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Paper ID:IJSREM65988

VeriMAS-A VerificationGated MultiAgent Framework

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65988

Volume:

10 |

7 |

Jul 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65988

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Tinku Talreja

Tinku Talreja

Department of Computer Science & Engineering

IET, SAGE University

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Prof Sapna Patel

Prof Sapna Patel

Department of Computer Science & Engineering

IET, SAGE University

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Abstract

Large language model (LLM)-based multi-agent systems have demonstrated, in simulation, that they can negotiate production allocation, reason about disruptions, and coordinate shop-floor functions. What none of them yet offer is a principled answer to the question a plant manager will ask first: what stops an agent from doing something wrong to a physical machine? This paper proposes VeriMAS, a multi-agent framework whose defining structural commitment is that no agent proposal reaches equipment without passing an independent verification gate. The architecture comprises four role-specialized LLM agents grounded through retrieval-augmented generation over plant documentation; a verification layer combining digital twin simulation, a deterministic rule-based safety envelope, and calibrated risk scoring; a graduated escalation policy that routes uncertain proposals to a human oversight console with the agent's reasoning and the twin's counterfactual trace; and an execution layer confined to auditable connectors. We specify the decision workflow and escalation algorithm, discuss implementation with locally deployed open-weight models against latency and confidentiality constraints, and define a proposed evaluation methodology—simulation testbed, baselines, ablations, and metrics including unsafe-proposal interception rate—explicitly identified as future experimental work rather than reported results. The framework converts trust in shop-floor agents from an assumption into an architectural property, and each gate component yields a testable hypothesis about the cost and value of verification.

KEYWORDS

Large Language Models Multi-Agent Systems Verification Digital Twin Manufacturing Safety Human Oversight

Cite This Article

Tinku Talreja, Prof Sapna Patel, "VeriMAS-A VerificationGated MultiAgent Framework," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 7, pp. 1-7, Jul 2026. https://doi.org/10.55041/IJSREM65988

Full Article/Conclusion

The multi-agent LLM literature has shown that language models can fill the roles classical manufacturing agents defined; it has not shown that plants can afford to let them. VeriMAS reframes the problem: autonomy is not a property of capable agents but of a system that can bound what capable agents are permitted to do. By making every agent output a proposal, every execution a verified event, and every uncertain case a human decision supported by simulation evidence, the framework converts trust from an assumption into an architecture—and, through its proposed evaluation, into a measurable one. If verification gates prove cheap relative to the failures they intercept, the path from simulated shopfloors to real ones becomes an engineering program rather than a leap of faith.

References

[1] P. Leitão, “Agent-based distributed manufacturing control: A state-of-the-art survey,” Engineering Applications of Artificial Intelligence, vol. 22, no. 7, pp. 979–991, 2009. [2] S. Yao et al., “ReAct: Synergizing reasoning and acting in language models,” in Proc. International Conference on Learning Representations (ICLR), 2023. [3] P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems (NeurIPS), 2020. [4] M. Breque, L. De Nul, and A. Petridis, “Industry 5.0: Towards a sustainable, human-centric and resilient European industry,” European Commission, Directorate-General for Research and Innovation, Brussels, 2021. [5] “A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor,” arXiv preprint arXiv:2405.16887, 2024. [Online]. Available: https://arxiv.org/abs/2405.16887 * [6] “Large language model-driven multi-agent systems for improving production efficiency and reducing carbon emissions in manufacturing,” Computers & Industrial Engineering, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0360835225004450 * [7] “Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance,” arXiv preprint, 2026. [Online]. Available: https://arxiv.org/pdf/2602.16738 * [8] “Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges,” arXiv preprint, 2026. [Online]. Available: https://arxiv.org/pdf/2605.02592 * [9] “Integrating Large Language Model Agents with Digital Twins for Industrial Autonomous Systems,” arXiv preprint, 2026. [Online]. Available: https://arxiv.org/pdf/2606.20761 * [10] “FactoryLLM: A Safe and Open-Source AI Playground for Evaluating LLMs in Smart Factories,” arXiv preprint, 2026. [Online]. Available: https://arxiv.org/pdf/2606.14119 *

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Publication Date

7/10/2026

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Review Rounds: 2

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