Author

Seung-Min Cha

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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

Open access Aug 2026

Action- and Language-Conditioned Video Assessment for Embodied Control

ALVA (Action- and Language-Conditioned Video Assessment), a trajectory evaluator that conditions its assessment on visual observations, the executed action sequence, and the natural language instruction, provides more effective feedback than the evaluated static image and embedding-based visual baselines and reduces th...

Hwanhee Kim, Jaehyun Jang, Seung-Min Cha et al. · Italian National Conference... · 0 citations