arXiv AI
Auditing Safety Preference Supervision with Recorded Derivations
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Positive-Unlabeled Learning for Agent Safety False Alarm Auditing
The paper introduces a two‑stage framework for auditing false alarms in safety monitors for language‑model agents, treating the problem as a positive‑unlabeled (PU) ranking task. It adapts safe references toward the alarm domain, consolidates ordering from multiple PU models, and refines the ranking with structural guidance, all without requiring alarm safety labels. The method improves macro AUPRC to 0.6444 and recovers 33.3% more false alarms at a 5% review budget compared to leading PU baselines.
arXiv AI
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Refuse without Refusal: A Structural Analysis of Safety-Tuning Responses for Reducing False Refusals in Language Models
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arXiv AI
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Preferred, Not Safer: Pairwise Preference Is a Poor Proxy for Clinical Safety
arXiv:2608. 02617v1 Announce Type: cross Abstract: We evaluate whether clinician pairwise preferences provide a reliable signal of clinical safety in large language model (LLM) evaluation using expert feedback from MOOVE (Massive Open Online Validation and Evaluation), a clinician-led platform collecting blinded pairwise preferences alongside multi-criterion rubric ratings.
arXiv AI
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Rater State Bias in RLHF Preference Data: An Audit Framework
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Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges
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arXiv AI
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REA: From Correct LLM Judgments to Safe Decision Delivery
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