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Li-Yin Liang

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Preprint Sep 2026

Learning to Reason with Compressed Context: Ground-Truth-Free Adaptation of OmniLLMs via Self-Distillation

Omni-modal large language models (OmniLLMs) enable unified audio-video understanding, but their long multimodal token sequences make deployment computationally expensive. Token compression reduces this cost, yet aggressive compression often lowers accuracy. Existing works predominantly focus on designing better compres...

Jiang-Hao Wang, Ke Meng, Jian Li et al. · 0 citations
#machine learning Preprint Oct 2026

The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptation

This paper shows why reliable deployment requires checking the monitor's assumptions, comparing adaptation with the frozen model under realistic feedback, and limiting the effect of every protective response, and exposes a basic failure mode in self-monitored adaptation.

Wei-Jia Han, Li-Sha Qu, Zhen-Da Li et al. · 0 citations

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