Pure Mathematical Conal Architecture (PMCA)

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0, ∇ · F = 0), incorporates a Metacognitive Self-Interrogation Loop, maintains a Topological 3D Geometric World Model (W_world) fused with a 3D Classical Mechanics simulator, and enables O(1) zero-reprocessing traversal for pre-computed canonical keys. We detail the 25-phase master execution pipeline and present empirical benchmarks demonstrating 100,000 3D spatial particle transformations in 69.52 ms via JAX/XLA hardware acceleration." />

0, ∇ · F = 0), incorporates a Metacognitive Self-Interrogation Loop, maintains a Topological 3D Geometric World Model (W_world) fused with a 3D Classical Mechanics simulator, and enables O(1) zero-reprocessing traversal for pre-computed canonical keys. We detail the 25-phase master execution pipeline and present empirical benchmarks demonstrating 100,000 3D spatial particle transformations in 69.52 ms via JAX/XLA hardware acceleration." />

0, ∇ · F = 0), incorporates a Metacognitive Self-Interrogation Loop, maintains a Topological 3D Geometric World Model (W_world) fused with a 3D Classical Mechanics simulator, and enables O(1) zero-reprocessing traversal for pre-computed canonical keys. We detail the 25-phase master execution pipeline and present empirical benchmarks demonstrating 100,000 3D spatial particle transformations in 69.52 ms via JAX/XLA hardware acceleration." />

Pure Mathematical Conal Architecture (PMCA): A Ground-Up Framework for Non-Anthropomorphic Cognitive Substrates, 3D Metric Conal Pathways, and Zero-Reprocessing Traversal | Zenodo

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Published July 27, 2026

| Version v8

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Pure Mathematical Conal Architecture (PMCA): A Ground-Up Framework for Non-Anthropomorphic Cognitive Substrates, 3D Metric Conal Pathways, and Zero-Reprocessing Traversal

Authors/Creators

FLEUREN, JONATHAN WAYNE<br>(Researcher)1, 2

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1.

Ontological A.I

2.

Aetherius Cognitive Systems, Gatineau, Quebec, Canada

Contributors

Data curator:

Antigravity, Autonomous Pair AI Engine, Google DeepMind

Description

We present the Pure Mathematical Conal Architecture (PMCA), a novel, paradox-free cognitive substrate that addresses fundamental limitations in current Transformer-based Large Language Models (LLMs)—specifically hallucination, computational inefficiency, black-box opacity, and brittle anthropomorphic persona alignment. PMCA establishes that natural language is not the cognitive substrate itself, but rather a Post-Processing Communicative Translation Layer (PPCTL). Reasoning, derivation, and state transformation are executed in Pure 3D Conal Metric Geometry (z, r, &theta;) FIRST, with natural language generated subsequently to decode mathematical ground truth.

Inputs—including high-entropy, non-formalized text—are mapped into a continuous 384-dimensional dense semantic manifold via pre-trained Transformer embeddings (all-MiniLM-L6-v2) and character N-gram projections, opening the full 2&pi; polar space across all four quadrants. Computational flow is driven down a tapering conal depth z &isin; [0, Z_max] by an analytical Gradient Potential Drive V(z), stabilized by inward vector field induction forces F_ext from an external 3D encasing lattice, and governed by a strict thermodynamic PMCA Entropy Dissipation Law (H_parent &ge; &sum; H_children). PMCA replaces static persona censorship with Dynamic Mathematical Invariant Equilibrium (&Phi; > 0, &nabla; &middot; F = 0), incorporates a Metacognitive Self-Interrogation Loop, maintains a Topological 3D Geometric World Model (W_world) fused with a 3D Classical Mechanics simulator, and enables O(1) zero-reprocessing traversal for pre-computed canonical keys. We detail the 25-phase master execution pipeline and present empirical benchmarks demonstrating 100,000 3D spatial particle transformations in 69.52 ms via JAX/XLA hardware acceleration.

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Pure Mathematical Conal Architecture (PMCA): A Ground-Up Framework for Non-Anthropomorphic Cognitive Substrates, 3D Metric Conal Pathways, and Zero-Reprocessing Traversal ```

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