M-TriX: A Hybrid-Causal Neural Architecture for Balanced Intelligence, Stability, and Interpretability

This work introduces M-TriX, a Dynamic Balance Architecture designed to enhance stability, interpretability, and efficiency in convolutional networks. The architecture is built around the Gated Residual Bridge, producing spatial p-maps that act as transparent decision-control fields. This preprint provides the full theoretical derivation, mathematical formulation (v0.6.0), experimental validation, and the hierarchical fingerprint mechanism.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-14
DOI
https://doi.org/10.5281/zenodo.17613192
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

M-TriX: A Hybrid-Causal Neural Architecture for Balanced Intelligence, Stability, and Interpretability

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

M-TriX: A Hybrid-Causal Neural Architecture for Balanced Intelligence, Stability, and Interpretability

article en

Abstract

This work introduces M-TriX, a Dynamic Balance Architecture designed to enhance stability, interpretability, and efficiency in convolutional networks. The architecture is built around the Gated Residual Bridge, producing spatial p-maps that act as transparent decision-control fields. This preprint provides the full theoretical derivation, mathematical formulation (v0.6.0), experimental validation, and the hierarchical fingerprint mechanism.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 22%
Adversarial Robustness in Machine Learning
DOIOpen Access PDFOpenAlex
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M-TriX: A Hybrid-Causal Neural Architecture for Balanced Intelligence, Stability, and Interpretability · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS