3 papers
cs.HC2026
PERSONAJUDGE: Simulating Individual Human Preference Judgments with Evaluator-Specific Demonstration Data
Zeyu He, Xuan Qi, Subramanian Chidambaram +4
Large language models increasingly serve as judges in AI evaluation, but current approaches rely on consensus preferences that ignore individual evaluator variation. We propose a n…
cs.CL2026
Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
Xuan Qi, Rongwu Xu, Zhijing Jin
Aligning large language models (LLMs) with human preferences is a critical challenge in AI research. While methods like Reinforcement Learning from Human Feedback (RLHF) and Direct…
cs.CL2026
Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents
Xuan Qi
How much should a language agent think before taking action? Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning…