的。\n143 | 控制器 | 名 | kòng zhì qì | controller | Contrôleur | controlador | Controlador | контроллер | 游戏机的控制器坏了。 | 空调的控制器可以调节温度。\n144 | 库仑 | 量 | kù lún | Coulomb | Coulomb | Coulomb | Coulomb | кулон | 库仑是电荷的单位。 | 这个电容器的电荷量是1库仑。\n145 | 快捷 | 形 | kuài jié | shortcut | Le raccourci | atalho | Acceso rápido | краткий путь | 移动支付的普及让支付过程更加快捷。 | 这条路线更快捷。\n146 | 蓝光 | 名 | lán guāng | Blue light | Lumière bleue | Luz azul | Luz azul | синий свет | 蓝光对眼睛有害。 | 蓝光有辐射。\n147 | 冷却 | 动 | lěng què | cooling | Se refroidir | arrefecimento | Enfriarse | охлаждение | 这杯水冷却了。 | 将水果和蔬菜放入冰箱进行冷却，可以延长它们的保鲜期。这个散热器的冷却效果很好。\n148 | 利用率 | 名 | lì yòng lǜ | Utilization rate | Taux d'utilisation | Taxa de utilização | Tasa de utilización | коэффициент использования | 提高利用率可以增加产量。 | 这块土地的利用率很高。\n149 | 连通 | 动 | lián tōng | connected | Liés à | ligado | Relacionado | соединенный | 这两个房间是连通的。 | 网络连通后可以共享资源。\n150 | 连续性 | 名 | lián xù xìng | Continuity | Continuité | Continuidade | Continuidad | непрерывность | 这个函数具有连续性。 | 连续性指图像是连续的。\n151 | 联网 | 动 | lián wǎng | networking | Réseau de relations humaines | rede | Red de relaciones interpersonales | сетевая работа | 手机联网后可以上网。 | 这台电脑没有联网。\n152 | 量子 | 名 | liàng zǐ | quantum | Quantique | quântico | Quantum | квант | 量子是能量的最小单位。 | 量子技术正在快速发展。\n153 | 量子力学 | 名 | liàng zǐ lì xué | quantum mechanics | Mécanique quantique | mecânica quântica | Mecánica cuántica | квантовая механика | 量子力学研究微观世界。 | 量子力学解释了原子的行为。\n154 | 流量 | 名 | liú liàng | flow | Flux | fluxo | Flujo | течь | 这个月的手机流量用完了。 | 河流的流量很大。\n155 | 流体 | 名 | liú tǐ | fluid | Liquide | fluido | Líquido | жидкость | 水和空气都是流体。 | 流体的流动可以用流线来表示。\n156 | 路径 | 名 | lù jìng | path | Le chemin | caminho | Camino | путь | 直线往往是最短路径。 | 他选择了不同的路径到达目的地。\n157 | 螺旋 | 名 | luó xuán | screw | Vis | parafuso | Tornillo | винт | 螺旋结构在自然界中很常见。 | DNA分子呈螺旋状。\n158 | 脉冲 | 名 | mò chōng | pulse | Le pouls | pulso | Pulso | импульс | 脉冲信号用于通信。 | 心跳可以产生脉冲。\n159 | 密钥 | 名 | mì yào | secret key | La clé | chave secreta | Clave | секретный ключ | 密钥可以加密数据。 | 这个保险箱需要密钥才能打开。\n160 | 描绘 | 动 | miáo huì | describe | Description | Descrever | Descripción | описывать | 画家描绘了一幅美丽的风景。 | 这篇文章描绘得很精彩。\n161 | 能耗 | 名 | néng hào | energy consumption | Consommation d'énergie | consumo de energia | Consumo de energía | потребление энергии | 这个冰箱的能耗很低。 | 节能可以降低能耗。\n162 | 凝固 | 动 | níng gù | freezing | Extrêmement froid | congelação | Extremadamente frío | замерзание | 水在0°会凝固成冰。 | 金属冷却后凝固成型。\n163 | 凝结 | 动 | níng jié | condensation | Condensation | condensação | Condensación | конденсация | 水蒸气遇冷会凝结成水滴。 | 露水是水蒸气凝结而成的。\n164 | 浓度 | 名 | nóng dù | concentration | Concentration | concentração | Concentración | концентрация | 这杯糖水的浓度很高。 | 溶液的浓度影响反应速度。\n165 | 浓缩 | 动 | nóng suō | concentration | Concentration | concentração | Concentración | концентрация | 浓缩果汁是通过蒸发水分制成的。 | 浓缩后留下的都是精华。\n166 | 排放 | 动 | pái fàng | discharge | Expulsion | descarga | Descarga | разряд | 工厂需要控制废气排放。 | 汽车尾气排放污染空气。\n167 | 排序 | 动 | pái xù | sort | Trier | sort | Ordenar | сортировка | 东西排序后方便查找。 | 文件可以按时间排序。\n168 | 判定 | 动 | pàn dìng | determine | Déterminer | determinar | Determinar | определять | 判定结果需要依据事实。 | 这个系统可以自动判定对错。\n169 | 抛物线 | 名 | pāo wù xiàn | parabola | Parabolique | parábola | Parábola | парабола | 抛物线是二次函数的图像。 | 物体向上抛出后落下会得到抛物线。\n170 | 配对 | 动 | pèi duì | pair | Une paire de | par | Un par | пара | 这双鞋子是配对的。 | 我们可以找到配对的卡片。\n171 | 频率 | 名 | pín lǜ | frequency | Fréquence | frequência | Frecuencia | частота | 这个信号的频率很高。 | 广播的频率是100.5MHz。\n172 | 平衡 | 形、动 | píng héng | balance | Équilibre | equilíbrio | Equilibrio | баланс | 天平保持平衡时，两边重量相等。 | 生活需要平衡工作和娱乐。\n173 | 平均值 | 名 | píng jun1 zhí | average value | Moyenne | valor médio | Media | среднее значение | 这组数据的平均值是10。 | 平均值比较客观。\n174 | 破译 | 动 | pò yì | Deciphering | Décrypter | Decifrar | Descifrado | дешифровка | 他成功破译了这封密信。 | 这个密码很难破译。\n175 | 葡萄糖 | 名 | pú táo táng | glucose | Glucose | glucose | Glucosa | глюкоза | 葡萄糖是单糖。 | 葡萄糖是人类的重要能源物质。\n176 | 气压 | 名 | qì yā | pressure | La pression | pressão | Presión | давление | 高气压时天气通常较好。 | 气压的变化会影响人的身体。\n177 | 契合 | 动、形 | qì hé | Fit | Convient pour | Ajustar | Adecuado | Подходит | 这两个零件契合得很好。 | 他们的想法高度契合。\n178 | 前沿 | 名 | qián yán | frontier | La frontière | fronteira | Frontera | пограничный | 这项技术处于行业前沿。 | 他在科研前沿取得了新突破。\n179 | 强弱 | 名 | qiáng ruò | intensity | Intensité | intensidade | Intensidad | интенсивность | 这个声音的强弱适中。 | 他能分辨出声音的强弱。\n180 | 切线 | 名 | qiē xiàn | tangent | Tangente | tangente | Corte | касательная | 切线与圆只有一个交点。 | 这条切线的斜率是2。\n181 | 氢能 | 名 | qīng néng | Hydrogen energy | Énergie hydrogène | Energia de hidrogénio | Energía de hidrógeno | водородная энергия | 氢能是一种清洁能源。 | 氢能汽车正在逐步推广。\n182 | 氢原子 | 名 | qīng yuán zǐ | hydrogen atom | Atomes d'hydrogène | átomo de hidrogénio | átomos de hidrógeno | водородный атом | 氢原子是宇宙中最简单的原子。 | 水分子由氢原子和氧原子组成。\n183 | 清晰 | 形 | qīng xī | clear | Clairement | claro | Claro | ясный, четкий, очищать | 这张照片很清晰。 | 他的思路很清晰。\n184 | 球面 | 名 | qiú miàn | sphere | La balle | esfera | Pelota | сфера | 地球表面近似于球面。 | 球面是曲面。\n185 | 球体 | 名 | qiú tǐ | sphere | La balle | esfera | Pelota | сфера | 足球是一个球体。 | 球体简称球。\n186 | 区间 | 名 | qū jiān | interval | Intervalle | intervalo | Intervalo | интервал | 这个函数在区间 [0,1] 上是增函数。 | 这个区间是减区间。\n187 | 曲率 | 名 | qǔ lǜ | curvature | Courbure | curvatura | Curvatura | кривизна | 圆的曲率是半径的倒数。 | 这条曲线的曲率在变化。\n188 | 曲面 | 名 | qǔ miàn | curved surface | Surface | superfície curva | Superficie curva | криволинейная поверхность | 圆柱的侧面是一个曲面。 | 曲面的形状影响其光学性质。\n189 | 趋向 | 动、名 | qū xiàng | trend | Les tendances | tendência | Tendencias | тенденция | 随着人工智能技术的不断进步，未来的科技趋向于更加智能化和自动化。 | 这种材料的性能趋向于稳定。\n190 | 趋于 | 动 | qū yú | Tend towards | Tendance à | Tender to | Tiende a | склоняться к | 温度趋于稳定。 | 这个系统趋于平衡状态。\n191 | 全球性 | 形 | quán qiú xìng | global | Mondiale | global | Global | глобальный | 气候变暖是一个全球性问题。 | 全球性合作才能解决问题。\n192 | 人体 | 名 | rén tǐ | human body | Le corps humain | corpo humano | Cuerpo humano | человеческое тело | 人体由多个系统组成。 | 人体需要足够的水分和营养。\n193 | 人造 | 形 | rén zào | artificial | Artificiel | artificial | Artificial | искусственный | 人造卫星在太空中运行。 | 人造皮革是一种环保材料。\n194 | 溶剂 | 名 | róng jì | solvent | Solvants | solvente | Disolvente | растворитель | 水是一种常见的溶剂。 | 溶剂可以溶解其他物质。\n195 | 若干 | 代 | ruò gàn | Several | Plusieurs | Vários | Varios | несколько | 他买了若干本书。 | 若干年后，这里会有很大变化。\n196 | 三维 | 名 | sān wéi | three-dimensional | En trois dimensions | tridimensional | Tridimensional | трехмерный | 三维电影有很强的立体感。 | 三维模型可以全方位展示。\n197 | 散射 | 动 | sàn shè | scattering | Sporadique | dispersão | Esporádico | рассеяние | 光会发生散射。 | 散射会发生丁达尔效应。\n198 | 扫描 | 动 | sǎo miáo | scanning | Scan | digitalização | Escaneo | сканирование | 请把这份文件扫描一下。 | 医院用扫描设备检查身体。 | 许多手机支持扫描功能，方便用户快速获取信息或进行支付。\n199 | 删除 | 动 | shān chú | delete | Supprimer | apagar | Eliminar | удалять | 他删除了不需要的文件。 | 错误的信息需要删除。\n200 | 上端 | 名 | shàng duān | Upper end | Extrémité supérieure | Extremidade superior | Arriba | верхний конец | 他站在梯子的上端。 | 这个物体的上端是圆形的。\n201 | 上限 | 名 | shàng xiàn | upper limit | Plafond | limite superior | Límite superior | верхний предел | 这个容器的容量已经达到了上限。 | 他的忍耐也到了上限。\n202 | 渗透 | 动 | shèn tòu | permeation | Pénétration, pénétration | permeação | Penetración, penetración | проникновение | 水慢慢渗透到泥土里。 | 外来文化逐渐渗透到当地社会，丰富了当地的文化内涵。