A function-aware preference alignment framework that improves functional preservation by fine-tuning models to favor function-preserving sequences over function-disrupting alternatives, avoiding the need for explicit function optimization.
Nilufer Tamatgar, Soobin Park, Ying-Hua Yao et al.· Proceedings of the 32nd ACM...· 0 citations
Protein inverse folding models conditioned on structure achieve high sequence recovery but often fail to preserve biological function due to the lack of functional supervision. We propose a function-aware preference alignment framework that improves functional preservation by fine-tuning models to favor function-preser...
Nilufer Tamatgar, Soobin Park, Yinghua Yao et al.· Proceedings of the 32nd ACM...· 0 citations
Experiments show that CASE achieves a 37\% average per-setting relative improvement in overall CoT faithfulness over the strongest baselines, exhibits stronger cross-dataset faithfulness transfer, and maintains competitive average accuracy.
This work forms the specialization of pretrained virtual screening models to individual pockets as a test-time adaptation problem and proposes PETA, a parameter-efficient framework that directly adapts pretrained model at test time and outperforms both pretrained and fully retrained baselines while updating only the La...
Jia-Qi Lin, Ying-Hua Yao, Chang-Ran Wang et al.· 0 citations
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