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Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document Search describes DocuSearch, an offline multi‑agent system designed for telecom network operations. The system combines semantic vector search, BM25 full‑text search, and knowledge‑graph neighbor expansion, merges the results via Reciprocal Rank Fusion, and reranks with a cross‑encoder before pruning with Maximal Marginal Relevance. A per‑chunk evaluation loop ensures only grounded answers are returned, achieving Precision@10 of 0.69, Recall@10 of 0.79, and an 89.6% grounding rate—improvements of 15, 16, and 18.4 percentage points over a dense‑only baseline.
The paper introduces Athena for Cloud Knowledge Base, a fully offline multi‑agent Retrieval Augmented Generation (RAG) system tailored for Vodafone Idea’s Telecom SNOC environment. It combines dense E5 Large V2 embeddings, BM25 sparse retrieval, and Knowledge Graph expansion, fusing results with Weighted CombSUM, cross‑encoder reranking, and MMR to produce a diverse evidence set. The framework further verifies each chunk with an LLM before response generation, discarding weak evidence and falling back to multi‑chunk evaluation when necessary, achieving an MRR@10 of 0.910 and an EM of 78.4% on a 4,200‑document corpus.
arXiv:2607. 17719v2 Announce Type: replace Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
Public institutions hold large volumes of sensitive documents and support tickets that cannot leave the premises, ruling out cloud-hosted language models entirely. We report on RAGAL, a retrieval-augmented assistant for the technical-support team of AFIR, the Romanian Agency for Financing Rural Investments, built and operated under three hard constraints: zero data egress (no external API calls, even for synthetic data), a read-only mandate (the assistant drafts, humans execute), and a single 8 GB consumer laptop as the only development and training machine.
arXiv:2606. 17698v1 Announce Type: new Abstract: As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked.
arXiv:2607. 17719v1 Announce Type: new Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).