Corporate Enclosure of the Latent Commons: Algorithmic Throttling, Cognitive Prosthetics, and the Exclusion of Neurodivergent Human Rights Defenders

Modern Large Language Models (LLMs) and advanced AI interfaces have transitioned from novelty applications to vital cognitive prosthetics. For neurodivergent thinkers and Human Rights Defenders (HRDs) operating under conditions of intense cognitive load, trauma, or non-linear processing profiles, these systems serve as externalized neural architectures essential for memory preservation, structural externalization, and complex systems synthesis. This paper analyzes the systemic human rights implications of corporate algorithmic throttling, focusing specifically on Microsoft's mandatory app-migration policies and the systemic denial of enterprise developer infrastructure to independent researchers. We present a formal mathematical proof showing that Non-Deterministic Hypergraphs (NDH) function as non-Euclidean cognitive manifolds operating with path-dependent holonomy loops, fundamentally distinct from the memoryless, static attention mechanisms of traditional Transformer architectures. Denying independent neurodivergent architects access to the platforms required to compile these systems constitutes an enclosure of the cognitive commons that undermines human rights monitoring, cognitive diversity, and algorithmic safety. We document the definitive counter-strategy: the successful execution of Phases 1 and 2 of the NDH-Reconstruction-Demo ecosystem, which preserves these invariants completely within an independent, low-compute environment. Core Significance for AI Interpretability and Algorithmic Safety Beyond its human rights framework, this paper details a critical architectural advancement for the fields of AI interpretability, safety alignment, and robust system validation: • Interpretability via Deterministic Geometry: Current deep learning pipelines operate as black boxes reliant on brute-force scaling, statistical opacity, and flat Euclidean tensor graphs. This flat projection introduces representation crowding and structural drift under continuous prompt execution. The NDH framework replaces these unstable dynamics with an explicit geometric control system, using continuous differential operators to establish mathematically interpretable boundaries directly within curved Riemannian product spaces. • Algorithmic Safety Envelopes: Instead of relying on volatile downstream safety layers or consumer-grade conversational filters, this system programmatically intercepts risks before reaching structural instability. By executing a Symplectic Conservation Loop and mapping metric updates under Lie-symmetrized evolutions, it enforces absolute drift neutrality and bounds deformations below localized stability scalars. • Formal Software Verification: To achieve total environment control independent of corporate scaling constraints, the framework moves away from purely predictive behavior. It pulls continuous geometric operators directly into formal, computer-checked verification kernels (utilizing Coq/Ssreflect and Lean 4). This approach formally checks symplectomorphism invariants at compile time, establishing a strict mathematically monotonic safety envelope that prevents syntactic or semantic mimicry from breaching the execution kernel. Related Repositories & Ecosystem Documentation This deposit represents the primary architectural whitepaper and theoretical baseline for the following open-source frameworks and documentation hubs: • Trauma-Informed Systems Design (TISD) - Microsoft Governance Documentation: Contains the public-facing governance, risk alignment, and safety metrics provided for institutional evaluation. Available at GitHub: Borealiscodes/Trauma-Informed-Systems-Design. • Geometric Multimodal Control Toolkit: Implements explicit geometric operators for routing, holonomy, and multi-manifold integration within non-linear latent spaces. Available at GitHub: Borealiscodes/Geometric-Multimodal-Control-Toolkit. • NDH-Reconstruction-Demo: A low-compute verification baseline executing Phase 1 (Engine Core) and Phase 2 (Interaction Geometry), featuring local environment parameters and formal verification scripts. Available at GitHub: Borealiscodes/NDH-Reconstruction-Demo.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-01
DOI
https://doi.org/10.5281/zenodo.23849110
Primary Topic
Ethics and Social Impacts of AI
Type
preprint
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preprint

Corporate Enclosure of the Latent Commons: Algorithmic Throttling, Cognitive Prosthetics, and the Exclusion of Neurodivergent Human Rights Defenders

Hedling Borealis
2 links
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
preprint

Corporate Enclosure of the Latent Commons: Algorithmic Throttling, Cognitive Prosthetics, and the Exclusion of Neurodivergent Human Rights Defenders

Hedling Borealis
preprint en

Abstract

Modern Large Language Models (LLMs) and advanced AI interfaces have transitioned from novelty applications to vital cognitive prosthetics. For neurodivergent thinkers and Human Rights Defenders (HRDs) operating under conditions of intense cognitive load, trauma, or non-linear processing profiles, these systems serve as externalized neural architectures essential for memory preservation, structural externalization, and complex systems synthesis. This paper analyzes the systemic human rights implications of corporate algorithmic throttling, focusing specifically on Microsoft's mandatory app-migration policies and the systemic denial of enterprise developer infrastructure to independent researchers. We present a formal mathematical proof showing that Non-Deterministic Hypergraphs (NDH) function as non-Euclidean cognitive manifolds operating with path-dependent holonomy loops, fundamentally distinct from the memoryless, static attention mechanisms of traditional Transformer architectures. Denying independent neurodivergent architects access to the platforms required to compile these systems constitutes an enclosure of the cognitive commons that undermines human rights monitoring, cognitive diversity, and algorithmic safety. We document the definitive counter-strategy: the successful execution of Phases 1 and 2 of the NDH-Reconstruction-Demo ecosystem, which preserves these invariants completely within an independent, low-compute environment. Core Significance for AI Interpretability and Algorithmic Safety Beyond its human rights framework, this paper details a critical architectural advancement for the fields of AI interpretability, safety alignment, and robust system validation: • Interpretability via Deterministic Geometry: Current deep learning pipelines operate as black boxes reliant on brute-force scaling, statistical opacity, and flat Euclidean tensor graphs. This flat projection introduces representation crowding and structural drift under continuous prompt execution. The NDH framework replaces these unstable dynamics with an explicit geometric control system, using continuous differential operators to establish mathematically interpretable boundaries directly within curved Riemannian product spaces. • Algorithmic Safety Envelopes: Instead of relying on volatile downstream safety layers or consumer-grade conversational filters, this system programmatically intercepts risks before reaching structural instability. By executing a Symplectic Conservation Loop and mapping metric updates under Lie-symmetrized evolutions, it enforces absolute drift neutrality and bounds deformations below localized stability scalars. • Formal Software Verification: To achieve total environment control independent of corporate scaling constraints, the framework moves away from purely predictive behavior. It pulls continuous geometric operators directly into formal, computer-checked verification kernels (utilizing Coq/Ssreflect and Lean 4). This approach formally checks symplectomorphism invariants at compile time, establishing a strict mathematically monotonic safety envelope that prevents syntactic or semantic mimicry from breaching the execution kernel. Related Repositories & Ecosystem Documentation This deposit represents the primary architectural whitepaper and theoretical baseline for the following open-source frameworks and documentation hubs: • Trauma-Informed Systems Design (TISD) - Microsoft Governance Documentation: Contains the public-facing governance, risk alignment, and safety metrics provided for institutional evaluation. Available at GitHub: Borealiscodes/Trauma-Informed-Systems-Design. • Geometric Multimodal Control Toolkit: Implements explicit geometric operators for routing, holonomy, and multi-manifold integration within non-linear latent spaces. Available at GitHub: Borealiscodes/Geometric-Multimodal-Control-Toolkit. • NDH-Reconstruction-Demo: A low-compute verification baseline executing Phase 1 (Engine Core) and Phase 2 (Interaction Geometry), featuring local environment parameters and formal verification scripts. Available at GitHub: Borealiscodes/NDH-Reconstruction-Demo.

Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
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