@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://preview.aclanthology.org/ingest-acl-workshops/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."
}