Tag
This paper proposes AdaRankLLM, an adaptive retrieval framework that challenges the necessity of adaptive RAG by using listwise ranking to dynamically filter retrieved passages. The work shows that adaptive retrieval serves as a noise filter for weaker models while acting as a cost-efficiency optimizer for stronger models, with extensive experiments across multiple datasets and LLMs.
RAG-Anything is a new open-source framework that enhances multimodal knowledge retrieval by integrating cross-modal relationships and semantic matching, outperforming existing methods on complex benchmarks.
A privacy-focused local deep research tool that supports various LLMs and search engines to achieve high accuracy on QA tasks while keeping data encrypted and local.