Evaluation of instrumented reasoning tools in less-robust regimes

Develop tool-use benchmarks for large language model agents that invoke verification-and-validation-instrumented tools, including robustness labels per call, to evaluate agent performance in regimes where pipelines provide trend-level rather than precise quantitative accuracy.

Background

Use 5 in the paper positions instrumented pipelines as callable reasoning tools, especially useful in extrapolative regimes where qualitative trends are reliable even if absolute values are not. Systematic evaluation of this use case is currently lacking.

Benchmarks that incorporate robustness labels can help assess how well agents weigh uncertainty bands and downweight extrapolative calls.

References

Nine open questions will determine whether instrumented data matures into a recognised substrate for scientific machine learning. Reasoning-tool evaluation for less-robust regimes. Tool-use benchmarks paired with V{content}V-instrumented tools, with robustness labels per call, are needed to score Use~5 agents that weight uncertainty bands and downweight extrapolative calls.

— Instrumented data for causal scientific machine learning  (2606.07865 - Wilke, 5 Jun 2026) in Section 7, Methodological questions for the community, Item 9

This offers a plausible explanation for the curriculum's performance gains, although call frequencies alone do not establish whether each invocation is appropriate or successful.

— DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding  (2610.21327 - Liang et al., 15 Oct 2026) in Appendix, Section “Tool Usage Analysis of DocPilot-4B”