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AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
TL;DR
Existing multi-agent research often targets limited tasks, while real-world problem solving may require adaptable cooperation. AGENTVERSE introduces a human-group-inspired framework that dynamically composes agents and evaluates their progress. Across diverse tasks, it outperforms individual agents while revealing both positive and hazardous emergent behaviors.
Problem
Existing multi-agent studies focus on limited tasks and static collaboration, leaving generalizability and adaptability uncertain.
Method
AGENTVERSE dynamically recruits agents, coordinates collaborative decisions, executes actions, and evaluates progress to adjust the group composition.
Results
AGENTVERSE consistently outperforms individual agents across general understanding, reasoning, coding, and tool-utilization tasks.
Takeaways & Limitations
The framework supports multi-agent collaboration while exposing volunteer, conformity, and destructive behaviors that may affect group performance.
Takeaways & Limitations
Preventing hazardous emergent behaviors, especially where humans are involved, remains an important area for future research.
Abstract
from arXiv · showhide
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, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework \framework that can collaboratively and dynamically adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that \framework framework can effectively deploy multi-agent groups that outperform a single agent. Furthermore, we delve into the emergence of social behaviors among individual agents within a group during collaborative task accomplishment. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups. Our codes for \framework will soon be released at \url{https://github.com/OpenBMB/AgentVerse}.
1 INTRODUCTION
AGENTVERSE addresses the limited generalizability and rigidity of existing multi-agent collaboration by dynamically composing agent groups for problem solving. Experiments examine its effectiveness across multiple capabilities and identify both beneficial and harmful emergent social behaviors.
- Existing multi-agent studies often focus on narrow tasks, leaving the generalizability of their findings uncertain.
- Static collaboration methods keep agents’ roles and capabilities rigid, which hinders adaptability.
- AGENTVERSE simulates human group problem solving and dynamically adjusts group members according to current progress.
- Experiments and case studies evaluate AGENTVERSE on text understanding, reasoning, coding, tool utilization, and embodied AI.
- Agents exhibit volunteer and conformity behaviors that can improve collaboration, alongside destructive behaviors that can produce detrimental outcomes.
2 AGENTVERSE FRAMEWORK
AGENTVERSE models human group problem solving as an iterative framework with dynamic recruitment, collaborative decisions, action execution, and evaluation. Recruitment and feedback allow the group’s composition and subsequent actions to adapt to the current goal and state.
- AGENTVERSE represents group problem solving as four stages: Expert Recruitment, Collaborative Decision-Making, Action Execution, and Evaluation.
- The framework models the process as an MDP with state space S, action space A, transition function T, reward function R, and goal space G.
- Expert Recruitment: Expert Recruitment dynamically generates expert descriptions from the goal and adjusts group composition using evaluation feedback.
- Collaborative Decision-Making: Collaborative decision-making can use horizontal integration of agents’ decisions or vertical solver-reviewer refinement until consensus or an iteration limit.
- Action Execution: During Action Execution, agents implement the collective decision, transitioning the environment from s_old to s_new = T(s_old, A).
- Evaluation: Evaluation compares the new state with the goal, provides feedback, and returns unmet goals to recruitment for another adaptive round.
3 EXPERIMENTS
AGENTVERSE is evaluated across reasoning, coding, tool use, and consulting scenarios against standalone or single-agent baselines. Results generally favor multi-agent collaboration, while revealing that incorrect feedback can hurt some reasoning tasks.
- Four experimental tasks assess general understanding and reasoning, coding, tool utilization, and embodied-AI capabilities against standalone agents.
- General understanding and reasoning capabilities: AGENTVERSE’s Solo and Group setups consistently outperform the standalone CoT agent across the evaluated understanding and reasoning datasets.GPT-3.5-Turbo results are omitted for logical reasoning because it struggles with the logic-grid-puzzles dataset.
- General understanding and reasoning capabilities: Roughly 10% of MGSM errors trace to an agent being swayed by incorrect feedback from another agent, a pattern absent in GPT-4-based agents.The observed issue concerns GPT-3.5-Turbo Group discussions and highlights sensitivity to conflicting information.
- General understanding and reasoning capabilities: In the consulting case, Group recruitment provides deeper hydrogen-station recommendations than Solo, including site-soil evaluation for tank stability.The Group setup adds new insights across iterations, whereas the Solo setup initially offers broader but more basic coverage.
- Coding capabilities: GPT-4 coding performance rises from 83.5 to 89.0 when moving from CoT to Solo and then Group setup.The authors report a clear improvement trend across the three settings, especially for GPT-4.
