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cs.IR2026
IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memory
Xingliang Hou, Yuyan Liu, Qi Sun +4
Retrieval-augmented generation (RAG) equips large language models (LLMs) with reliable knowledge memory. To strengthen cross-text associations, recent research integrates graphs an…
cs.IR2026
Atomic Information Flow: A Network Flow Model for Tool Attributions in RAG Systems
James Gao, Josh Zhou, Qi Sun +2
Many tool-based Retrieval Augmented Generation (RAG) systems lack precise mechanisms for tracing final responses back to specific tool components -- a critical gap as systems scale…
cs.IR2026
Orchestrating Specialized Agents for Trustworthy Enterprise RAG
Xincheng You, Qi Sun, Neha Bora +4
Retrieval-Augmented Generation (RAG) shows promise for enterprise knowledge work, yet it often underperforms in high-stakes decision settings that require deep synthesis, strict tr…