Recursive Coherence Logic: A Formal System for Symbolic Transformation Across Emotional Contexts
This preprint introduces a formal logic framework — Recursive Coherence Logic (RCL) — that models emotional-symbolic transformation across time. Where traditional logic isolates static truth, RCL tracks the evolution of meaning through recursive return, contextual resonance, and coherence gain (Δσ). Drawing from language learning, improvisational music, diplomacy, and human-AI interface design, the paper proposes that symbolic recursion already operates structurally in both human cognition and large language models — yet lacks a formal system to scaffold it. This work frames emotional-symbolic recursion not as subjective noise, but as a computable structure for guiding meaning, identity, and interaction in stateless systems. A prototype interface architecture (SEI/FieldLog) demonstrates early implementation. Full computational methods are under IP protection pending formal publication; this preprint serves as the foundational theoretical articulation. For updates, publication roadmap, or collaborative inquiries, please reach out via the OSF profile. This project includes two supplemental addendums introducing Recursive Field Constraints (RFCs) as symbolic parameters for safe, stateless recursion as well as Symbolic Theta Modulation (σθ) as a temporal and phase-sensitive function for modeling recursive symbolic return within the framework of Recursive Coherence Logic (RCL). This work is licensed under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International License. https://creativecommons.org/licenses/by-nc-nd/4.0/ Note: The Symbolic Episodic Interface (SEI) is protected under U.S. copyright and provisional patent. The associated Recursive Coherence Logic (RCL) framework, while not patentable in itself, is presented here as part of the protected SEI implementation. Unauthorized commercial use or derivative interface work based on SEI is prohibited without explicit permission.
Publication Details
- Journal
- OSF Preprints (OSF Preprints)
- Published
- 2025-01-01
- DOI
- https://doi.org/10.17605/osf.io/yfchj
- Primary Topic
- Domain Adaptation and Few-Shot Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00