The authors developed EvoProbe, a closed-loop framework combining task generation, diagnostic evaluation, and feedback-driven regeneration.
Diagnostic feedback drives iterative benchmark evolution revealing hidden LLM agent weaknesses
EvoProbe integrates task generation, multi-judge trajectory evaluation, and feedback-driven regeneration to probe agents' capability boundaries beyond aggregate scores.
Research Lab
Qianhong Guo · Wei Xie · Xiaofang Cai · Shuoyoucheng Ma · Chengxin Hu · Baosheng Wang
National University of Defense Technology · Institute of Information Engineering, Chinese Academy of Sciences
Research Digest··3 min read
The authors propose EvoProbe, a framework that uses diagnostic feedback from LLM agent evaluations to iteratively generate new, more challenging tasks.
Why this paper
From Institute of Information Engineering, Chinese Academy of Sciences and National University of Defense Technology
In one line
EvoProbe uses fine-grained diagnostics to iteratively generate harder benchmark tasks that probe LLM agent capabilities.
What we could check
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- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks
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