arXiv AI
Four Ledgers, Not One Score: Responsible Communication of LLM-Judge Calibration in Biomedical ML
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Jun 9
Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture
arXiv:2606. 09500v1 Announce Type: new Abstract: Objective.
arXiv AI
Aug 26
More Rejective, Not More Discriminative: The Unit of Verification in Pre-Execution LLM Oversight
The paper introduces the twin‑prefix framework to evaluate how the size of the verification unit—i.e., how many actions a pre‑execution LLM monitor reviews in one call—affects its performance. By pairing each gold plan with a twin that differs by a single write and injecting a controlled error, the authors isolate the impact of review length on catch rates and false rejections. Their findings show that longer review windows increase rejection rates but do not improve discrimination, with the highest informedness occurring at one or two actions across all judges and domains.
arXiv AI
Jul 9
A Multi-Analyst LLM Pipeline for Auditable Rule Discovery Across 68 Public Physiological Corpora
arXiv:2607. 06802v1 Announce Type: cross Abstract: Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints.
arXiv AI
Sep 3
ClaimReceipt: Verifying Evidence Sufficiency and Coverage in Agent Evaluations
The paper introduces ClaimReceipt, a specification and verifier that checks whether a claim in an agent evaluation can be recomputed from retained evidence (sufficiency) and whether the evidence covers the entire experiment set (coverage). Using the CR‑2 verifier on 1,392 historical records, the authors demonstrate accurate reproduction of audit verdicts, non‑redundant field groups, and zero false positives on semantic faults. In a prospective CR‑3 run, the system correctly flags missing receipts and preserves coverage when private evidence is withheld, while adding minimal overhead to inference time and transaction size.
arXiv AI
Jul 16
Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows
arXiv:2607. 13078v1 Announce Type: cross Abstract: LLMs are now proposed for fraud detection, scam investigation, content moderation, and other trust-and-safety workflows.
arXiv AI
2d ago
Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
arXiv:2610.01616v1 Announce Type: cross Abstract: The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotat...