2 papers
cs.IR2026
Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders
Dojun Hwang, Seunghan Lee, Cheonyoung Park +2
While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains challenging. Real-world items are d…
cs.CV2026
Focus, Don't Prune: Identifying Instruction-Relevant Regions for Information-Rich Image Understanding
Mincheol Kwon, Minseung Lee, Seonga Choi +7
Large Vision-Language Models (LVLMs) have shown strong performance across various multimodal tasks by leveraging the reasoning capabilities of Large Language Models (LLMs). However…