arXiv:2608. 16003v1 Announce Type: new Abstract: Automated checking pipelines increasingly place one language model as the checker and another (or the same one) as the fixer.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 09046v1 Announce Type: new Abstract: Useful audits reveal not only how often a model fails, but also where its failures concentrate.
arXiv:2608. 16852v1 Announce Type: new Abstract: Regulatory compliance monitoring in deployed language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy.
arXiv:2608. 10216v1 Announce Type: cross Abstract: Agent frameworks ship quality gates that compare text blocks by embedding-cosine similarity and decide at a fixed cutoff.
The paper investigates how procedural traces—detailed step-by-step accounts of a language model’s reasoning—affect the decision-making of LLM overseers tasked with auditing another model’s outputs. Using signal detection theory, the authors evaluated five overseers on 19 compliance tasks, finding that while error detection remains high when disconfirming evidence is always visible, more elaborate traces shift the decision criterion toward rejection, leading to increased false alarms. The study also shows that providing option labels reduces the stated inability to link evidence to options, yet some overseers still exhibit residual rejection of correct work that grows with trace detail.
arXiv:2606. 14530v1 Announce Type: new Abstract: Large language models encode rich information in their hidden states.
arXiv:2608.31016v1 Announce Type: cross Abstract: Ambient AI scribes draft clinical notes, and published audits find their dominant error is omission: information the encounter established that the n...