2 papers
cs.AI2026
Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs
Seungwoo Jung, Dohyeok Kwon, Seungmin Cha +4
Mixture-of-experts (MoE) architectures scale large language models efficiently, but they demand massive GPU memory. To cope with such demand, models are commonly compressed to redu…
cs.RO2026
Action- and Language-Conditioned Video Assessment for Embodied Control
Hwanhee Kim, Jaehyun Jang, Seungmin Cha +3
Vision-based embodied agents executing multi-step natural language instructions require feedback mechanisms that assess task progress over complete trajectories. Conventional appro…