Counterfactual Tool Ranking under Utility, Cost, and Privilege Constraints
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Computer Science > Machine Learning
arXiv:2609.22819 (cs)
[Submitted on 19 Sep 2026]
Title:Counterfactual Tool Ranking under Utility, Cost, and Privilege Constraints
Authors:Jiapeng Li
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Abstract:Counterfactual tool evaluation must distinguish authority, historical support, and what a comparison actually estimates. We study these distinctions with eleven executable enterprise-inspired tools, exact-propensity logs, and real local Model Context Protocol transport. An initial 45-run synthetic study is retained, then challenged by 30 realized-return control runs and 15 experiments on 1,930 independently released Berkeley Function Calling Leaderboard (BFCL) tasks. Full-return direct regression reverses an initially favorable doubly robust (DR) evaluation result in the linear setting: mean absolute errors are 0.0139 for direct regression and 0.0272 for DR. Under a shifted environment, DR retains an advantage, with errors 0.0227 versus 0.0948. On function-name-group-disjoint BFCL-derived splits, direct and DR selectors obtain balanced accuracies of 81.85% and 79.83%. Two pinned local Qwen2.5 models are evaluated on the same 200 held-out tasks, exposing a strong failure to abstain under the fixed prompt. We further characterize policy differences under missing support: unsupported actions shared by two policies cancel, allowing point identification of an incremental change when neither absolute value is identifiable. A disagreement-preserving fallback achieves this property in all five support-gap runs, but conservative sampling bounds do not certify deployment improvement. The contribution is a falsifiable evaluation method and independent public evidence, not a new DR estimator, official BFCL leaderboard score, or production-agent safety claim.
| Comments: | Working paper, version 2. Includes synthetic execution studies, BFCL-derived function-selection experiments, and local open-weight LLM baselines. Code and artifacts: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.22819 [cs.LG] |
| (or arXiv:2609.22819v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.22819
arXiv-issued DOI via DataCite (pending registration)
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