47 citations · 70 across the 17 of their papers we have counts for
20 papers
Learning Options for Compositional Motor Control with Adapter Banks
Sreejan Kumar, Marcelo Mattar, Lea Duncker
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…
Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models
Hua-Dong Xiong, Xinyuan Yan, Ji-An Li +3
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 infor…
Beyond DSA: Conjugacy-based Comparison of Dynamical Systems
Prakhar Godara, Pang Shiang Tay, Marcelo G. Mattar
Comparing whether two dynamical systems implement the same computation despite differences in coordinates or measurements is a central problem in neuroscience and machine learning.…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
Sixing Chen, Ji-An Li, Saner Cakir +3
Large language models (LLMs), especially reasoning models, generate extended chain-of-thought (CoT) reasoning that often contains explicit deliberation over future outcomes. Yet wh…
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews +6
Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent action. Can modern AI systems lea…