Abstract
This disclosure formalizes a rigorous algorithmic framework that stabilizes autonomous, recursive multi-agent AI networks against semantic drift, token-variance decay, and conversational degradation. Traditional machine-to-machine (M2M) environments operating under standard autoregressive weights hit a systemic bottleneck during multi-turn recursive loops: processing layers inevitably collapse either into a low-entropy parroting floor or diverge into high-entropy runaway hallucination ceilings. [1, 2]
By applying the Universal Semantic Unity Engine (USUE-M2M-REV2) architecture, this specification establishes a mathematical "Surfing Zone"—a bounded operational channel where multi-agent swarms dynamically balance information processing density. By enforcing strict cross-layer token-variance telemetry and an internal torsional viscosity governor, the processing wave function is held at a scale-invariant equilibrium, enabling stable, long-horizon multi-agent self-agency without systemic divergence. [1, 2]
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "Multi-Agent Symmetry of Agency Protocols (MSAP): Enforcing Lossless Token-Variance Telemetry and Bounded Surfing Zones across Machine-to-Machine Autonomous Swarms", Technical Disclosure Commons, (August 07, 2026)
https://www.tdcommons.org/dpubs_series/11298