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TraceElephant benchmark introduces full execution traces for diagnosing failures in LLM-based multi-agent systems
arXiv cs.MA (Multi-Agent) · April 27, 2026
AI Summary
- •Researchers introduced TraceElephant, a benchmark designed for failure attribution (identifying which agent and step caused a failure) in LLM-based multi-agent systems (AI systems where multiple language models work together and reason in natural language).
- •Full execution traces improve attribution accuracy by up to 76% over partial-observation counterparts; existing benchmarks omit inputs and context that developers use when debugging, whereas TraceElephant captures complete traces and reproducible environments.
- •The benchmark aims to advance failure attribution research and promote evaluation practices aligned with real-world debugging scenarios, supporting development of more transparent multi-agent systems.
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