Author

Junseok Lee

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#artificial intelligence Preprint Sep 2026

Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs

PARSER is a new residual sparsification method that shifts the compression objective from minimizing isolated matrix errors to preserving the expert output error, and achieves this by introducing output importance, which measures the actual contribution to the expert output error.

Seung-Woo Jung, Dohyeok Kwon, Seung-Min Cha et al. · 0 citations

#artificial intelligence Preprint Jan 2026

FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation

Experimental results demonstrate that FastSLM achieves competitive performance across diverse speech-language tasks while requiring substantially fewer speech tokens and FLOPs than existing speech-language models.

Junseok Lee, Chang-Jae Chun · 1 citation