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March 26, 20260 citationsOpen Access

APR-Lite Phases 7.1–7.3 Technical Note: Coalition Learning, Mesh Topology, and Governance Hardening in Regulated Multi-Agent AI Systems

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NTNarnaiezzsshaa Truong

Key Points

  • The aim is to enhance governance in multi-agent systems through phases of Coalition Learning and Mesh Topology.
  • Description of Phase 7.1 implementing Coalition Learning for behavior changes under a two-person rule.
  • Introduction of Phase 7.2 for Mesh Topology and Learning Governance with graph routing and cycle detection.
  • Implementation of Phase 7.3 including governance hardening and validation against a smoke test suite.
  • Completion of the 7.x governance line enhancing multi-agent coordination.
  • Successful implementation of structured changes under governance rules.
  • Validation against a comprehensive 28-test suite confirming reliability.

Abstract

This technical note describes Phases 7.1, 7.2, and 7.3 of the APR-Lite governance platform, completing the 7.x governance line. Phase 7.1 introduces Coalition Learning — a governed mechanism for proposing, reviewing, activating, and rolling back changes to coalition behavior under strict two-person rule and evidence requirements. Phase 7.2 introduces Mesh Topology and Learning Governance — directed acyclic graph routing with cycle detection, fan-out and fan-in ceilings, and change-set schema validation at proposal time. Phase 7.3 is a governance hardening pass that introduces unified governance event envelopes, deterministic evaluator ordering, total freeze semantics, tenant config integrity validation, and mesh safety case evaluation. Together these three phases complete a fully governed, multi-tenant, multi-agent substrate with coalition drift, cross-session drift, mesh routing, rollback governance, learning ceilings, two-person rule, tenant ceilings, freeze semantics, and unified evaluator ordering. The implementation is validated against a 28-test smoke suite.

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Narnaiezzsshaa Truong (2026) studied this question.

synapsesocial.com/papers/69c4cd65fdc3bde448919ab2https://doi.org/10.5281/zenodo.19209397
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