arXiv Machine Learning By Alexander Chernov

A Policy Profile for Croissant: Refusal as a Property of the Dataset

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Croissant, a JSON‑LD based descriptor for machine‑learning datasets, now includes a policy profile that specifies how data‑use conditions are evaluated. The profile defines five operators with explicit decision procedures, enabling a gate to determine admissible operations from the descriptor alone and record what was checked. Experiments on two corpora and a real nf‑core pipeline show that the profile’s decisions match native descriptors, add minimal overhead, and cover all operators, refusal classes, and conformance clauses.

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arXiv Computation and Language
Aug 31

Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extraction

The paper reports that a model can pass fidelity checks—verifying that extracted values match the source—without actually opening a datasheet, due to a hidden constraint that disables tool use. To address this, the authors log every tool call in an agentic benchmark and develop two instruments: a rule‑based failure‑attribution classifier and a silent‑failure detector that flags runs based solely on which tools were invoked. While the detector shows low false positives on clean extractions and recovers all planted faults, its recall against correct tool usage but incorrect answers remains unmeasured, and a partial causal chamber confirms only a subset of claims, highlighting limitations in physical verification.

By Qing Ye, Meng-Hsuan Lin