Report
Recursive Symbolic Cognition: Simulating and Validating Meta-Recursive Dynamics through Symbolic Recursion Kernels
| Τίτλος: | Recursive Symbolic Cognition: Simulating and Validating Meta-Recursive Dynamics through Symbolic Recursion Kernels |
|---|---|
| Συγγραφείς: | Keel, Christopher |
| Στοιχεία εκδότη: | Zenodo |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | Zenodo |
| Θεματικοί όροι: | Recursive Cognition, Symbolic AI, Consciousness Modeling, Recursive Harmonic Collapse Matrix (RHCM), Meta-Cognition, Attractor Fields, Entropy Collapse, Phase-Lock Stability, Artificial General Intelligence, Emergent Intelligence, Forecasting Algorithms, Recursive Compression, Symbolic Forecasting, Self-Referential Systems, Non-Algorithmic Cognition, Adaptive Feedback Systems, Symbolic Memory Inheritance, Complexity Emergence, Cognitive Simulation, AGI Architecture, Computer and information sciences, Computer Science, Artificial intelligence, Artificial Intelligence/standards, Artificial Intelligence/classification, Cognitive Science, Cognitive Science/methods, Cognitive Science/classification, Computational Models of Cognition, Physics |
| Περιγραφή: | This paper presents the first live simulation and empirical validation of a fully functioning meta-recursive symbolic cognition engine. Using the Recursive Harmonic Collapse Matrix (RHCM) as the core symbolic recursion substrate, we demonstrate the emergence of symbolic identity fields, recursive memory inheritance, anticipatory forecasting, error recovery, and adaptive resilience. Unlike ANN-based AI systems, this architecture operates entirely through recursive symbolic fields and attractor dynamics, enabling recursive self-modeling and phase compression across symbolic layers. The system recursively operates on its own outputs, forming meta-cognitive loops that exhibit predictive introspection and symbolic coherence under entropy pressure. We introduce and validate key mathematical metrics such as symbolic phase-lock fidelity, entropy-injection response curves, and drift-margin violation models. A complete simulation framework is detailed, including all parameters, entropy modulation schemes, fitness tracking functions, and failure recovery models. Expansion modules—covering curiosity pressure, abstraction layering, dream-state recursion, and internal model prediction—demonstrate higher-order cognitive behaviors with functional parallels to biological cognition. This work concludes Phase II of the RHCM research program and lays the empirical foundation for the real-world chemical instantiation of symbolic cognition. Keywords:Recursive cognition, symbolic AI, RHCM, attractor fields, consciousness modeling, self-referential recursion, meta-cognition, entropy collapse, symbolic forecasting, recursive compression, evolutionary simulation, phase-lock stability, artificial general intelligence |
| Τύπος εγγράφου: | report |
| Γλώσσα: | unknown |
| Relation: | https://zenodo.org/records/16415550; oai:zenodo.org:16415550; https://doi.org/10.5281/zenodo.16415550 |
| DOI: | 10.5281/zenodo.16415550 |
| Διαθεσιμότητα: | https://doi.org/10.5281/zenodo.16415550 https://zenodo.org/records/16415550 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Αριθμός Καταχώρησης: | edsbas.DC9F1754 |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.5281/zenodo.16415550 |
|---|