Author

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

Modern forecasting models are often updated after deployment so they can respond to changing data. These updates can also make predictions worse, so practical systems need a reliable monitor that can detect harmful changes and trigger protection. A natural design is to monitor the same prediction errors that guide the...

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