BenchTrace: A Benchmark for Testing Reflection Ability and Controlled Evolution in LLM Agents
arXiv:2605.29225v1 Announce Type: new Abstract: Self-evolving agents improve over time by reflecting on past failures, but existing evaluation is limited in two ways: it measures only task scores, leaving reflection quality unknown, and it relies on agents' own episode runs, offering no mechanism to target specific failure patterns. We present \textbf{BenchTrace}, a benchmark for evaluating self-evolution ability in LLM agents. BenchTrace is built on a snapshot-reflection dataset of 1,821 annotated episodes spanning six diverse tasks, and comprises a \textbf{Reflection Evaluation} that probes
The rapid advancement of large language models necessitates better evaluation methods for their autonomous capabilities as they move towards self-improvement.
This benchmark addresses a critical gap in assessing true reflection and controlled evolution in AI agents, which is fundamental for their reliable deployment and scaling.
The ability to accurately evaluate and guide the self-evolution of LLM agents moves closer to reality, shifting from opaque progress to measurable improvement.
- · AI Agent developers
- · Companies deploying AI agents
- · Open-source AI community
- · Researchers in AI safety
- · Inefficient LLM agent evaluation methodologies
- · Companies relying on ad-hoc agent testing
More effective and robust AI agents will be developed, accelerating their integration into various workflows.
Increased trust and adoption of autonomous AI systems as their self-improvement mechanisms become auditable and steerable.
The acceleration of AI agents could significantly reshape white-collar work and redefine software paradigms faster than anticipated.
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Read at arXiv cs.AI