Tootfinder

MODE: Mixture of Document Experts for RAG
Rahul Anand
https://arxiv.org/abs/2509.00100 https://arxiv.org/pdf/2509.00100

MODE: Mixture of Document Experts for RAG
Retrieval-Augmented Generation (RAG) often relies on large vector databases and cross-encoders tuned for large-scale corpora, which can be excessive for small, domain-specific collections. We present MODE (Mixture of Document Experts), a lightweight alternative that replaces fine-grained nearest-neighbor search with cluster-and-route retrieval. Documents are embedded, grouped into semantically coherent clusters, and represented by cached centroids. At query time, we route to the top centroid(s)…

PentaRAG: Large-Scale Intelligent Knowledge Retrieval for Enterprise LLM Applications
Abu Hanif Muhammad Syarubany, Chang Dong Yoo
https://arxiv.org/abs/2506.21593

PentaRAG: Large-Scale Intelligent Knowledge Retrieval for Enterprise LLM Applications
Enterprise deployments of large-language model (LLM) demand continuously changing document collections with sub-second latency and predictable GPU cost requirements that classical Retrieval-Augmented Generation (RAG) pipelines only partially satisfy. We present PentaRAG, a five-layer module that routes each query through two instant caches (fixed key-value and semantic), a memory-recall mode that exploits the LLM's own weights, an adaptive session memory, and a conventional retrieval-augmentati…