aiqbench analysis
This paper introduces a formal framework to audit AI evaluation artifacts, revealing that many reported metrics lack the necessary historical evidence or semantic grounding to license their associated claims. By censusing 124 Inspect Evals units, the authors demonstrate how deterministic inference often fails due to missing context, offering instead a structured analysis of instability and stable substructures rather than binary robustness labels. This work is critical for researchers seeking rigorous verification methods in machine learning evaluation pipelines.
What Does an Evaluation License? A Commit-Bound Census of Claim-Relative Inference in Inspect Evals
arXiv:2608.19269v3 Announce Type: cross Abstract: Evaluation artifacts specify a forward computation: a task, scorer, and reported metric. They do not necessarily license the claim attached to that metric because the historical evidence and alternative semantics needed to replay it may be unbound. We formalize this missing claim-replay layer through a frozen substrate D, a grounded family F, a claim query q, and the resulting identified set. We then census all 124 mechanically eligible Inspect Evals units at a pinned commit. Every unit receives a terminal disposition; 110 stop before deterministic inference because required historical evidence or semantic grounding is unavailable. Where execution closes, exact values, winners, complete orders, and pairwise relations separate by claim resolution and by primary versus review family. The audit therefore returns typed stops, instability witnesses, and stable substructure rather than forcing one evaluator meaning or one robust/not-robust label.
Title:What Does an Evaluation License? A Commit-Bound Census of Claim-Relative Inference in Inspect Evals
Abstract:Evaluation artifacts specify a forward computation: a task, scorer, and reported metric. They do not necessarily license the claim attached to that metric because the historical evidence and alternative semantics needed to replay it may be unbound. We formalize this missing claim-replay layer through a frozen substrate D, a grounded family F, a claim query q, and the resulting identified set. We then census all 124 mechanically eligible Inspect Evals units at a pinned commit. Every unit receives a terminal disposition; 110 stop before deterministic inference because required historical evidence or semantic grounding is unavailable. Where execution closes, exact values, winners, complete orders, and pairwise relations separate by claim resolution and by primary versus review family. The audit therefore returns typed stops, instability witnesses, and stable substructure rather than forcing one evaluator meaning or one robust/not-robust label.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2608.19269 [cs.SE]
(or arXiv:2608.19269v3 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2608.19269 Focus to learn more arXiv-issued DOI via DataCite
census/CENSUS_SUMMARY.json
census/TERMINAL_RECORDS.json
contract/CONTRACT.md
contract/records/agentdojo/audit.normalized.json
contract/records/agentdojo/report.json
contract/records/automlbenchmark/audit.normalized.json
contract/records/automlbenchmark/report.json
contract/records/beir-scifact/audit.normalized.json
contract/records/beir-scifact/report.json
evidence/ANALYSIS_ROLES.json
evidence/EXPLORATORY_SOURCE_AUTHORIZATION.json
recomputation/INDEPENDENT_RECOMPUTATION.json
recomputation/source_tables/ANIMA.md
recomputation/source_tables/BFCL.md
recomputation/source_tables/MORU.md
reviewer/MANUSCRIPT_BINDING.json
reviewer/RESULTS.md
verification/verify_supplement.py
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Full text under the publisher's open licence · 278 words
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