← Back to all news
arXiv AI By Priya Pitre, Jun Hou, Yi Fang, Naren Ramakrishnan, Xuan Wang

Understanding Communication Failures in Multi-Agent LLM Systems

Read the original on arXiv AI →

The paper presents a taxonomy of communication failures in multi-agent large language model systems, derived from 3,211 failed multi-round trajectories across diverse domains such as medical QA, coding, finance, multi-hop reasoning, ethics, and embodied text tasks. It analyzes how failures arise, noting that they are largely driven by recovery/correction and consensus/decision issues rather than isolated first-round errors, and highlights patterns like early convergence, minority-signal suppression, and wrong framing persistence. The authors recommend stronger disagreement handling, evidence verification, minority-signal preservation, and recovery checks before finalizing decisions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Oct 2

Beyond Final Accuracy: Auditing Communication in LLM Multi-Agent Systems

The paper introduces Independent–Communicate–Revise (ICR), a framework that isolates communication effects in large language model multi‑agent systems by fixing initial reasoning and measuring how messages influence answer revision. ICR evaluates correction, preservation, and selectivity across four reasoning benchmarks, revealing that similar overall accuracy can mask divergent revision behaviors. The study shows that richer messages can both improve and harm outcomes, and that receiver policies can shift preservation and correction dynamics differently across tasks.

By Shixuan Li, Wei Yang, Peiyu Zhang, Anzhe Cheng, Heng Ping, Paul Bogdan
llmsagentsbenchmarks
More like this →
arXiv AI
Oct 1

Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training

The Agent Error Dataset (AED) presents 50,228 error–diagnosis pairs collected from 9,961 source tasks across 33 environments, 19 harness families, and 23 policy models in text‑based agent systems. A five‑stage Agentic Error‑to‑Training (AET) pipeline generates diagnoses and proposed corrections, verifies them against recorded evidence, and creates separate training views for diagnosis and actor recovery. Experiments show that first‑proposal corrections improve verifier pass rates from 18.4% to 51.1%, and fine‑tuning with full‑diagnosis data raises Qwen3‑8B’s exact‑step agreement from 47.2% to 63.6% on a holdout set.

By Kunlun Zhu, Xuyan Ye, Yibo Li, Cheng Qian, Beibin Li, Heng Ji
llmsagentsfine-tuning
More like this →