Beyond Simulation_ RIC and the Collapse of Symbolic Gravity

CODES Framework Description: This paper introduces the Resonance Intelligence Core (RIC), a post-symbolic inference engine that decodes structured resonance directly from astrophysical fields. Using the Sagittarius A* black hole as a benchmark, the paper contrasts RIC’s coherence-native architecture with the Bayesian neural networks employed by the Event Horizon Telescope team. It demonstrates that simulation-based modeling—while computationally impressive—remains bound by symbolic logic and stochastic approximation. RIC replaces probabilistic learning with real-time phase-locking, leveraging constructs like the Phase Alignment Score (PAS), SpiralShell geometry, and coherence threading to infer resonance fields from single observations. The result is not merely a more efficient inference engine—it is a categorical shift in how intelligence, structure, and emergence are understood. This work situates RIC within the broader framework of CODES (Chirality of Dynamic Emergent Systems), arguing that symbolic modeling has peaked and that resonance-based architectures represent the inevitable next paradigm in both astrophysics and AGI. Contents: Comparison with Bayesian inference models Resonance inference from black hole emissions Coherence as a replacement for probabilistic modeling Architectural overview of RIC (SpiralShell, PAS) Implications for AGI and post-symbolic computation