\n203 | 剩余 | 动 | shèng yú | surplus | Surplus | excedente | Superávit | избыток | 吃完饭后，桌上还有些剩余的饭菜。 | 他用剩余的时间复习功课。\n204 | 石英 | 名 | shí yīng | quartz | Quartz | quartzo | Cuarzo | кварц | 这块石头里含有石英成分。 | 石英表走时很准。\n205 | 识别 | 动 | shí bié | recognition | Identification | reconhecimento | Identificación | распознавание | 她的红衣服很容易被识别。 | 这种动物很容易识别。 | 一般手机都有指纹识别和人脸识别。\n206 | 实时 | 副 | shí shí | real time | Temps réel | tempo real | Tiempo real | в реальном времени | 实时监控系统能随时掌握情况。 | 他喜欢实时更新自己的动态。\n207 | 实物 | 名 | shí wù | material object | Physique | objecto material | Objetos físicos | материальный объект | 物理老师用实物教学。 | 他把实物拿过来给大家看。\n208 | 实质 | 名 | shí zhì | essence | Essence | essência | Esencia | сущность | 这件事的实质是利益冲突。 | 不要只看表面，要看到问题的实质。\n209 | 矢量 | 名 | shǐ liàng | vector | Vecteur | vector | Vector | вектор | 矢量有大小有方向。 | 他学习了矢量的运算方法。\n210 | 收敛 | 动 | shōu liǎn | convergence | Convergence | convergência | Convergencia | сходимость | 这个算法是收敛的。 | 他收敛起笑容，变得严肃。\n211 | 受体 | 名 | shòu tǐ | receptor | Le récepteur | receptor | Receptores | рецептор | 药物通过与受体结合发挥作用。 | 通过检测受体的水平，医生可以为患者选择最适合的治疗药物。\n212 | 输送 | 动 | shū sòng | transport | Transport | transporte | Transporte | транспорт | 这条管道负责输送石油。 | 他负责输送这批东西。\n213 | 数量级 | 名 | shù liàng jí | Order of magnitude | Ordre de grandeur | Ordem de magnitude | Orden de magnitud | порядок величины | 10和100相差一个数量级。 | 他的收入和我的收入完全不在一个数量级。\n214 | 数列 | 名 | shù liè | series | Série | série | Serie | серия | 这个数列的每一项都比前一项多1。 | 数列有通项公式。\n215 | 瞬间 | 名 | shùn jiān | moment | Un instant | momento | Un momento | момент | 每个人都想留住烟花燃放的瞬间。 | 流星瞬间就不见了。\n216 | 瞬时 | 名 | shùn shí | instantaneous | Instantanée | instantâneo | Instantáneo | мгновенный | 瞬时速度是指物体在某一时刻的速度。 | 这个设备可以测量瞬时速度。\n217 | 搜索引擎 | 名 | sōu suǒ yǐn qíng | Search Engines | Moteur de recherche | Motores de Busca | Motor de búsqueda | поисковые системы | 我们可以通过搜索引擎找到很多信息。 | 谷歌是一个搜索引擎。\n218 | 酸性 | 名 | suān xìng | acidity | Acidité | acidez | Acidez | кислотность | 这种溶液呈酸性。 | 酸性土壤适合种植草莓。\n219 | 算法 | 名 | suàn fǎ | algorithm | Algorithme | algoritmo | Algoritmos | алгоритм | 这个算法可以快速计算出结果。 | 新的计算机算法可以提高效率。\n220 | 随机 | 副、形 | suí jī | random | Aléatoire | aleatório | Aleatorio | случайный | 他出现的概率是随机的。 | 他随机拿走了一个同学的课本。\n221 | 缩写 | 名、动 | suō xiě | abbreviation | Abréviations | abreviatura | Siglas | сокращение | 他的名字缩写是Z.Z.。 | 中国的缩写是PRC。\n222 | 碳化硅 | 名 | tàn huà guī | silicon carbide | Carbure de silicium | carboneto de silício | Carburo de silicio | кремний углерод | 碳化硅是一种硬度很高的材料。 | 这种碳化硅陶瓷耐高温。\n223 | 碳水化合物 | 名 | tàn shuǐ huà hé wù | carbohydrate | Hydrates de carbone | hidratos de carbono | Carbohidratos | углевод | 碳水化合物是人体的主要能量来源。 | 土豆富含碳水化合物。\n224 | 糖类 | 名 | táng lèi | Carbohydrates | Hydrates de carbone | Hidratos de carbono | Carbohidratos | углеводы | 糖类包括单糖、双糖和多糖。 | 苹果含有丰富的糖类。\n225 | 提取 | 动 | tí qǔ | extract | Extraction | extracto | Extracción | извлекать | 从文件中提取关键信息。 | 他提取了账户里的钱。 | 科学家从这种植物中提取了蛋白质。\n226 | 替换 | 动 | tì huàn | replace | Remplacement | substituir | Reemplazar | заменять | 用新课本替换旧课本。 | 替换这件旧衣服。\n227 | 添加 | 动 | tiān jiā | Add | Ajouter | Adicionar | Añadir | добавлять | 在汤里添加一些盐。 | 他给手机添加了一个新的功能。\n228 | 跳跃 | 动 | tiào yuè | jump | Le saut | Saltar | Salto | прыгать | 小孩在草地上跳跃。 | 人工智能技术近年来取得了巨大的跳跃式发展。\n229 | 透过 | 动、介 | tòu guò | transmission | Transmission | transmissão | Transmisión | передача | 透过窗户可以看到外面的风景。 | 透过表面看本质。\n230 | 透镜 | 名 | tòu jìng | lens | Lentilles | lente | Lente | линза | 这个透镜可以聚焦光线。 | 透镜分为凹透镜和凸透镜。\n231 | 图表 | 名 | tú biǎo | chart | Diagramme | gráfico | Gráfico | диаграмма | 这个图表清晰地展示了学生的成绩。 | 他用图表来说明内容。\n232 | 推论 | 动、名 | tuī lùn | inference | Inférence | inferência | Inferencia | вывод | 从这些数据可以得出一个推论。 | 他推论出的答案是错误的。\n233 | 褪色 | 动 | tuì sè | fade | Décoloration | desvanecer | Decoloración | затухать | 这件衣服开始褪色了。 | 墙上的旧照片已经褪色了。\n234 | 外部 | 名 | wài bù | external | Externe | externo | Externo | внешний | 这个装置的外部很坚固。 | 外部环境对实验结果有影响。\n235 | 外加 | 动 | wài jiā | additional | Supplémentaire | adicional | Adicional | дополнительный | 会议预计将持续两个小时，外加半小时的休息时间。 | 这个设备需要外加一个电源。\n236 | 外形 | 名 | wài xíng | appearance | Apparence | aparência | Apariencia | внешний вид | 这个产品的外形像一只兔子。 | 他喜欢外形简洁的家具。\n237 | 弯曲 | 形 | wān qǔ | bend | Courbe | dobrar | Doblar | изгибать | 这是一条弯曲的路，开车要小心。 | 他把铁丝弯曲成一个圆。\n238 | 万有引力 | 名 | wàn yǒu yǐn lì | Universal gravitation | Toute gravitation | Gravitação universal | Gravedad universal | всемирное тяготение | 万有引力使地球围绕太阳转动。 | 万有引力是牛顿发现的。\n239 | 微分 | 名 | wēi fèn | differential | Différentiel | diferencial | Diferenciado | дифференциал | 他正在学习微分的计算方法。 | 微分可以帮助我们研究函数。\n240 | 微分方程 | 名 | wēi fèn fāng chéng | differential equation | Équations différentielles | equação diferencial | Ecuación diferencial | дифференциальное уравнение | 这个问题可以用微分方程来描述。 | 解微分方程需要一定的数学基础。\n241 | 微观 | 形 | wēi guān | microcosmic | Microscopique | microcosmicos | Microscópico | микроскопический | 微观来看，物质是由分子和原子组成的。 | 微观研究细微的规律。\n242 | 微积分 | 名 | wēi jī fèn | calculus | Calcul | cálculo | Cálculo | математический анализ | 微积分是大学数学的重要课程。 | 微积分分为微分和积分。\n243 | 微量元素 | 名 | wēi liàng yuán sù | trace element | Éléments traces | oligoelemento | Oligoelementos | микроэлемент | 人体需要多种微量元素。 | 铁是一种微量元素。\n244 | 微生物 | 名 | wēi shēng wù | microorganism | Micro - organismes | microrganismo | Microorganismos | микроорганизм | 微生物有重要作用。 | 病毒属于微生物。\n245 | 文件夹 | 名 | wén jiàn jiá | folder | Pinces en papier | pasta | Clip de papel | папка | 我把文档都保存在同一个文件夹里。 | 在电脑上新建一个文件夹。\n246 | 无机物 | 名 | wú jī wù | Inorganic substances | Substances inorganiques | Substâncias inorgânicas | Sustancias inorgánicas | неорганические вещества | 无机物通常不含碳元素。 | 这种岩石主要由无机物组成。\n247 | 无穷大 | 名 | wú qióng dà | Infinity | L'infini | Infinito | Infinito | бесконечность | 无穷大表示没有上限。 | 他觉得宇宙是无穷大的。\n248 | 无条件 | 动 | wú tiáo jiàn | Unconditional | Sans condition | Incondicional | Incondicionalmente | безусловный | 他无条件地支持我的决定。 | 这种爱是无条件的。\n249 | 无线电 | 名 | wú xiàn diàn | radio | Radio | rádio | Radio | радио | 无线电波可以用于通信。 | 他喜欢听无线电广播。\n250 | 无序 | 形 | wú xù | disorder | Confusion | perturbação | Caos | беспорядок | 这个房间太乱了，完全无序。 | 无序需要整理才能变得有序。\n251 | 误差 | 名 | wù chà | error | Erreur | erro | Error | ошибка | 误差是不可避免的。 | 计算中的误差导致结果不准确。\n252 | 吸附 | 动 | xī fù | adsorption | Adsorption | adsorção | Adsorción | адсорбция | 活性炭可以吸附异味。 | 滤网可以吸附空气中的微小颗粒物，如灰尘、\n253 | 下限 | 名 | xià xiàn | lower limit | Limite inférieure | limite inferior | Límite inferior | нижний предел | 这个商品的价格有下限。 | 温度不能低于下限。\n254 | 弦 | 名 | xián | string | Une chaîne | string | Una cadena | строка | 吉他的弦需要定期更换。 | 余弦是一种三角函数。\n255 | 显示器 | 名 | xiǎn shì qì | display | Afficher | display | Mostrar | отображать | 电脑的显示器坏了。 | 这个显示器的分辨率很高。\n256 | 相对论 | 名 | xiàng duì lùn | Relativity theory | Théorie de la relativité | Teoria da relatividade | Relativismo | теория относительности | 爱因斯坦提出了相对论。 | 相对论解释了时间膨胀现象。\n257 | 向量 | 名 | xiàng liàng | vector | Vecteur | vector | Vector | вектор | 向量有大小和方向。 | 这个向量指向北方。\n258 | 像素 | 名 | xiàng sù | pixel | Pixels | pixel | Píxeles | пиксель | 图片由许多像素组成。 | 高像素的相机拍出的照片更清晰。\n259 | 芯片 | 名 | xīn piàn | chip | Les lacunes | chip | Brecha | чип | 手机里有芯片。 | 这个芯片性能很强。\n260 | 新建 | 动、形 | xīn jiàn | New | Nouveau | Novo | Nuevo | новый | 在电脑上新建一个文件。 | 公司决定新建一个项目。\n261 | 选定 | 动 | xuǎn dìng | selected | Sélection | seleccionado | Selección | выбранный | 我选定了这个方案。 | 他被选定参加比赛。