@inproceedings{das-etal-2026-orchid,
title = "{ORCHID}: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making",
author = "Das, Sanjay and
Mahbub, Maria and
Lama, Vanessa and
Starks, Brian and
Polchek, Christopher and
Silvers, Saffell and
Deck, Lauren and
Balaprakash, Prasanna and
Patton, Robert M. and
Ghosal, Tirthankar",
editor = "Yang, Eugene and
Lawrie, Dawn and
MacAvaney, Sean and
Mayfield, James and
Soldaini, Luca and
Yates, Andrew",
booktitle = "Proceedings of the 1st Workshop on Multilingual Report Generation via Retrieval Augmented Generation ({RAG}4{R}eports 2026)",
month = jul,
year = "2026",
address = "San Diego, CA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.rag4reports-1.7/",
pages = "57--64",
ISBN = "979-8-89176-417-0",
abstract = "High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents{---}retrieval, description refiner, classifier, validator, and feedback logger{---}coordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an ``Item to Evidence to Decision'' loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts{---}illustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows."
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<abstract>High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents—retrieval, description refiner, classifier, validator, and feedback logger—coordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an “Item to Evidence to Decision” loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts—illustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows.</abstract>
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%0 Conference Proceedings
%T ORCHID: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making
%A Das, Sanjay
%A Mahbub, Maria
%A Lama, Vanessa
%A Starks, Brian
%A Polchek, Christopher
%A Silvers, Saffell
%A Deck, Lauren
%A Balaprakash, Prasanna
%A Patton, Robert M.
%A Ghosal, Tirthankar
%Y Yang, Eugene
%Y Lawrie, Dawn
%Y MacAvaney, Sean
%Y Mayfield, James
%Y Soldaini, Luca
%Y Yates, Andrew
%S Proceedings of the 1st Workshop on Multilingual Report Generation via Retrieval Augmented Generation (RAG4Reports 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, CA, USA
%@ 979-8-89176-417-0
%F das-etal-2026-orchid
%X High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents—retrieval, description refiner, classifier, validator, and feedback logger—coordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an “Item to Evidence to Decision” loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts—illustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows.
%U https://aclanthology.org/2026.rag4reports-1.7/
%P 57-64
Markdown (Informal)
[ORCHID: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making](https://aclanthology.org/2026.rag4reports-1.7/) (Das et al., RAG4Reports 2026)
ACL
- Sanjay Das, Maria Mahbub, Vanessa Lama, Brian Starks, Christopher Polchek, Saffell Silvers, Lauren Deck, Prasanna Balaprakash, Robert M. Patton, and Tirthankar Ghosal. 2026. ORCHID: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making. In Proceedings of the 1st Workshop on Multilingual Report Generation via Retrieval Augmented Generation (RAG4Reports 2026), pages 57–64, San Diego, CA, USA. Association for Computational Linguistics.