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cs.CL2026

Metacognition in LLMs: Foundations, Progress, and Opportunities

Gabrielle Kaili-May Liu, Areeb Gani, Jacqueline Lu +3

Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become…

cs.CL2026

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs

Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona +2

Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in ke…

cs.AI2026

Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results

Jan Batzner, Sree Harsha Nelaturu, Damian Stachura +45

AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First…

cs.CL2026

SciMDR: Advancing Scientific Multimodal Document Reasoning

Ziyu Chen, Yilun Zhao, Chengye Wang +3

Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and realism. To address this…

cs.AI2026

Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training

Yixin Liu, Yue Yu, DiJia Su +7

Reasoning LLMs-as-Judges, which can benefit from inference-time scaling, provide a promising path for extending the success of reasoning models to non-verifiable domains where the…