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cs.AI2026
IntElicit: Eliciting and Assessing Contextualized Creativity via Dialogue Policy Optimization
Mingjia Li, Jin Wu, Hong Qian +7
Contextualized assessment offers high ecological validity for evaluating creativity but introduces a critical challenge: observed performance may be confounded with cognitive profi…
cs.AI2026
RealMath-Eval: Why SOTA Judges Struggle with Real Human Reasoning
Yiteng Mao, Kenan Xu, Yijia Lyu +3
While Large Language Models (LLMs) have achieved near-perfect performance in \emph{solving} high-school mathematics, their ability to \emph{evaluate} the diverse reasoning processe…
cs.AI2026
OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation
Haochen Yang, Ke Zhao, Mengyuan Ma +3
Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient paradigm for automated optimiza…