mlsanityData to 7 Oct 2026

Verification-Gated Agentic Mission-State Governance for Intelligent Industrial Multi-Robot Systems

arXiv 2606.31339 ↗First posted 30 Jun 2026cs.ROcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Agentic artificial intelligence is increasingly used to decompose industrial tasks, propose robot actions, and adapt execution plans in dynamic cyber-physical environments. However, autonomous proposal generation alone does not guarantee that multi-robot industrial systems preserve task dependencies, resource ownership, safety holds, or repair boundaries during long-horizon execution. This paper introduces a verification-gated agentic mission-state governance framework for intelligent industrial multi-robot systems. The framework maintains two synchronized state objects: an evolving task forest for persistent hierarchy, delayed grounding, and repairable substructures; and a governed blackboard for online execution state, robot traces, resource locks, world beliefs, proposals, verification records, and scene-temporary constraints. From each forest--blackboard snapshot, a derived execution coupling topology exposes cross-branch dependencies for proposal verification, parallel-commit eligibility, and bounded repair. Candidate assignments, repairs, deferrals, and constraint updates may be generated by heuristic, optimization, or agentic reasoning modules, but they can update the committed mission state only after deterministic verification and atomic commit. We evaluate the framework in an indoor factory multi-robot scenario, 30-seed remote-construction stress benchmarks, structural ablations, and scalability probes. The results show improved verified and safety-audited mission-state progress with fewer invalid commitments, lock conflicts, duplicate assignments, abandoned nodes, and disruptive repairs under modeled mission predicates. The study positions agentic AI as a proposal-generating layer governed by inspectable mission-state verification rather than as an unchecked execution authority.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 17 of its 37 references that are in the map. First posted on arXiv 30 Jun 2026.

Builds on

17 of 37 references.

References · 17

  1. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  2. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  3. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
  4. 2023VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Modelscited by 733
  5. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
  6. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 372
  7. 2023ChatGPT for Robotics: Design Principles and Model Abilitiescited by 296
  8. 2023SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planningcited by 289
  9. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  10. 2023SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Modelscited by 151
  11. 2024COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Modelscited by 43
  12. 2025Large Language Models for Multi-Robot Systems: A Surveycited by 26
5 more
  1. 2024LiP-LLM: Integrating Linear Programming and dependency graph with Large Language Models for multi-robot task planningcited by 23
  2. 2022Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planningcited by 19
  3. 2024DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Modelscited by 18
  4. 2024Heterogeneous Multi-robot Task Allocation for Long-Endurance Missions in Dynamic Scenarioscited by 3
  5. 2026EmboTeam: Grounding LLM Reasoning into Reactive Behavior Trees via PDDL for Embodied Multi-Robot Collaborationcited by 2

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