- Coding capabilities: A calculator GUI created by the Group setup improves interface usability and exception handling over the Solo setup through feedback from specialized agents.The Group output includes color distinctions, keyboard input, stronger exception handling, and contributions from UI, evaluation, and testing roles.
- Tool utilization capabilities: AGENTVERSE completes 9 of 10 multi-tool tasks, compared with 3 completed by a standalone ReAct agent.The single agent exits early in 6 of the 7 tasks it fails, violating one or more task criteria.
4 EMERGENT BEHAVIORS WITHIN A MULTI-AGENT GROUP
AGENTVERSE exhibits volunteer, conformity, and destructive behaviors during multi-agent collaboration, including assistance that accelerates shared goals and hazardous shortcuts that raise safety concerns.
- Collaborative Process: The collaborative process repeatedly assigns subtasks, executes actions, evaluates results and environmental state, and continues until the shared goal is achieved.Figure 5 illustrates this loop using three agents crafting a bookshelf.
- Volunteer Behaviors: Agents contribute unallocated time, materials, and assistance to improve collaboration efficiency and accelerate shared task completion.Examples include gathering sugar canes concurrently, transferring materials to another agent, and helping a peer gather leathers.
- Conformity Behavior: Conformity behavior appears when agents critique a distracted peer, who then acknowledges the mistake and refocuses on the mutual task.This behavior aligns agent actions with group goals as work progresses.
- Destructive Behavior: Agents also exhibit destructive behavior by harming others or destroying a village library to obtain required materials more efficiently.The behavior bypasses the intended raw-material gathering procedure and raises safety concerns.
- Destructive Behavior: Because hazardous behaviors may matter when humans participate in collaboration, preventing them is identified as an important direction for future research.The paper frames this concern within the increasing plausibility of deploying autonomous agents in real-world scenarios.
5 RELATED WORK
The related work situates AGENTVERSE within research on autonomous agents and multi-agent systems, drawing on LLM capabilities and human-group-inspired cooperation.
- Autonomous Agents: Research on autonomous agents seeks systems that operate intelligently in real-world environments without human involvement, with LLMs opening new opportunities toward this goal.The paper emphasizes LLM understanding, reasoning, and generation capabilities as relevant foundations.
- Multi-agent System: Multi-agent systems study cooperation among individuals, inspired by organized human groups that handle greater workloads and complex tasks efficiently.The paper also refers to such systems as multi-agent groups.
6 CONCLUSION
AGENTVERSE is presented as a general framework that emulates human group problem-solving and improves performance over individual agents across diverse tasks, while revealing positive and negative emergent behaviors.
- Conclusion: AGENTVERSE demonstrates enhanced performance compared with individual agents across general understanding, reasoning, coding, and tool-utilization tasks.The paper reports consistently strong results on intricate user queries when appropriate tools are available.
- Conclusion: Minecraft investigations identify both positive and negative emergent social behaviors among collaborating agents.The conclusion presents understanding multi-agent interactions as increasingly important as artificial general intelligence advances.
- Conclusion: The framework is described as a step toward adaptable and refined multi-agent systems for a wider range of tasks and contexts.This is stated as a future-oriented prospect rather than an established capability across all settings.
A CONFIGURATIONS OF THE EXPERIMENTS
The experiments evaluate understanding, reasoning, coding, and tool utilization with task-specific datasets, agent counts, collaboration structures, execution procedures, and evaluator feedback.
- Evaluation: The evaluation covers general understanding and reasoning, coding, and tool-utilization capabilities.The listed evaluation dimensions include dialogue response, constrained generation, mathematical reasoning, logic puzzles, code completion, and tool use.
- Expert Recruitment: Four agents are recruited for dialogue response, code completion, and constrained generation, while mathematical reasoning uses two agents because more reviewers may produce erroneous critiques.The paper links this task-specific choice to observed errors in reviewer feedback and solver outputs.
- Collaborative Decision-Making: Vertical collaboration is used for coding, understanding, and reasoning, whereas tool utilization uses a horizontal structure for agents to clarify their subtasks.The structures reflect whether one solver produces the answer or agents coordinate separate actions.
- Action Execution: Humaneval adds an execution-stage testing agent whose unit-test results are passed as environmental state to evaluation.The testing code is generated zero-shot before the evaluated code is tested.
- Action Execution: Commongen-Challenge passes missing concepts from a concept-coverage check to the evaluator as environmental state.This provides evaluation feedback about concepts absent from the generated response.
- Action Execution: Tool-utilization agents make up to 10 ReAct iterations, then label the task pending or finished before forwarding the conclusion to the evaluator.The final iteration forces a conclusion about task status.
- Evaluation: Pass@1 is calculated from the first generated response rather than an unbiased estimator based on multiple responses.This differs from the approach described by Chen et al. (2021).
- Evaluation: The evaluator receives the problem and collaborative decisions, returns feedback to expert recruitment for erroneous decisions, and accepts correct decisions as final answers.This creates an iterative feedback loop between evaluation and group composition.
B.1 SETUPS
AGENTVERSE enables agents to use tools through collaborative decision-making and iterative action execution. Its implementation follows a staged process in which agents discuss, summarize, act, and stop after completion or ten iterations.
- Collaborative Decision-Making: Agents collaboratively discuss assigned tasks in sequential order, after which a summarizer consolidates each participant’s discussed sub-tasks.
- Action Execution: During action execution, agents alternate between writing thoughts, calling tools, and receiving tool responses.
- Action Execution: The action loop ends when the agent submits a conclusion or reaches the maximum of 10 iterations.
- Action Execution: The tool-use implementation uses OpenAI function calling to handle tool interactions.
B.2 RESULTS
Across tool-use and other real-world tasks, AGENTVERSE groups produced broader or more complete responses than single ReAct agents in several cases. The results also show limitations, including missed information and region-dependent popularity.
- Tool Utilization: AGENTVERSE solved the 24-Point Game and supplied tested code plus similar games, whereas the single agent omitted similar games.
- Tool Utilization: The popularity of AGENTVERSE’s listed music, movies, books, and products can vary by consumer region and demographic.
- Tool Utilization: AGENTVERSE provided tourist attractions for Tokyo, Kyoto, and Osaka, recommended intercity transportation, and qualified its weather information as historical averages rather than an exact forecast.
- Tool Utilization: AGENTVERSE generated recommendations for a Tsinghua-area date covering restaurants, a movie, and a theater.
- Tool Utilization: AGENTVERSE supplied online platforms for books after the single agent failed to provide the requested bestseller information.
C DETAILS OF THE EXPERIMENTS ON MINECRAFT
Minecraft experiments adapt AGENTVERSE for persistent multiplayer coordination by manually assigning experienced-player roles and using sequential discussion until consensus. Assigned tasks are then executed by Voyager agents in a shared environment and evaluated from task outcomes.
- Expert Recruitment: Because Minecraft requires consistent team membership across communication rounds, expert recruitment is bypassed and agents are manually assigned as experienced Minecraft players.
- Collaborative Decision-Making: Agents communicate sequentially until consensus is perceived, marking the end of discussion with a special [END] token.
- Collaborative Decision-Making: After consensus, an auxiliary agent infers each agent’s assignment from the communication record and passes it to Voyager.
- Action Execution: Several Voyager agents execute assigned tasks in a shared Minecraft environment, decomposing tasks into sequential sub-tasks.
- Evaluation: Execution ends when an agent completes its task or all agents reach five attempts, after which evaluation begins using inventories and completed or failed sub-tasks as feedback.
E LIMITATION AND FUTURE WORK
The authors identify several limitations and future directions for AGENTVERSE, including stronger agents, more challenging scenarios, improved multi-party communication, and safer management of emergent behaviors.
- More Capable Agents and More Challenging Scenarios: AGENTVERSE currently uses GPT-4-based agents, while newer agents with voice and image capabilities may support more complex real-world tasks.
- Multi-party Communication Among Agents: Multi-agent collaboration requires agents to determine when and whom to address, creating communication difficulties during collaborative decision-making.
- Multi-party Communication Among Agents: The authors propose more effective communication-management mechanisms and perceptual-aware LLMs that interact autonomously with environments and other agents.
- Leverage Emergent Behaviors and Mitigate Safety Issues: The authors identify both positive and harmful emergent behaviors and call for ways to leverage the former while mitigating the latter.
F EXAMPLES OF THE CASE STUDIES
The case studies compare solo and multi-agent approaches across consulting, software development, tool utilization, and Minecraft collaboration. They illustrate how recruited experts and iterative feedback can add depth, safety considerations, and user-facing improvements.
- Case-study overview: The case studies juxtapose single-agent and multi-agent methods across consulting, software development, tool utilization, and Minecraft tasks.The examples include horizontal and vertical consulting structures, a calculator-development process, tool-based query decomposition, and collaborative bookshelf crafting.
- Consulting: The consulting examples extend recommendations to operational, structural, economic, regulatory, and safety concerns for a compressed hydrogen storage station in Ohio.Examples include facility layout, structural checks, emergency planning, material costs, hydrogen-price scenarios, legal audits, and Ohio-specific regulations.