mlsanityData to 14 Oct 2026

Verify-Gated Completion as Admission Control in a Governed Multi-Agent Runtime: A Bounded Architecture Case Study

arXiv 2605.17998 ↗First posted 18 May 2026cs.SE cs.AIcited by 0

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Abstract (arXiv)

As multi-agent systems move from short interactions to tool-using workflows with specialized roles and persistent state, completion becomes a runtime-control problem rather than a purely generative one. This preprint studies verify-gated completion as an admission-control pattern for governed multi-agent runtimes: agents may propose completion, but a read-only verifier decides whether the claim is admitted. Ambiguous or weakly evidenced cases resolve fail-closed, while packetized state and event traces preserve an audit path. We examine one bounded reference implementation and ask what the released evidence can support about auditable, verify-gated completion. In the released verify-completed slice, the known-outcome invoked-event verify success share was 1,791/1,800 = 99.5%. This is an accounting measure over invoked verification events, not a task-completion, production-reliability, or benchmark-success rate. Task-level verify coverage is not computable; 1,762/1,801 rows came from one high-volume reporting cluster; and only 17 events were production-classified. A shadow Policy/Governance Verifier evaluation showed 1,526/1,548 = 98.58% rule agreement, 0/1,526 false-success among safe-to-proceed predictions, and blocked precision of 2/518 = 0.39%, so it remains advisory. The evidence supports a narrow conclusion: under observed conditions, a read-only verify gate plus packetized admission records made completion decisions inspectable and fail-closed. Claims about deployed operation, safety guarantees, outcome gains, task-level coverage, recovery effectiveness, or external validity remain outside scope.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 18 May 2026.

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References · 14

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  5. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  6. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  7. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  8. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  9. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
  10. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  11. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  12. 2023GAIA: a benchmark for General AI Assistantscited by 1,128
2 more
  1. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 324
  2. 2024Large Language Models are Inconsistent and Biased Evaluatorscited by 153

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