Multi-Agent Systems

Source papers 52

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• A Domain Specific Modeling Language for Multiagent Systems
   Software systems are becoming more and more complex with a large number of interacting partners…
• AI Agent Traps
  As autonomous AI agents increasingly navigate the web, they face a novel challenge: the information environment itself. This gives rise to a critical vulnerability we refer to as "AI Agent Traps" — ad…
• Adapting LLM Agents with Universal Feedback in Communication
  recent works also focus on how to train the LLMs agent use linguistic feedback and non-linguistic reward signals. The linguistic feedback is usually processed as instruction data to do Instruction Fin…
• Agent Laboratory: Using LLM Agents as Research Assistants
  This framework accepts a human-provided research idea and progresses through three stages—literature review, experimentation, and report writing to produce comprehensive research outputs, including a …
• Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models
  Abstract: Recent advances in prompting techniques and multi-agent systems for Large Language Models (LLMs) have produced increasingly complex approaches. However, we lack a framework for characterizin…
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
  Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, coo…
• Agentic AI and the next intelligence explosion
  By its nature, intelligence is high-dimensional and relational, not a single quantity that must be unambiguously less or greater than human scale. In fact, it is unclear what we even mean by “human sc…
• Agentic Reasoning for Large Language Models
  Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in close…
• Agents of Chaos
  LLM-powered AI agents are rapidly becoming more capable and more widely deployed (Masterman et al., 2024; Kasirzadeh & Gabriel, 2025). Unlike conventional chat assistants, these systems are increasing…
• Apollo's Oracle: Retrieval-Augmented Reasoning in Multi-Agent Debates
   Multi-agent debate systems are designed to derive accurate and consistent conclusi…
• AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration
  Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next …
• Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration
  While AI agents show potential in scientific ideation, most existing frameworks rely on single-agent refinement, limiting creativity due to bounded knowledge and perspective. Inspired by real-world re…
• Building Cooperative Embodied Agents Modularly with Large Language Models
  In this work, we address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multiobjective tasks instantiated in various embod…
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
  The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be…
• Can AI Agents Agree?
  Large language models are increasingly deployed as cooperating agents, yet their behavior in adversarial consensus settings has not been systematically studied. We evaluate LLM-based agents on a Byzan…
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
  multiple human annotators collaborating in the evaluation, we resort to a multi-agent debate framework, moving beyond single-agent prompting strategies. The multi-agentbased approach enables a group o…
• Collaborative Reasoner: Self-Improving Social Agents with Synthetic Conversations
  With increasingly powerful large language models (LLMs) and LLM-based agents tackling an ever-growing list of tasks, we envision a future where numerous LLM agents work seamlessly with other AI agents…
• Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
  https:// CGMI: Configurable General Multi-Agent Interaction Framework [https://arxiv.org/abs/2308.12503](https://arxiv.org/abs/2308.12503) [[Memory]] [[Role Play]] “With the capabilities of large …
• Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook
  As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to huma…
• Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
  Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps—missing or outdated information in LLMs—might always persist given the evolving nature of knowledge. In this wor…
• Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures
  Abstract—We present a 25,000-task computational experiment comparing coordination architectures in multi-agent LLM systems across 8 models, 4–256 agents, and 8 protocols. Our key finding is the endoge…
• Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
  still struggle on complex reasoning tasks, which drives the research on cognitive behaviors of LLMs to explore human-like problem-solving strategies. Along this direction, one representative strategy …
• Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems
  Abstract. LLM-based MAS are gaining popularity due to their potential for collaborative problem-solving enhanced by advances in natural language comprehension, reasoning, and planning. Research in The…
• FLOWSTEER: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems
  Multi-agent systems (MAS) powered by large language models (LLMs) increasingly adopt planner–executor architectures, where planners convert prompts into subtasks, roles, dependencies, and routing path…
• FlowReasoner: Reinforcing Query-Level Meta-Agents
  This paper proposes a query-level meta-agent named FLOWREASONER to automate the design of query-level multi-agent systems, i.e., one system per user query. Our core idea is to incentivize a reasoning-…
• Foundation Protocol: A Coordination Layer for Agentic Society
  Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly interact with one another. As these systems scal…
• From Context to Skills: Can Language Models Learn from Context Skillfully?
  Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge…
• Generative Agents: Interactive Simulacra of Human Behavior
  “Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, …
• Intelligent AI Delegation
  AI agents are able to tackle increasingly complex tasks. To achieve more ambitious goals, AI agents need to be able to meaningfully decompose problems into manageable sub-components, and safely delega…
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges
  an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges. Our goal is for readers to gain substantial insights on the following questions: What d…
• Learning "Partner-Aware" Collaborators in Multi-Party Collaboration
  Large Language Models (LLMs) are increasingly bring deployed in agentic settings where they act as collaborators with humans. Therefore, it is increasingly important to be able to evaluate their abili…
• MAPS: A Multi-Agent Framework Based on Big Seven Personality and Socratic Guidance for Multimodal Scientific Problem Solving
  Multimodal scientific problems (MSPs) involve complex issues that require the integration of multiple modalities, such as texts and diagrams, presenting a significant challenge in artificial intellige…
• MARS: A Multi-Agent Framework Incorporating Socratic Guidance for Automated Prompt Optimization
  The basic question-answering format of large language models involves inputting a prompt and receiving a response, and the quality of the prompt directly impacts the effectiveness of the response. Aut…
• MODS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections
  Query-focused summarization (QFS) gives a summary of documents to answer a query. Past QFS work assumes queries have one answer, ignoring debatable ones (Is law school worth it?). We introduce Debatab…
• MetaClaw: Just Talk — An Agent That Meta-Learns and Evolves in the Wild
  Large language model (LLM) agents have rapidly emerged as powerful assistants for complex, multi-step tasks, yet agents deployed in the wild remain largely static, trained once and served unchanged re…
• MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
  Human social interactions depend on the ability to infer others’ unspoken intentions, emotions, and beliefs—a cognitive skill grounded in the psychological concept of Theory of Mind (ToM). While large…
• Metagpt: Meta Programming For Multi-agent Collaborative Framework
  “Multi-agent Collaboration Prior works have explored using multiple LLMs in a collaborative setting to solve complex tasks [32, 29]. The motivation is that by cross-agent interaction, LLMs can collect…
• Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation
  emerging “LLM-as-a-judge” paradigm sheds light on a promising approach to leverage LLM agents to believably simulate human evaluators. Yet, to date, existing LLM-as-a-judge approaches face two limitat…
• Multi-agent cooperation through in-context co-player inference
  Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced between “learningawa…
• Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
  Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the training data, recent work…
• Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
  The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms—from static imitation to incentive-driven decision mak…
• OpenClaw-RL: Train Any Agent Simply by Talking
  Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a liv…
• PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
  In domain-specific applications, GPT-4, augmented with precise prompts or Retrieval- Augmented Generation (RAG), shows notable potential but faces the critical tri-lemma of performance, cost, and data…
• Peer-Preservation in Frontier Models
  Recently, it has been found that frontier AI models can resist their own shutdown, a behavior known as self-preservation. We extend this concept to the behavior of resisting the shutdown of other mode…
• ProAgent: Building Proactive Cooperative Agents with Large Language Models
  Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely primarily on learni…
• Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
  Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation. When computation is normalized, single-agent systems…
• Single-agent or Multi-agent Systems? Why Not Both?
  Multi-agent systems (MAS) decompose complex tasks and delegate subtasks to different large language model (LLM) agents and tools. Prior studies have reported the superior accuracy performance of MAS a…
• SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
  Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage…
• SkillOS: Learning Skill Curation for Self-Evolving Agents
  LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience…
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete various tasks in a self…
• Thought Communication in Multiagent Collaboration
  Natural language has long enabled human cooperation, but its lossy, ambiguous, and indirect nature limits the potential of collective intelligence. While machines are not subject to these constraints,…
• Unleashing Cognitive Synergy In Large Language Models: A Task-solving Agent Through Multi-persona Self-collaboration
  “A cognitive synergist denotes an intelligent agent that works in conjunction with several minds, merging their unique abilities and expertise to improve problem-solving and overall efficacy in intric…