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20242026
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cs.CL2026

Learning User Simulators with Turing Rewards

Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu +5

Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research in the social sciences, and…

cs.CL2026

Evaluating Language Models' Evaluations of Games

Katherine M. Collins, Cedegao E. Zhang, Graham Todd +9

Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily f…

cs.CL2025

Code-enabled language models can outperform reasoning models on diverse tasks

Cedegao E. Zhang, Cédric Colas, Gabriel Poesia +2

Reasoning models (RMs), language models (LMs) trained with reinforcement learning to produce long-form natural language reasoning, have been remarkably successful, but they still r…

cs.CL2025

On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts

Linlu Qiu, Cedegao E. Zhang, Joshua B. Tenenbaum +2

Language use is shaped by pragmatics -- i.e., reasoning about communicative goals and norms in context. As language models (LMs) are increasingly used as conversational agents, it…

cs.CL2025

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models

Lionel Wong, Katherine M. Collins, Lance Ying +8

When faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions. W…

cs.CL2025

Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-The-Fly

Lance Ying, Ryan Truong, Katherine M. Collins +6

Drawing real world social inferences usually requires taking into account information from multiple modalities. Language is a particularly powerful source of information in social…