Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval

Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-Language Model (LVLM)-based retrievers are efficient and scalable, directly encoding raw multimodal inputs often misses fine-grained discriminative ...