# IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance

arXiv: https://arxiv.org/abs/2604.23446 · HTML: https://mlsanity.com/paper/2604.23446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Language Models Still Struggle to Zero-shot Reason about Time Series](https://mlsanity.com/paper/2404.11757/) (2024)
- [Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement](https://mlsanity.com/paper/2503.01875/) (2025)
- [Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection](https://mlsanity.com/paper/2104.09061/) (2021)
- [MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering](https://mlsanity.com/paper/2503.16858/) (2025)
- [ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset](https://mlsanity.com/paper/2506.20093/) (2025)
- [Knowledge-based Embodied Question Answering](https://mlsanity.com/paper/2109.07872/) (2021)
- [UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization](https://mlsanity.com/paper/2407.03525/) (2024)
- [AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance](https://mlsanity.com/paper/2506.03828/) (2025)
- [IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios](https://mlsanity.com/paper/2505.20640/) (2025)
- [Embodied Intelligence for 3D Understanding: A Survey on 3D Scene Question Answering](https://mlsanity.com/paper/2502.00342/) (2025)
- [IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation](https://mlsanity.com/paper/2511.17384/) (2025)
- [Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring](https://mlsanity.com/paper/2510.18817/) (2025)
- [Chain-of-thought Reviewing and Correction for Time Series Question Answering](https://mlsanity.com/paper/2512.22627/) (2025)

## Abstract (arXiv)

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 episodic telemetry representations with a Failure Mode 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% (approximately 93% reduction). Code, datasets, and the FMEA-KG are available at https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA.