arXiv:2604. 14892v3 Announce Type: replace-cross Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators.
By Amy Rouillard, Sitwala Mundia, Linda Camara, Ziyaad Dangor, Michael Cameron Gramanie, Ismail Kalla, Shabir A. Madhi, Kajal Morar, Marlvin T. Ncube, Haroon Saloojee, Bruce A. Bassett
arXiv:2608.30676v1 Announce Type: new
Abstract: When medical AI systems hallucinate clinical reasoning, the consequences extend beyond incorrect answers: fabricated justifications that superficially...
By Jiangwang Chen, Chenghao Zhang, Hengxing Cai
The paper introduces a framework for evaluating AI systems that not only checks final labels but also tracks the reasoning behind them through three core sources—grounds, norms, and authority—forming an eight-cell counterfactual judgment cube. It defines minimal source replacement sets, called judgment receipts, to explain changes in verdicts and provides certification cost bounds for black-box evaluators. The authors present ReasonBench, a benchmark with 19,520 cases, and demonstrate that while high standard accuracy can mask robustness issues, receipt accuracy reveals significant gaps in reasoning consistency across different models.
By Ye Chen, Weining Zhang
The paper proposes a new method for evaluating AI accountability by analyzing the structural quality of a model’s defense for its decisions, using a four‑phase dialectical protocol based on Walton’s argumentation schemes and Govier’s criteria. Applied to nine large language models and 200 ambiguous moral-choice items, the study finds that models generally defend their reasoning well above the rubric minimum, though failures cluster on grounds and sufficiency and correlate with epistemic hedging. The protocol also reveals that models often present different argument schemes in justification than in reasoning, detects indefensible defenses, and highlights challenges in assessing retraction in AI alignment.
By Daan R. Henselmans, Derck W. E. Prinzhorn, Arno Libert
LogiMed‑RoB is a new benchmark that tests large language models (LLMs) on hierarchical logical consistency in medical risk‑of‑bias assessments, using 860 randomized controlled trials and 14,820 queries based on Cochrane Risk of Bias 2.0 expert logic. The benchmark evaluates models across four dimensions—Atomic Consistency, Domain Consistency, Aggregation Consistency, and Evidential Faithfulness—revealing a catastrophic error‑compounding effect where high atomic accuracy does not translate to end‑to‑end consistency. Experiments on ten state‑of‑the‑art LLMs show that even top models can fail to deduce correct outcomes in a significant portion of cases, highlighting a gap between evidence retrieval and reasoning.
whyItMatters":"The study shows that high outcome accuracy can mask critical reasoning flaws, emphasizing the need for white‑box logical verification before deploying LLMs in clinical settings."
By Jiayu Huang, Zichen Tang, Qianhui Ling, Zemin Kuang, Haihong E
arXiv:2608. 12645v1 Announce Type: new Abstract: LLM judges have become central infrastructure for model evaluations, online grading, and reward modeling.
By Justin Zhao, Himaghna Bhattacharjee, Hannah Korevaar, Bhaktipriya Radharapu, Khalid El-Arini