3 papers
cs.IR2026
FRESCO: Benchmarking and Optimizing Re-rankers for Evolving Semantic Conflict in Retrieval-Augmented Generation
Sohyun An, Hayeon Lee, Shuibenyang Yuan +4
Retrieval-Augmented Generation (RAG) is a key approach to mitigating the temporal staleness of large language models (LLMs) by grounding responses in up-to-date evidence. Within th…
cs.IR2026
MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
Zilin Xiao, Qi Ma, Mengting Gu +4
Universal multimodal embedding models have achieved great success in capturing semantic relevance between queries and candidates. However, current methods either condense queries a…
cs.AI2025
Language Self-Play For Data-Free Training
Jakub Grudzien Kuba, Mengting Gu, Qi Ma +3
Large language models (LLMs) have advanced rapidly in recent years, driven by scale, abundant high-quality training data, and reinforcement learning. Yet this progress faces a fund…