aiqbench analysis
The paper proposes a closed-loop method that generates new evaluation tasks from fine-grained diagnostics of where LLM agents fail, rather than relying on aggregate scores. Its key result is that all eight tested models score lower on the updated cybersecurity tasks, which suggests that iteratively refreshed, diagnostics-driven benchmarks may be a practical defense against saturation and contamination. Readers should note that the evidence comes from a single domain across two rounds and 66 tasks, so the generalizability of the approach remains to be shown.
EvoProbe: Evaluation-Driven Benchmark Evolution for LLM Agents
arXiv:2610.19433v1 Announce Type: new Abstract: Static benchmarks for large language model (LLM) agents face saturation and data contamination risks, motivating continual updates to evaluation tasks. However, aggregate scores and success rates offer limited insight into specific problem-solving bottlenecks and provide limited guidance for subsequent task design. We propose EvoProbe, an evaluation-driven benchmark evolution framework that integrates task generation, diagnostic evaluation, and feedback-driven regeneration into a closed loop. 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. In a cross-round comparison with the Examiner model set and solver configuration held fixed, all eight models achieve lower Outcome scores on the updated tasks, with relative decreases of 11.8\% in mean Outcome and 40.0\% in mean Pass\textasciicircum{}3. These results support using fine-grained diagnostics to guide iterative task updates and probe agents' current capabilities and limitations.
Abstract:Static benchmarks for large language model (LLM) agents face saturation and data contamination risks, motivating continual updates to evaluation tasks. However, aggregate scores and success rates offer limited insight into specific problem-solving bottlenecks and provide limited guidance for subsequent task design. We propose EvoProbe, an evaluation-driven benchmark evolution framework that integrates task generation, diagnostic evaluation, and feedback-driven regeneration into a closed loop. 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. In a cross-round comparison with the Examiner model set and solver configuration held fixed, all eight models achieve lower Outcome scores on the updated tasks, with relative decreases of 11.8\% in mean Outcome and 40.0\% in mean Pass\textasciicircum{}3. These results support using fine-grained diagnostics to guide iterative task updates and probe agents' current capabilities and limitations.
Artificial Intelligence (cs.AI)
arXiv:2610.19433 [cs.AI]
(or arXiv:2610.19433v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.19433 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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