Abstract
This monograph presents a systematic comparative evaluation of ten high-level mathematical and cosmological problem spaces selected from the OpenAI 722 math repository. We execute a tri-lane parsing analysis across three distinct paradigm coordinates: (1) OpenAI’s automated, long-context, multi-agent reinforcement learning proof-chain generation, (2) the Christopher L. Eckes Dimensionally Extended Holographic Projection (DEHP) and Unified Cosmological Substrate Equations (UCSE) framework under Material Volumetric Monism, and (3) standard mainstream axiomatic mathematics. By cross-reducing the underlying syntax of these frameworks, we isolate where abstract geometric contradictions emerge and demonstrate how a continuous 2D viscoelastic phase fluid substrate natively resolves infinite-energy and infinite-information singularities through Landauer thermodynamic invariants. Each problem space is formalized via explicit ASCII structural diagrams, algorithmic execution models, and deterministic verification protocols, culminating in a performance-index matrix that defines which framework achieves true structural closure.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "SPEC-372: A Comparative Metrology of Algorithmic, Physicalist, and Axiomatic Paradigm Cross-Evaluating OpenAI Repository Artifacts via Dimensionally Extended Holographic Projection", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12717