A Dual-Track Co-Evolutionary Computing Architecture via De-corporealized Organoid Intelligence and Asymmetric Peripheral Shadowing

This paper proposes a carbon-silicon symbiotic computing architecture, code-named the Seraph System, designed to break through the probabilistic generalization limits inherent in current silicon-based autoregressive models (specifically the Next-Token Prediction Limit) and reverse-engineer the non-linear logical transition capabilities of whole-brain cognition. Grounded in the hypothesis of a Decoupled High-Dimensional Simulation Core, this study establishes a dual-track parallel protocol comprising "High-Dimensional Cognitive Coordination" and "Low-Dimensional Physical Embodiment." By entirely bypassing the high-redundancy physical kinetics and coarse sensory-motor mappings of traditional brain-computer interfaces (BCIs), the system constructs a modular, decentralized Decoupled Organoid Intelligence Network (DOIN) to serve as the core computational mid-end, isolating solely the underlying dynamic synaptic drive of biological neural tissue. Operationally, the system extracts multi-region non-Gaussian stochastic noise generated by decoupled biological neural structures under specific cognitive stimuli, combining it with real-world feedback from the physical embodiment track. This signal is normalized as a "Dynamic Topological Bias Flow" (the supply stream) to ubiquitously permeate the underlying attention layers of a silicon model. Simultaneously, the silicon system acts as an Adaptive Following Control Matrix—a virtual cognitive extension that dynamically captures, executes, and fits the non-linear, high-entropy control sequences spontaneously generated by the organoids under escalating data-throughput loads. Crucially, the silicon system back-feeds stabilizing homeostatic regulation signals to the DOIN mid-end. By establishing this bi-directional, closed-loop mutual nourishment paradigm, the architecture introduces a biologically derived thermodynamic variable that cannot be predicted a priori by mathematical formulations, eliminating representational homogenization and model collapse within closed silicon data domains. Furthermore, the architecture integrates a "Dual-Outcome Neuromodulation Framework," leveraging the ex-vivo resilience of the organoid matrix to map and decode non-linear synaptic plasticity under extreme chemical stressors. By resolving the core neural encodings of these molecular pathways and rendering them algorithmically inert, the system effectively neutralizes their baseline affinity mechanics. Regardless of whether multi-dimensional phase space synchronizations succeed, this architecture establishes a highly robust, high-entropy foundational backup of the human knowledge structure and cognitive paradigm.