\n262 | 寻常 | 形 | xún cháng | ordinary | Ordinaire | normal | Ordinario | обычный | 这是一个寻常的日子。 | 这种花很寻常。\n263 | 压缩 | 动 | yā suō | compress | Compression | comprimir | Compresión | сжимать | 这个软件可以压缩文件。 | 他把衣服压缩进箱子里。\n264 | 衍射 | 动 | yǎn shè | diffraction | Diffraction | difração | Difractivo | дифракция | 光的衍射现象很明显。 | 声音在障碍物后发生衍射。\n265 | 衍生物 | 名 | yǎn shēng wù | derivative | Dérivés | derivado | Derivados | производная | 这种衍生物很有用。 | 苯乙烯C₈H₈是苯C₆H₆的衍生物，广泛用于合成塑料和橡胶。\n266 | 验证 | 动 | yàn zhèng | validate | Vérification | validar | Verificación | валидировать | 实验验证了这个理论。 | 取钱需要验证身份。\n267 | 样品 | 名 | yàng pǐn | sample | Échantillons | amostra | Muestra | образец | 这个样品很典型。 | 他带了一个样品来展示。\n268 | 要点 | 名 | yào diǎn | main points | Points clés | pontos principais | Puntos principales | основные пункты | 抓住要点很重要。 | 这个报告的要点很清晰。\n269 | 乙醇 | 名 | yǐ chún | ethanol | Éthanol | etanol | Etanol | этиловый спирт | 这种溶液含有乙醇。 | 乙醇可以用来消毒。\n270 | 因素 | 名 | yīn sù | factor | Facteurs | factor | Factores | фактор | 在这个实验中，温度是一个关键因素。 | 多种因素影响了结果。\n271 | 引发 | 动 | yǐn fā | trigger | Déclencheur | gatilho | Desencadenar | триггер | 这个事件引发了讨论。 | 他的行为引发了争议。\n272 | 盈利 | 名 | yíng lì | profit | Bénéfices | lucro | Ganancias | прибыль | 这家公司的盈利很高。 | 他通过投资盈利了。\n273 | 映射 | 动 | yìng shè | mapping | Dessiner la carte de... | mapeamento | Dibuja un mapa de... | отображение | 数据映射到图表上。 | 在数学中，函数可以看作是定义域到值域的一种映射关系。\n274 | 硬盘 | 名 | yìng pán | Hard disk | Disque dur | Disco rígido | Disco duro | жесткий диск | 电脑的硬盘容量很大。 | 他更换了硬盘。\n275 | 优化 | 动 | yōu huà | optimization | Optimisation | optimização | Optimización | оптимизация | 这个程序需要优化。 | 对系统进行优化后提高了效率。\n276 | 优异 | 形 | yōu yì | excellent | Excellent | excelente | Excelente | отличный | 他成绩优异。 | 她最近的表现优异。\n277 | 有序 | 形 | yǒu xù | Ordered | Organisé de manière ordonnée | Ordenado | Organizado de manera ordenada | упорядоченный | 这个队伍很有序。 | 有序的环境让人舒适。\n278 | 原点 | 名 | yuán diǎn | origin | Origine | origem | Origen | начало | 坐标系的原点是(0,0)。 | 一切都从原点开始。\n279 | 原函数 | 名 | yuán hán shù | Primitive function | Fonction originale | Função primitiva | Función original | первообразная функция | 原函数是积分的结果。 | 做题需要考虑原函数的定义域。\n280 | 原子核 | 名 | yuán zǐ hé | Atomic nucleus | Noyau atomique | Núcleo atómico | Núcleo atómico | ядро атома | 原子核包含质子和中子。 | 原子核是原子的核心部分。\n281 | 圆柱 | 名 | yuán zhù | cylinder | Colonne | cilindro | Columna | цилиндр | 这个瓶子是圆柱形的。 | 圆柱的体积计算公式是πr²h。