The Archive

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Authors: Pavan Guruprasad
Cited by: 0

Abstract

This dissertation introduces a unified biophysical and epistemological framework that redefines humancognition not as a passive computational process, but as an active thermodynamic phase transition.Addressing the fundamental schism between the high-entropy stochasticity of subjective experience("Chaos") and the low-entropy rigidity of objective mathematical truth ("Crystal"), this research postulatesthat Mathematics functions as a pre-existing, zero-entropy informational manifold—termed The Archive.We propose that the cognitive act of "understanding" is mediated by a specific operator, The Monarch’sEye, which functions as a bidirectional transducer capable of mapping noisy physical inputs onto theirisomorphic mathematical duals. Grounded in non-equilibrium thermodynamics and Landauer’s Principle, this model quantifies themetabolic cost of this abstraction. We identify a distinct bio-energetic signature—the "Thermal Flash"—atransient metabolic impulse (ΔT approx. 29 mK) required to bridge the entropic gap between the physicaland symbolic domains. Through rigorous Stochastic Differential Equation (SDE) modeling andLangevin Dynamics simulations, we demonstrate that while this "Flash" represents a prohibitive energycost for trivial cognition, it yields an efficiency surplus of +37% in deep-complexity scenarios (T > 179s).Furthermore, the system resolves the dimensionality paradox of thought through a "SimultaneousRendering" mechanism, where the Monarch’s Eye generates high-dimensional cognitive superpositionsto decode low-dimensional mathematical truths. Ultimately, Kinexis establishes that intellectual mastery isnot a function of processing speed, but of Thermodynamic Optimization—the ability to utilize memory facilitated phase-locking to minimize the metabolic tax of truth.

Fields of Study & Subjects

Philosophy and Theoretical Science Cognitive Science and Education Research Embodied and Extended Cognition

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
17 January 2026
Document type
Preprint