@inproceedings{rayfield-etal-2025-react,
    title = "{R}e{A}ct Meets Industrial {I}o{T}: Language Agents for Data Access",
    author = "Rayfield, James T  and
      Lin, Shuxin  and
      Zhou, Nianjun  and
      Patel, Dhaval C",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.24/",
    pages = "364--382",
    ISBN = "979-8-89176-333-3",
    abstract = "We present a robust framework for deploying domain-specific language agents that can query industrial sensor data using natural language. Grounded in the Reasoning and Acting (ReAct) paradigm, our system introduces three key innovations: (1) integration of the Self-Ask method for compositional, multi-hop reasoning; (2) a multi-agent architecture with Review, Reflect and Distillation components to improve reliability and fault tolerance; and (3) a long-context prompting strategy leveraging curated in-context examples, which we call \textit{Tiny Trajectory Store}, eliminating the need for fine-tuning. We apply our method to Industry 4.0 scenarios, where agents query SCADA systems (e.g., SkySpark) using questions such as, ``How much power did B002 AHU 2-1-1 use on 6/14/16 at the POKMAIN site?'' To enable systematic evaluation, we introduce \textbf{IoTBench}, a benchmark of 400+ tasks across five industrial sites. Our experiments show that ReAct-style agents enhanced with long-context reasoning (ReActXen) significantly outperform standard prompting baselines across multiple LLMs including smaller models. This work repositions NLP agents as practical interfaces for industrial automation, bridging natural language understanding and sensor-driven environments."
}