The paper introduces Adaptive Influence Graphs (AIGs), a two‑stage framework that first converts a failed trace into a structured graph and then navigates it to pinpoint the critical error in multi‑agent large language model systems. Experiments across multiple models demonstrate that richer trace representations and adaptive graph construction improve failure attribution, with AIGs achieving state‑of‑the‑art results on the Who&When benchmark. The study shows that both the diagnosing model and the way traces are represented and explored are crucial for accurate failure attribution.
By Yarden Bakish, Amir Dudai, Roy Ganz, Oren Nuriel, Elad Ben Avraham, Mor Shpigel Nacson, Ron Litman
EDGE is a framework that attributes multiple related errors in multi-agent large language model systems by constructing an error dependency graph from observed error events. It validates a reliable causal subset through counterfactual rollout and uses this inference graph to guide a two-stage LLM-as-judge detector for error attribution. Experiments on TRAIL and MAST demonstrate that EDGE improves category-level multi-error attribution across most models and settings, and that the graph aids explanation and repair analysis.
By Jun Hou, Priya Pitre, Yi Fang, Xuan Wang
arXiv:2602.02475v2 Announce Type: replace
Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
By Shraddha Barke, Arnav Goyal, Alind Khare, Avaljot Singh, Suman Nath, Chetan Bansal
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: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
arXiv:2607. 12747v1 Announce Type: new Abstract: Failure attribution for LLM-based agentic systems, i.
By Samuel Yeh, Yiwen Zhu, Shaleen Deep, Sharon Li
arXiv:2606. 09071v1 Announce Type: new Abstract: Large language model (LLM) agents now solve complex tasks through long plan-and-execution traces, yet the ability to locate errors in a completed traces still lags far behind, especially in the \emph{silent failure} regime.
By Xiaofeng Lin, Yingxu Wang, Tung Sum Thomas Kwok, Daniel Guo, Sahil Arun Nale, Charles Fleming, Guang Cheng
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:2608. 06909v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate through long-horizon trajectories involving user instructions, tool use, external observations, and memory.
By Jing Chen, Yang Sun, Li Zhang, Lin Xu, Jie Shi
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
DeFA is a dependency-guided framework that attributes failures in large language model agents by constructing an event dependency graph and a failure propagation graph from protocol relations and semantic dependencies. It identifies violating events, traces their sources and effects, and determines the decisive error, responsible agent, and error category. The method supports long trajectories through segmentation and has shown superior accuracy on text, image, and video tasks, while its diagnostic feedback can improve agent performance on subsequent tasks.
By Bo Deng, Xinlei Zheng, Yi Wei, Kang Zhou, Chongyang Tao, Renzhao Liang, Xuanren Chen, Lifan Guo, Chi Zhang