3 papers
cs.AI2026
Reasoning Jury: Multi-Model Consensus for Evaluating Reasoning Traces
Congchao Wang, Diwakar Singh, Qiaozi Gao +3
Improving reasoning LLMs requires the ability to judge the quality of long reasoning traces for effective reasoning data curation, strong training signals during reinforcement lear…
cs.CR2026
Evaluating Nova 2.0 Lite model under Amazon's Frontier Model Safety Framework
Satyapriya Krishna, Matteo Memelli, Tong Wang +5
Amazon published its Frontier Model Safety Framework (FMSF) as part of the Paris AI summit, following which we presented a report on Amazon's Premier model. In this report, we pres…
cs.CL2025
D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models
Satyapriya Krishna, Andy Zou, Rahul Gupta +6
The safety and alignment of Large Language Models (LLMs) are critical for their responsible deployment. Current evaluation methods predominantly focus on identifying and preventing…