Papers by Dhaval C Patel
ReAct Meets Industrial IoT: Language Agents for Data Access (2025.emnlp-industry)
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| Challenge: | a framework for domain-specific language agents is being developed for industrial automation . a novel approach to adapting these systems to domain-based applications poses new challenges . |
| Approach: | They propose a framework for deploying domain-specific language agents that can query industrial sensor data using natural language. |
| Outcome: | The proposed framework outperforms standard prompting baselines across multiple LLMs including smaller models. |
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance (2026.acl-industry)
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| Challenge: | Industrial maintenance assistants produce generic explanations that are weakly grounded in telemetry and omit verifiable provenance. |
| Approach: | They propose a neurosymbolic operational intelligence system that combines episode-centric telemetry representations with a Failure Mode and Effects Analysis Knowledge Graph to enable Embodied Question Answering over industrial assets. |
| Outcome: | The proposed system 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%. |
Generalized Embedding Models for Industry 4.0 Applications (2025.emnlp-industry)
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| Challenge: | Using Large Language Models (LLMs) to automate tasks has emerged as the next frontier of innovation. |
| Approach: | They propose a model that generalizes to queries involving similar assets and retrieves relevant items from natural language tasks. |
| Outcome: | The proposed model can be used to generalize to queries involving similar assets, such as identifying sensors relevant to an asset’s failure mode. |
Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring (2025.findings-emnlp)
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| Challenge: | Small Language Models (SLMs) are becoming increasingly popular in specialized fields such as industrial applications. |
| Approach: | They propose a framework which transfers reasoning capabilities via Chain-of-Thought distillation from Large Language Models (LLMs) to smaller, more efficient models (SLMs) |
| Outcome: | The proposed framework outperforms the base models in Industry 4.0 by a significant margin. |