Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains. However, traditional TTA methods become ineffective when the label distribution shift occurs, a challenge commonly referred to as an open-world scenario. In this paper, we introduce a new method named Reliable Neural Collapse approximation (ReNC) for Open-World Test-Time Adaptation (OWTTA). Specifically, we leverage neural collapse as a structural prior for reliable target-domain adaptation. Guided by this prior, we justify that the pre-trained classifier weights can serve as the prototypes of the source domain. By measuring the similarity between samples and prototypes, we filter out the Out-Of-Distribution~(OOD) samples for reliable updates. Furthermore, we propose a neural collapse approximation mechanism to refine these prototypes, ensuring they can gradually adapt to the target domain while maintaining the neural collapse structure. Extensive experiments on several open-world benchmarks demonstrate the superiority of the proposed method. Our empirical analysis suggests that ReNC better preserves NC-related properties in the target domain, providing useful evidence for explaining reliable OWTTA and offering new insights for model design. Code is available at https://github.com/JiaqiLin-AI/ReNC.
Jia-Qi Lin, Yuangang Pan, Changran Wang et al.· 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-preserving sequences over function-disrupting alternatives, avoiding the need for explicit function optimization. Our approach constructs reliable preference pairs in silico using hypothesis-driven perturbations of critical residues and model-consistent likelihood constraints, enabling scalable supervision without additional wet-lab measurements. The resulting framework guides protein sequence design models toward generating sequences that better preserve functional integrity, while remaining compatible with existing inverse folding pipelines such as ProteinMPNN and ESM-IF. Extensive experiments on protein design benchmarks and enzyme datasets with established wet-lab validation show that our fine-tuned models consistently outperform pretrained counterparts in preserving functional integrity during protein sequence design. The code is available at https://github.com/EvaFlower/Function-aware-Protein-Inverse-Folding
Nilufer Tamatgar, Soobin Park, Yinghua Yao et al.· Proceedings of the 32nd ACM...· 0 citations
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 LayerNorm parameters.
Jia-Qi Lin, Yinghua Yao, Changran Wang et al.· 0 citations
