@inproceedings{shyalika-etal-2026-industryasseteqa,
    title = "{I}ndustry{A}sset{EQA}: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance",
    author = "Shyalika, Chathurangi  and
      Patel, Dhaval C  and
      Sheth, Amit",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.49/",
    pages = "716--735",
    ISBN = "979-8-89176-394-4",
    abstract = "Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although large language models (LLMs) enable fluent natural-language interaction, deployed maintenance assistants routinely produce generic explanations that are weakly grounded in telemetry, omit verifiable provenance, and offer no testable support for counterfactual or action-oriented reasoning that undermine trust in safety-critical settings. We present IndustryAssetEQA, a neurosymbolic operational intelligence system that combines episode-centric telemetry representations with a Failure Mode and Effects Analysis Knowledge Graph (FMEA-KG) to enable Embodied Question Answering (EQA) over industrial assets. We evaluate on four datasets covering four industrial asset types, including rotating machinery, turbofan engines, hydraulic systems, and cyber{--}physical production systems. Compared to LLM-only baselines, IndustryAssetEQA improves structural validity by up to +0.51, counterfactual accuracy by up to +0.47, and explanation entailment by +0.64, while reducing severe expert-rated overclaims from 28{\%} to 2{\%} ({~} 93{\%}). Code, datasets, and the FMEA-KG are available at: https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA"
}