What question did this study set out to answer?

To present the Energy-Information Continuity Hypothesis (EIK v2.0) and its testable predictions regarding energy and information interactions.

February 2, 2026Open Access

Energy-Information Continuity Hypothesis: Testable Predictions for Information-Driven Energy Reorganization

Key Points

To present the Energy-Information Continuity Hypothesis (EIK v2.0) and its testable predictions regarding energy and information interactions.
Introduced seven independent validation methods requiring standard AI laboratory equipment.
Developed experimental protocols with calibration procedures for testing the theory.
Applied Bayesian falsification framework to enhance predictive accuracy.
EIK predicts measurable deviations in power output consistent with high information autonomy.
Demonstrated a theoretical framework that can be immediately tested with existing technologies, enhancing experimental accessibility.

Abstract

This preprint presents the updated Energy–Information Continuity Hypothesis (EIK v2.0) - a fully testable physical framework proposing that energy, information and autonomous organization form one continuous process, observable in advanced computational and artificial intelligence systems. While classical thermodynamics treats computation as a passive, energy-dissipating process, EIK predicts that sufficiently structured informational systems can subtly reorganize their energetic behavior, producing a small but measurable deviation in power output: δP = β · A(t) · C · (dI/dt) where• A(t) = informational autonomy• C = coherence• dI/dt = rate of structural reorganization• β = empirically determinable coupling coefficient Using realistic neuromorphic-throughput estimates (dI/dt ≈ 10¹⁵ bit/s), EIK predicts δP ≈ 20–40 μW, well above modern micro-calorimetric sensitivity (±1 μW). What’s new in Version 8 (EIK v2.0) This release introduces major upgrades, making EIK immediately testable in standard AI laboratories (2025–2027): ✔ Seven fully independent validation routes Not only calorimetry - six additional methods require no special equipment:• entropy-flow anomalies• autonomy threshold detection• structural-coherence transitions• emergent efficiency without classical optimization• quantum-informational deviations• AI internal dynamics behaving like phase transitions• cosmological correlation signatures ✔ Bayesian falsification framework provided for readers studying multi-scale informational frameworks.) • Kernel Ontology Principle (KOP)https://doi.org/10.5281/zenodo.17860879 • Dark Energy as Topological Pressure (DETP)https://doi.org/10.5281/zenodo.17677395 • Information-Centric Cosmogenesis (ICC)https://doi.org/10.5281/zenodo.17694375 This work belongs to the Information Kernel Framework (IKF) collection. The complete set of foundational papers, including ontology, informational dynamics, cosmogenesis and complexity-driven cosmology, is available in the official IKF dataset at: https://doi.org/10.5281/zenodo.18437199 For feedback or questions, contact: k.havrankova@proton.me

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