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A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers deployed APS-RAG, a retrieval-augmented generation system combining dense, sparse, and knowledge-graph search channels with a corrective agentic loop for querying scientific facility operational knowledge.
- Why it matters
- Matters for engineers at large research facilities who need to search decades of accumulated operational data including logbooks, maintenance records, and control-system information through natural language.
- Watch out
- Performance gains from graph channels and corrective loops were marginal; cross-encoder reranking proved critical, and the system was evaluated on only fifty questions specific to one facility.
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Physics > Accelerator Physics
arXiv:2607.24663v1 (physics)
[Submitted on 27 Jul 2026]
Title: A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility
Authors: Rajat Sainju , Dariusz Jarosz , Hairong Shang , Michael Prince , Ryan M. Aydelott , Mathew J. Cherukara , Yine Sun , Michael D. Borland
View a PDF of the paper titled A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility, by Rajat Sainju and 7 other authors
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Abstract: Scientific user facilities accumulate decades of operational knowledge that no single search index covers: electronic logbooks, technical documents, internal wikis, operations chat messages, maintenance records, and live control-system data. We present APS-RAG, Advanced Photon Source Retrieval Augmented Generation, a deployed platform that makes the institutional knowledge at the Advanced Photon Source (APS) accessible to staff through natural-language queries, along with an operations-grounded evaluation. The retrieval engine fuses dense, sparse, and knowledge-graph (KG) channels with query-type-adaptive reciprocal-rank fusion, adds a corrective agentic loop, and runs a native-tool ReAct executor over a Model Context Protocol (MCP) tooling layer. We construct APS-Bench, a 50-question, question-answering (QA) dataset with auditable gold an
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