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TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- TrajDebug framework identifies critical errors in long multi-step LLM agent trajectories by tracing error lifecycle and determining which failures caused final task failure.
- Why it matters
- Engineers building or debugging LLM-based agents need this when multi-step tasks fail and they must pinpoint which early mistake cascaded into the final error.
- Watch out
- The benchmark contains only 486 manually annotated trajectories from two sources, so performance on other agent types or domains remains unclear.
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