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)…

This is great news for all #XML and #pandoc users: Pandoc has now a native XML reader and writer for an XML representation of its internal document model (which was previously mainly serialized as JSON). This means that it is now possible to add support for new input or output formats via X technologies like

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…