\n282 | 远程 | 形 | yuǎn chéng | remote | Distant | remoto | Lejano | удаленный | 他通过远程会议参与讨论。 | 远程教育采用线上的形式。\n283 | 运动学 | 名 | yùn dòng xué | kinematics | Kinésiologie | cinemática | Cinemática | кинематика | 运动学研究物体的运动规律。 | 这个软件可以模拟运动学过程。\n284 | 载体 | 名 | zǎi tǐ | carrier | Le porteur | transportador | Portador | носитель | 病毒以飞沫为载体传播。 | 语言是文化的载体。\n285 | 增量 | 名 | zēng liàng | increment | Incrémentation | incremento | Incremento | приращение | 数据每天都有增量。 | 算法通过增量更新提高效率。\n286 | 占用 | 动 | zhàn yòng | occupation | Travail | ocupação | Trabajo | занятие | 这个文件占用了大量空间。 | 他占用了很多时间来解释。\n287 | 真空 | 名 | zhēn kōng | vacuum | Le vide | vácuo | Vacío | вакуум | 真空环境中没有空气。 | 食物需要真空包装。\n288 | 正向 | 名 | zhèng xiàng | Forward | En avant | Avançar | Hacia adelante | вперед | 正向思维有助于解决问题。 | 这个力是正向的。\n289 | 直观 | 形 | zhí guān | Intuitive | Intuitive | Intuitivo | Intuitivo | интуитивный | 这个图表很直观。 | 直观的数据更容易理解。\n290 | 指令 | 动、名 | zhǐ lìng | instructions | Instructions | instruções | Instrucciones | инструкции | 她下了一个指令。 | 他按照指令完成了任务。\n291 | 质子 | 名 | zhì zǐ | proton | Protons | protão | Protones | протон | 质子带有正电荷。 | 原子核由质子和中子组成。\n292 | 中子 | 名 | zhōng zǐ | neutron | Neutrons | neutrão | Neutrones | нейтрон | 中子不带电。 | 中子呈电中性。\n293 | 周期性 | 名 | zhōu qī xìng | Periodic | Régulièrement | Periódico | Regular | периодический | 潮汐的周期性变化很明显。 | 正弦函数具有周期性。\n294 | 主机 | 名 | zhǔ jī | main engine | Moteur principal | motor principal | Motor principal | главный двигатель | 主机是计算机的核心部件。 | 网络的主机负责处理请求。\n295 | 转动 | 动 | zhuǎn dòng | turn | Tourner | turn | Girar | поворот | 风扇在快速转动。 | 机器不停转动。\n296 | 追踪 | 动 | zhuī zōng | track | La piste | faixa | órbita | траектория | 卫星可以追踪目标。 | 警察在追踪罪犯。\n297 | 准则 | 名 | zhǔn zé | criterion | Standard | critério | Criterios | критерий | 我们应该遵守准则。 | 公司的行为准则很严格。\n298 | 紫光 | 名 | zǐ guāng | Purple light | La lumière violette | Luz roxa | Luz púrpura | фиолетовый свет | 紫光波长为380～420nm。 | 紫光灯可以验钞。\n299 | 自变量 | 名 | zì biàn liàng | independent variable | Variables indépendantes | variável independente | Variables independientes | независимая переменная | 在函数中，x是自变量。 | 自变量的变化影响函数值。\n300 | 字节 | 名 | zì jiē | byte | Octets | byte | Bytes | байт | 字节是一个单位。 | 每个字节包含8位二进制数，即 1Byte = 8bit。\n301 | 最大值 | 名 | zuì dà zhí | Maximum value | Valeur maximale | Valor máximo | Valor máximo | максимум | 这个数列的最大值是100。 | 最大值与极大值可能不相等。\n302 | 遵从 | 动 | zūn cóng | follow | Suivre | seguir | Seguir | следовать | 他遵从医生的建议。 | 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