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Author

Ge Su

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Preprint Aug 2026

SkillAligner: Treating Retrieved Skills as Adaptable Drafts at Execution Time

SkillAligner is proposed, a training-free execution-time skill adaptation framework that treats retrieved skills as adaptable drafts rather than fixed instructions that substantially improves task performance over existing skill-use baselines, reduces skill-induced regressions at the instance level, and lowers total in...

Qin-Feng Li, Dalin He, Yun-Tai Bao et al. · 0 citations
Review Jul 2026

Federated Learning Meets Test-Time Adaptation: Methods, Challenges, and Future Directions

A comprehensive survey of FedTTA is provided, formalizing its problem setting and establishing a unified taxonomy encompassing three paradigms: i) Federated Initialization and Test Fine-tuning, where the global model serves as a robust prior for local refinement; ii) Federated Shared Backbone and Test Personalized Adap...

Chen Zhang, Ge Su, Huaxia Zhou et al. · 0 citations

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