EvoProbe: Evaluation-Driven Benchmark Evolution for LLM Agents
We propose EvoProbe, an evaluation-driven benchmark evolution framework that integrates task generation, diagnostic evaluation, and feedback-driven regeneration into a closed loop.
ProofPaper ↗
Key points
- Static benchmarks for large language model (LLM) agents face saturation and data contamination risks, motivating continual updates to evaluation tasks.
- It combines verifiable subtask outcome scoring with evidence-supported multi-judge trajectory evaluation and distills phase-level bottlenecks, success patterns, and failure patterns into diagnostic feedback for generating new executable tasks.
- Across two rounds of cybersecurity experiments, evaluations of eight models on 66 tasks reveal phase-level performance differences and common failure patterns beyond aggregate scores.
- These results support using fine-grained diagnostics to guide iterative task updates and probe agents' current capabilities and limitations.
Sources (1)
- [1]EvoProbe: Evaluation-Driven Benchmark Evolution for LLM AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 02:27 AM
We propose EvoProbe, an evaluation-driven benchmark evolution framework that integrates task generation, diagnostic evaluation, and feedback-driven regeneration into a closed loop.
Static benchmarks for large language model (LLM) agents face saturation and data contamination risks, motivating continual updates to evaluation tasks.
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