Be Careful Who You Trust: Coordination Dynamics under Corrupted Communication in LLM Multi-Agent Games

arXiv:2609.31704 · cs.MA, cs.LG · Submitted 2026-09-19 · Read on arXiv

cs.MA, cs.LG

Submitted: 2026-09-19

Updated: 2026-09-19

Terminology

• Benchmark — 52× in this paper · explained in 2 episodes
• Alignment — 30× in this paper · explained in 1 episode
• Ablation — 13× in this paper · explained in 1 episode
• Robustness — 9× in this paper · explained in 5 episodes
• Cooperation — 8× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Expected Calibration Error — 2× in this paper
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Multi-Agent System — 2× in this paper · explained in 6 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes

Sources

• Multi-Agent Consensus Seeking via Large Language Models
• The Traitors: Deception and Trust in Multi-Agent Language Model Simulations
• Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage
• Ask WhAI:Probing Belief Formation in Role-Primed LLM Agents
• Agents of Chaos

Related papers

• Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control
• You Only Align Once: Propagating Cooperative Behaviors in Multi-Agent Systems through Seed Agents
• Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems
• MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization
• PeroMAS: A Multi-agent System of Perovskite Material Discovery
• StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning