2 papers
cs.IR2026
Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform
Seungho Han, Byeongchang Kim, Jin Yu
Contrastive learning is widely used for user modeling in large-scale recommender systems, where standard in-batch negatives implicitly assume universal exposure that any user can b…
cs.CL2026
Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
Young-Jun Lee, Seungone Kim, Minki Kang +5
Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrated into evolutionary search h…