Academic demonstration package presents a framework to suppress epistemic collapse in large language model outputs, indicating potential in effective AI communication.
This record contains the academic demonstration package for the FyCore Research Programme, Papers I–III. FyCore proposes a structural, non-behavioral framework for suppressing epistemic collapse in large language model outputs. The package includes the main framework paper, mathematical formulation, empirical validation under a minimal toy environment, companion notes, and reproducibility artefacts. The trilogy introduces three layers — Silicon Lens (observation), Thought Value (measurement), and Φ_Fy (constrained evaluation) — unified by a logarithmic barrier on epistemic closure that makes absolute certainty mathematically inadmissible. This work is explicitly scoped as an academic and demonstrative research package. It does not constitute a certification authority, runtime decision system, production validator, consciousness model, ground-truth oracle, or operational deployment claim. Simulation results are confined to a controlled toy environment and should not be interpreted as evidence of real-world LLM performance under production conditions. Scholarly circulation is open. Commercial operation, product integration, model-governance integration, certification use, or commercial runtime use requires a separate written license agreement with the author. Foundational preceding work: CCFy — A Curvature-Based Perspective on Regulatory Dynamics in Conscious Experience (Zenodo DOI: 10.5281/zenodo.18130127).
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Fatih Yenen (2026) studied this question.
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