The paper introduces EMFA, a method for attributing failures in large language model-based multi-agent systems by identifying the decisive error—an agent–step pair whose correction would restore correct execution. EMFA builds a structured representation of the failed trajectory, models error propagation and persistent loops, and applies propagation-aware screening with counterfactual verification to pinpoint the decisive error. On the Who&When benchmark, EMFA outperforms prior methods, improving step-level attribution accuracy by 3.45 and 4.40 percentage points on two subsets.
By Jiaqi Liao, Yuanzhao Zhai, Huanxi Liu, Xu Zhang, Zheming Zhuang, Dawei Feng, Bo Ding, Huaimin Wang
arXiv:2606. 00765v1 Announce Type: new Abstract: LLM-based agents increasingly solve complex tasks through long trajectories involving reasoning steps, tool calls, and inter-agent communication.
By Md Nakhla Rafi, Md Ahasanuzzaman, Dong Jae Kim, Zhijie Wang, Tse-Hsun Chen
The paper introduces DCFA, a training‑free framework for attributing failures in large language model‑based multi‑agent systems. DCFA uses a global module to build causal‑inspired dependency graphs from system traces, pinpointing the earliest decisive error, and a local module that refines this attribution through counterfactual reasoning. Experiments on the Who&When benchmark across six LLMs demonstrate that DCFA improves step‑level accuracy by up to 8.27% over existing baselines.
By Zehao Wang, Lanjun Wang, Shilong Jin, Junjie Chen, Yanghua Xiao
arXiv:2606. 03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks.
By Taiyu Zhu, Yifan Wu, Weilin Jin, Ying Li, Gang Huang
arXiv:2607. 07989v1 Announce Type: cross Abstract: Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed structure also introduces new challenges in diagnosing system-level failures.
By Yufei Xia, Anjun Gao, Yueyang Quan, Zhuqing Liu, Minghong Fang
arXiv:2608.29646v1 Announce Type: new
Abstract: Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable t...
By Jiayi Zhang, Zexin Wang, Degang Sun, Changhua Pei, Fei Sun, Gaogang Xie, Jingjing Li