Hugging Face Trending Papers August 4, 2026

MuRA: Multi-Rank Adaptation for Efficient and Effective Test-Time Vision-Language Generalization

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Vision-language models exhibit remarkable zero-shot capabilities but suffer significant performance degradation under distribution shifts. While test-time adaptation (TTA) via Low-Rank Adaptation offers a parameter-efficient solution, we identify a fundamental bottleneck in current methods: the reliance on static rank configurations.

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Related stories

RSLoRA: Training-free Rank Allocation for LoRA via Representational Sensitivity Probing

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

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