3 papers
q-bio.NC2026
Training-Driven Representational Geometry Modularization Predicts Brain Alignment in Language Models
Yixuan Liu, Zhiyuan Ma, Likai Tang +5
How large language models (LLMs) align with the neural representation and computation of human language is a central question in cognitive science. Using representational geometry…
eess.SY2024
Brain-Like Replay Naturally Emerges in Reinforcement Learning Agents
Jiyi Wang, Likai Tang, Huimiao Chen +2
Replay is a powerful strategy to promote learning in artificial intelligence and the brain. However, the conditions to generate it and its functional advantages have not been fully…
cs.CL2024
UniMem: Towards a Unified View of Long-Context Large Language Models
Junjie Fang, Likai Tang, Hongzhe Bi +12
Long-context processing is a critical ability that constrains the applicability of large language models (LLMs). Although there exist various methods devoted to enhancing the long-…