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Source note2 min read

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs

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OpenReview

openreview.net/forum

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Plain-Language Brief

The paper proposes AgentsNet, a new benchmark for multi-agent reasoning. This benchmark is designed to measure the ability of multi-agent systems to collaborate effectively and leverage their network topology. The authors evaluate various baseline methods on this benchmark and find that some state-of-the-art LLMs perform well in small networks but struggle as network size increases.

What the Document Says

  • Large-language models (LLMs) are used in multi-agent systems for problem-solving.
  • The paper introduces AgentsNet, a benchmark for measuring the ability of multi-agent systems to collaborate and leverage their network topology.
  • AgentsNet is inspired by problems in distributed systems and graph theory.
  • It measures the ability of multi-agent systems to form strategies for problem-solving, self-organization, and effective communication given a network topology.
  • The paper evaluates various baseline methods on AgentsNet, including homogeneous networks of agents that first have to agree on protocols for organization and communication.
  • Some frontier LLMs are found to perform well in small networks but struggle as the network size increases.
  • Existing multi-agent benchmarks typically cover 2-5 agents, while AgentsNet can scale with new generations of LLMs and is practically unlimited in size.

Key Concepts Mentioned

  • Large-language models
  • Multi-agent systems
  • AgentsNet
  • Network topology
  • Problem-solving
  • Self-organization
  • Communication

Claims Made by the Document

  • LLMs are effective in multi-agent systems.
  • AgentsNet measures the ability of multi-agent systems to collaborate and leverage their network topology.
  • Some frontier LLMs perform well in small networks but struggle as the network size increases.

Evidence Used

  • LLMs are used in multi-agent systems for problem-solving (implied from context).
  • AgentsNet is introduced and described (from text).
  • Results of evaluation on AgentsNet are presented (from text).

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also raises several questions on the ability of a complex network of agents to effectively self-organize and collaborate. While measuring performance on standard reasoning benchmarks indicates how well multi-agent systems can solve reasoning tasks, it is unclear whether these systems are able to leverage their topology effectively. Here, we propose AgentsNet, a new benchmark for multi-agent reasoning. By drawing inspiration from classical problems in distributed systems and graph theory, AgentsNet measures the ability of multi-agent systems to collaboratively form strategies for problem-solving, self-organization, and effective communication given a network topology. We evaluate a variety of baseline methods on AgentsNet including homogeneous networks of agents which first have to agree on basic protocols for organization and communication. We find that some frontier LLMs are already demonstrating strong performance for small networks but begin to fall off once the size of the network scales. While existing multi-agent benchmarks cover at most 2--5 agents, AgentsNet is practically unlimited in size and can scale with new generations of LLMs. As such, we also probe frontier models in a setup with up to 100 agents.