AI RESEARCH
SLQ: Bridging Modalities via Shared Latent Queries for Retrieval with Frozen MLLMs
arXiv CS.CV
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ArXi:2604.13710v1 Announce Type: new Multimodal Large Language Models (MLLMs) exhibit strong reasoning and world knowledge, yet adapting them for retrieval remains challenging. Existing approaches rely on invasive parameter updates, such as full fine-tuning and LoRA, which may disrupt the pre-trained semantic space and impair the structured knowledge essential for reasoning. In this work, we argue that adapting MLLMs for retrieval should focus on eliciting pre-trained representations rather than overwriting them.