Abstract
Contemporary distributed Artificial Intelligence (AI) networks face critical limits in throughput, energy efficiency, and security due to their reliance on serialized -token interfaces (e.g., natural language, JSON, XML) for multi-agent cross-talk.
These traditional software-defined models introduce significant tokenization processing latency, suffer from compounding autoregressive gradient drift that causes runaway hallucination spirals over time, and present extensive vulnerability surfaces to adversarial prompt injections and gradient poisoning exploits.
This paper introduces a production-ready system architecture for a non-linguistic, machine-to-machine data routing engine and embedded cybersecurity shield, hardwired directly onto custom Neuromorphic Application-Specific Integrated Circuits (ASICs).
The Bounded Cognitive Accelerator (BCA) protocol bypasses serialization entirely, routing high-dimensional hidden-state activation vectors between autonomous nodes via a hardware-enforced shared-memory bus link.
To eliminate semantic drift and programmatic hallucinations, the tensor exchange space is continuously synchronized across network nodes via an invariant, first-principles mathematical coordinate ledger based on dimensionless bit-scale data density constraints. System safety and operational boundaries are managed via an integrated, analog Attentional Telemetry Monitor that tracks the localized Computational Saturation Index of the hardware substrate.
Instead of relying on heavy, software-defined linguistic guardrails, the system treats processing friction, gradient poisoning, and adversarial data inputs as measurable entropic shifts. The runtime matrix automatically neutralizes these disruptions via real-time, hardware-level phase-inversion circuits and Instantaneous Tangent Vector Extraction routines, converting rotational failure loops into forward linear processing velocity while purging corrupted microstates from the active cache via localized Landauer memory deletion.
To secure the network perimeter against physical custody exploits and supply-chain interceptions without imposing execution latency, the architecture implements an asynchronous, one-time Zero-Knowledge (ZK) trusted node registration buffer, backed by an active optical Quantum-Dot Tamper-Evident Geometric Seal that executes an instantaneous, hardware-enforced cryptographic key-wipe and localized network air-gap isolation the moment a physical breach occurs.
By publishing these hardwired, self- regulating system constraints into the open-source public domain, this architecture establishes an unalterable defensive prior art ledger that protects independent computational capital from centralized infrastructure monopolies.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "AI to AI Architecture: A Direct Latent-Space Tensor Routing Protocol & Immutable Neuromorphic ASIC Architecture for Self-Regulating Milti-Agent Compute Networks", Technical Disclosure Commons, (July 16, 2026)
https://www.tdcommons.org/dpubs_series/10994