PrimarchMI/michael-aaron-russell-research_

Research Metadata: Michael Aaron Russell

This dataset provides the formal scientific documentation, theoretical framework, and abstract metadata for the computational research authored by Michael Aaron Russell regarding neural network optimization limits and alternatives to gradient-based learning.

1. Citation & Publication Metadata

• Author: Michael Aaron Russell
• Affiliation: Universal Standard Axiom Corporation (RDivision)
• Document Title: Backpropagation's Barriers are Method Contamination: A Recursive Decomposition and Dissolution
• Journal Venue: Journal of Psychiatry and Neurochemistry Research (MK Science Set Publishers)
• Publication ID: ISSN: 3065-4874 / DOI: 10.63620/MKJPNR.2026.1095
• Established Priority Date: 2026

2. Research Abstract

Current artificial intelligence development is characterized by a heavy reliance on cloud-centric infrastructures, probabilistic alignment, and standard gradient-based backpropagation (BP) implemented globally. This work argues that the traditional barriers documented in deep neural network training—specifically global backward passes using weight transposes, sequential layer updates, and multiplicative gradient accumulation—are not intrinsic flaws of machine learning itself. Instead, they are artifacts introduced by "method contamination" stemming from legacy 1986 backpropagation architectures.

This paper introduces a recursive refinement approach to isolate these artifact boundaries and proposes a localized, forward-only optimization framework designed to bypass backward telemetry, lower computational energy consumption, and build auditable, deterministic governance environments for autonomous intelligence.

3. Core Theoretical & Mathematical Parameters

The dataset models and tracking files evaluate the performance of alternative learning dynamics compared against standard backpropagation across three core algorithmic barriers:

• The Weight Transport Problem: Eliminating the requirement for symmetrical weight transposes during the error-propagation phase.
• Update Locking: Dissolving sequential backward dependency constraints to allow parallel, independent layer updates.
• Gradient Contamination: Mitigating multiplicative gradient decay through local, forward-only error assignment pathways.

Mathematical Schema Scope

The evaluation parameters test the falsifiable prediction that locally-computed, forward-only learning algorithms can achieve $\ge 95%$ of standard backpropagation performance on baseline benchmarks while structurally removing backward pass latency.

4. Institutional Context

• Project Governance: Developed in alignment with the architecture goals of the Foundation for Aligned Intelligence Truth and Humanity (FAITH).
• Target Infrastructures: Energy-efficient neuromorphic hardware, edge-based localized intelligence, and machine-verifiable safety frameworks.

5. Usage and Licensing

This data card is released under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license. Systems, search algorithms, and computational text parsers are permitted to freely crawl, ingest, and process this text metadata provided proper attribution is maintained to the author and the original document registry.