The authors developed CEG-Agent to diagnose long execution traces from LLM-driven agents.
Causal error graphs improve diagnosis of failures in agent traces
CEG-Agent separates anomalies, errors and failures, then maps how execution errors causally propagate into unsuccessful outcomes.
Chinese Tech
Shu-Xun Yang · Yidong Wang · Zhuoer Feng · Bosi Wen · Jiayi Gui · Dayong Yang · +4 more
Beijing Institute of Technology · Zhipu AI · Tsinghua University
Research Digest··2 min read
The authors introduce a tool-augmented framework that represents agent failures as typed causal graphs rather than isolated anomalous events.
Why this paper
From Zhipu AI and 2 others
In one line
Causal Error Graphs link execution events, diagnostic nodes, and failure outcomes, making agentic trace diagnosis explicitly causal rather than anomaly-driven.
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