2 papers
cs.CL2026
Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning
Seungmin Oh, Donggeon Lee, Jongbin Ryu
Large language models achieve strong performance across diverse tasks, but deployment remains costly because of memory, latency, and energy demands. Structured pruning reduces thes…
cs.CV2026
Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models
Seungmin Oh, Seunghun Kang, Jongbin Ryu
Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many existing methods overlook data chara…