Author

M. Mattar

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Jul 2026

Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models

Inference-time thinking improves the performance of large language models, but aggregate outcomes do not reveal whether models use available evidence more effectively or seek information that could improve future decisions. We distinguish these responses by measuring action preference, thinking length, and reported con...

Hua-Dong Xiong, Xinyuan Yan, Ji-An Li et al. · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

Learning Options for Compositional Motor Control with Adapter Banks

Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of a shared recurrent network, but leaves open how such a system is learned. We translate this principle into a novel architecture for learnin...

Sreejan Kumar, M. Mattar, Lea Duncker · 0 citations