The Universal Rosetta Stone: The Neuromorphic Resolution

This paper identifies and formalizes a fundamental physical bottleneck in scaling AI models for interactive tasks: the Bandwidth Wall. We demonstrate that scaling fails not due to a deficit in parameters or training data, but when a system's System Latency (ₛ) exceeds the Environmental Coherence Time (Tc) —the critical temporal window for a causally relevant response. By deriving the first-principles constraint of Ideal Causal Depth (D^*) from hardware interconnect limits, we provide a mathematical boundary for architectural viability. Empirical validation using Llama 3. 1 8B on NVIDIA H100 hardware demonstrates a measured system latency of 12. 4 ms, safely within the 100 ms conversational coherence threshold. However, our model predicts that monolithic scaling of deeper dense architectures leads to Causal Decoupling, where computation becomes irrelevant to the evolving environmental state. To resolve this, we propose a Hierarchical Caching Strategy—a three-tier asynchronous architecture leveraging geometric power decay in access latency—to enable unbounded narrative depth without violating the Tc constraint. Utilizing the Neutral Relata + Asymmetric Causation (NR+AC) framework, this work shifts the AI scaling discourse from a 3D spatial paradigm to a 4D spatiotemporal model, providing the technical substrate necessary for navigating the 2027 Border Zone. **Versioning Note: ** This Zenodo record represents the first public release of this manuscript. The internal version number (2. 0) reflects significant pre-release revisions and development within the NR+AC research program. All subsequent updates will be tracked as new versions on this Zenodo record.