arXiv:2607. 21324v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents.
By Paolo Pedinotti, Enrico Santus
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.
By Harish Saragadam, Sudhanshu Sharma, Meghana Pujari
arXiv:2606. 10381v1 Announce Type: cross Abstract: Muon collider research spans accelerator physics, detector instrumentation, and high-energy phenomenology, with relevant evidence scattered across a rapidly expanding and heterogeneous body of scientific literature.
By Ruobing Jiang, Dawei Fu, Cheng Jiang, Tianyi Yang, Zijian Wang, Youpeng Wu, Yong Ban, Yajun Mao, Qiang Li
arXiv:2606. 29706v1 Announce Type: cross Abstract: Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language.
By Heshan Fernando, Quan Xiao, Yan Xin, Tianyi Chen
The BIT.UA team from the University of Aveiro participated in the 14th BioASQ Task B challenge, presenting a refactored modular pipeline for biomedical question answering. They replaced the PyTerrier PISA index with PostgreSQL-based pg_textsearch for BM25 retrieval and adopted Qdrant for dense embedding indexing, while also exploring HyDE-based query expansion and a Context-1 retrieval strategy. For answer generation, they introduced an LLM-as-a-judge framework and an agent quorum mechanism that allows multiple agents with diverse prompts to debate and converge on a consensus answer, achieving competitive MAP ranks of 5 in Phase A batches.
By Andr\'e Ribeiro, R\'uben Garrido, Alexander Christiansen, Richard A. A. Jonker, S\'ergio Matos
arXiv:2607. 20498v1 Announce Type: new Abstract: Large language models (LLMs) augmented with tools are emerging as autonomous agents capable of using Web engine, APIs, and code to solve complex, long-horizon tasks.
By Fanjin Zhang, Zhengyang Wang, Ruixuan Huang, Kefan Zhang, Amy Xin, Yuanchun Wang, Shu Zhao, Evgeny Kharlamov, Jie Tang, Juanzi Li