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arXiv AI

Adaptive Influence Graphs for Failure Attribution in Multi-Agent Systems

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.

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arXiv AI
Sep 2

EDGE: Error Dependency Graph-Guided Multi-Error Attribution in Multi-Agent LLM Systems

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
llmsagents
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arXiv AI
Sep 7

DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems

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
llmsagentsbenchmarks
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arXiv Machine Learning
Oct 12

Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels

The paper introduces SOAP, a method for attributing failures in large language model agents without requiring step-level labels or a separate set of successful trajectories. SOAP builds a spectral reference space from LLM representations to assign base error scores to each step, then uses attention-based soft dependencies to propagate downstream error signals back to their likely upstream causes. Experiments demonstrate that SOAP outperforms existing baselines in accurately identifying decisive steps responsible for task failures.

By Do Nguyen-Thanh, Junlin Fang, Bing Li, Zhen Fang, Sean Du
llmsagents
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Hugging Face Trending Papers
Oct 8

Error-Propagation Modeling for Failure Attribution in LLM-Based Multi-Agent Systems

The paper introduces EMFA, a method for pinpointing the decisive error in large‑language‑model‑based multi‑agent systems. EMFA builds a structured representation of a failed execution, models how errors cascade and loop, and then screens candidates with propagation awareness before verifying them counterfactually. 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.

llmsagentsbenchmarks
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