A Locality-First Architecture for Bounded-Coherence Scaling in Continuous Streaming Multi-Agent Systems

This paper introduces the Coherence–Reconciliation Criterion, a locality-based condition for intrinsic alignment and scalable cognition in continuous multi-agent systems. It demonstrates that prompt injection, hallucination, and value drift are architectural consequences of reconciliation cost scaling rather than failures of behavioral training.A concrete Helical Cut-Aware Local-First (CALF) architecture is presented in which reconciliation is a primitive operation, memory access is coherence-gated, and cross-compartment communication is strictly bounded. Empirical evaluation on 1.2 million SWaT/SCADA-style multivariate streaming sensor vectors shows constant coordination latency, bounded reconciliation traffic, and 94% anomaly isolation, while a global all-to-all baseline exhibits linear scaling failure.The work reframes AI alignment and large-scale cognitive system design as a coordination-energy problem and provides a hardware-realizable path for continuous-duty multi-agent systems across FPGA dataflow, neuromorphic, and edge-streaming platforms.This release represents the first public disclosure of the CALF locality-first architecture and Mitchell’s Coordination Latency Bound as an operational scalability test for distributed cognition.