3 papers
cs.CL2026
Large language models reorganize representational geometry during in-context learning
Hua-Dong Xiong, Li Ji-An, Robert C. Wilson +2
Large language models (LLMs) show remarkable flexibility in adapting to novel tasks without parameter updates, a capacity known as in-context learning (ICL). Prior work has sought…
q-bio.NC2026
The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities
Xin Tong, Thi Thu Uyen Hoang, Xue-Xin Wei +1
Humans systematically misrepresent probability in a stereotyped inverse-S pattern. It has been documented for decades, but its origin remains unexplained. We propose a Bayesian enc…
cs.LG2026
In-context superposition: human-like working memory interference in large language models
Hua-Dong Xiong, Li Ji-An, Jiaqi Huang +3
Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments. This capacity, known as working memory, is fundamental to human…