arXiv:2606. 15127v1 Announce Type: new Abstract: Reasoning models are increasingly used in settings where the final answer is not the only object of review: educational tools may show students intermediate steps, decision-support systems may require human oversight, and audit workflows may inspect traces for misleading or biased input.
By Xian Sun, Wei Gao, Yingshuo Wang, Lingdong Kong, Yanhang Li, Zhichao Fan, Zexin Zhuang, Wenlong Dong, Zhiyuan Zheng, Hrishikesh Paranjape, Abhishek Mandal, Johnny R. Zhang
arXiv:2609.38107v1 Announce Type: cross
Abstract: Chain-of-thought traces are widely read as records of how models reach their answers, informing debugging, agent auditing, and claims about reasoning...
By Ratish Puduppully, Pranabendu Misra, Paarth Iyer, Durgesh Kalwar, Vardhan Palod, Subbarao Kambhampati
The paper evaluates safety monitors by measuring recall only on prompts that the target model actually answers, rather than on all harmful prompts. Across several guard systems, recall at a 1% false‑positive rate drops sharply when focusing on answered prompts, with monitors catching refused requests 1.1–6.4 times more often than answered ones. Rewriting prompts to be less explicit dramatically increases compliance and reveals that many harmful requests slip past monitors, especially when phrasing is softened. Fine‑tuning guards on these rewritten prompts improves recall from 0.24 to 0.89 on answered requests and generalizes to unseen benchmarks.
By Sripad Karne
arXiv:2607.23458v2 Announce Type: replace
Abstract: Chain-of-thought (CoT) explanations support oversight only if they are faithful: the stated reasoning must actually produce the answer. Auditing bl...
By Suramya R. Angdembay, Dikshant Aryal, Nick Rahimi
The study investigates the role of rationales in role‑specialized question‑answering pipelines by fixing evidence and candidate answers while varying only the rationale sent from a reasoner to a verifier. Experiments on 400 examples from MuSiQue, HotpotQA, and 2WikiMultiHopQA show that faithful rationales add almost no answer accuracy, whereas corrupted rationales significantly alter support judgments, especially under explicit rationale‑checking prompts. Human audits reveal that many corrupted rationales lead to mistrust or rejection, highlighting the importance of evaluating rationale sharing as a verification mechanism rather than merely a means to improve accuracy.
By Jiameng Zhang, Hongqiu Wu
arXiv:2607. 08456v1 Announce Type: cross Abstract: A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones resting on a false premise.
By Benedikt J. Wagner