# AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs

arXiv: https://arxiv.org/abs/2507.08616 · HTML: https://mlsanity.com/paper/2507.08616/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 10 of 10)

- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration](https://mlsanity.com/paper/2310.02170/) (2023)
- [Scaling Large Language Model-based Multi-Agent Collaboration](https://mlsanity.com/paper/2406.07155/) (2024)
- [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://mlsanity.com/paper/2411.00640/) (2024)
- [Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence](https://mlsanity.com/paper/2407.07061/) (2024)
- [LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models](https://mlsanity.com/paper/2310.03903/) (2023)
- [MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration](https://mlsanity.com/paper/2311.08562/) (2023)
- [Multi-Agent Consensus Seeking via Large Language Models](https://mlsanity.com/paper/2310.20151/) (2023)
- [A Scalable Communication Protocol for Networks of Large Language Models](https://mlsanity.com/paper/2410.11905/) (2024)
- [MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains](https://mlsanity.com/paper/2407.18961/) (2024)

## Built on by (most cited first, 30 of 31)

- [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026)
- [Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis](https://mlsanity.com/paper/2509.13782/) (2025)
- [Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook](https://mlsanity.com/paper/2602.14299/) (2026)
- [Multi-agent systems for chemical engineering: A review and perspective](https://mlsanity.com/paper/2508.07880/) (2025)
- [Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing](https://mlsanity.com/paper/2604.08401/) (2026)
- [Architectural Design, Not Only Model Intelligence, Governs Multi-Agent LLM Performance](https://mlsanity.com/paper/2602.03128/) (2026)
- [Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination](https://mlsanity.com/paper/2508.14635/) (2025)
- [TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration](https://mlsanity.com/paper/2601.04544/) (2026)
- [CRAFT: Grounded Multi-Agent Coordination Under Partial Information](https://mlsanity.com/paper/2603.25268/) (2026)
- [AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators](https://mlsanity.com/paper/2605.08647/) (2026)
- [Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm](https://mlsanity.com/paper/2607.25446/) (2026)
- [Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence](https://mlsanity.com/paper/2608.21156/) (2026)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [Benchmarking Open-Ended Multi-Agent Coordination in Language Agents](https://mlsanity.com/paper/2606.08340/) (2026)
- [Social Networks of LLM Agents](https://mlsanity.com/paper/2607.03695/) (2026)
- [Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems](https://mlsanity.com/paper/2609.05933/) (2026)
- [SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks](https://mlsanity.com/paper/2512.00834/) (2025)
- [Interaction Theater: A case of LLM Agents Interacting at Scale](https://mlsanity.com/paper/2602.20059/) (2026)
- [EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions](https://mlsanity.com/paper/2605.02289/) (2026)
- [BRA-Audit: Budgeted Runtime Auditing for LLM Multi-Agent Systems via Cumulative-Exposure Audit-Point Placement](https://mlsanity.com/paper/2608.14668/) (2026)
- [Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordination](https://mlsanity.com/paper/2609.32939/) (2026)
- [Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamics](https://mlsanity.com/paper/2510.08242/) (2025)
- [When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling](https://mlsanity.com/paper/2605.13172/) (2026)
- [An Empirical Study of Coordination Mode as the First-Class Citizen in From-Scratch Multi-Agent Coding](https://mlsanity.com/paper/2607.27877/) (2026)
- [Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy](https://mlsanity.com/paper/2608.09857/) (2026)
- [Memory Is Communication: The Frontier Between Remembering and Signaling](https://mlsanity.com/paper/2608.17053/) (2026)
- [GTA: Graph Theory Agent and Benchmark for Algorithmic Graph Reasoning with LLMs](https://mlsanity.com/paper/2609.12265/) (2026)
- [Symbolic Guidance for LLM Agents in Distributed Multiagent Coordination](https://mlsanity.com/paper/2609.31963/) (2026)
- [RepoMAS: Solving Progressively Specified Tasks with Issue-Driven Multi-Agent Systems](https://mlsanity.com/paper/2609.32490/) (2026)
- [Evo2Team: When Do Evolved Skills Transfer? From Selection to Deployment](https://mlsanity.com/paper/2609.34135/) (2026)

## Abstract (arXiv)

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.