3 papers
cs.LG2026
Supplement Generation Training for Enhancing Agentic Task Performance
Young Min Cho, Daniele Bonadiman, Divya Bhargavi +8
Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are…
cs.CL2025
SEAL: Scaling to Emphasize Attention for Long-Context Retrieval
Changhun Lee, Minsang Seok, Jun-gyu Jin +2
While many advanced LLMs are designed to handle long sequence data, we can still observe notable quality degradation even within the sequence limit. In this work, we introduce a no…
cs.CV2025
PTQ4VM: Post-Training Quantization for Visual Mamba
Younghyun Cho, Changhun Lee, Seonggon Kim +1
Visual Mamba is an approach that extends the selective space state model, Mamba, to vision tasks. It processes image tokens sequentially in a fixed order, accumulating information…