arXiv Machine Learning By Qiaomu Li, Qiuyuan Zhang, Nong Ming

Beyond the Query: Do Retrieval Signals Improve Adaptive Multimodal RAG Routing?

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The paper investigates whether retrieval-time signals add value to Adaptive Retrieval-Augmented Generation (RAG) routing when the query is already known. Experiments across document, audio, and video RAG compare routers that use only the query versus those that also incorporate retrieval signals, keeping all other factors constant. The results show that adding retrieval signals does not reliably improve routing decisions over a query-only baseline, suggesting that any routing benefit must be explicitly demonstrated rather than presumed.

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arXiv Computer Vision
Oct 9

Autoregressive Retriever: Improving Query Understanding from Item Feedback for Universal Multimodal Retrieval

The paper introduces the AutoRegressive Retriever (ARR), a multimodal retrieval model that iteratively refines a query embedding by selecting informative items and incorporating their content. ARR alternates between retrieval and query update, using stepwise contrastive supervision and reinforcement learning to optimize item selection and final ranking. Experiments show ARR outperforms baselines on in‑domain and zero‑shot benchmarks, with feedback improving both inference performance and initial query embeddings.

By Jianfei Zhao, Yifan Wang, Feng Zhang, Xin Sun, Chong Feng, Zhixing Tan, Yang Luo, Boyuan Pan, Xu Kai, Yao Hu