3 accepted papers
Chain-of-Thought (CoT) reasoning has significantly improved the performance of Large Language Models (LLMs) but comes with substantial computational costs due to excessive token consumption. Existing approaches to reduce inference latency, such as explicit length penalties, often degrade reasoning q…
Predicting protein mutation effects is fundamental to protein engineering and disease variant interpretation, yet experimental mutation data remain accurate but extremely sparse. Large-scale computational augmentation offers scalability, but introduces heterogeneous and poorly calibrated supervision…
Understanding protein dynamics is critical for elucidating their biological functions. The increasing availability of molecular dynamics (MD) data enables the training of deep generative models to efficiently explore the conformational space of proteins. However, existing approaches either fail to…