AI.LCD.ME-CFS GENESIS Framework V10.1: A Governed Multi-Timescale System Architecture for Modeling Chronic Disease Dynamics

The AI.LCD.ME-CFS GENESIS Framework (V10.0) specifies the system architecture of a deterministic, mechanistic, and topographic multi-timescale model for the analysis of chronic disease dynamics, with a particular focus on ME/CFS and related post-infectious syndromes. The framework represents a controlled successor to the AI.LC-Analyzer V6.9x series and is explicitly grounded in the epistemic closure established in Phase 3 of the preceding project. GENESIS does not aim to optimize outcomes, generate prognostic predictions, or provide clinical decision support. Instead, it formalizes a methodologically disciplined extension of a chronification-focused system model, preserving core identity constraints while introducing carefully bounded representational capacities. Immutable identity anchors include the asymmetric stress accumulation–recovery ratio (8:1), a strict hierarchy of functional versus structural timescales, projection decoupling (e.g., FSC, EEI as read-only observables), and an explicit anti-teleological design principle. Within these constraints, the framework introduces (i) conditionally activated structural recovery mechanisms governed by explicit Structural Safety Conditions (SSC), (ii) a Pharmacology Compiler translating real-world substances into model-internal PK/PD-based parameter modulations without therapeutic claims, (iii) a formally externalized Behavioral Coupling Layer whose assumptions are marked as epistemically non-derivable, and (iv) a constrained dual-agent reinforcement learning layer limited to navigation within the modeled landscape rather than outcome optimization. A central contribution of GENESIS is the explicit separation between structural dynamics and observational, behavioral, or navigational overlays. Learning is permitted only where it cannot modify core system identity, while all potential sources of teleology, implicit optimization, or quasi-prognostic interpretation are explicitly constrained, monitored, or excluded. Positive feedback mechanisms are slow, fragile, state-dependent, and immediately deactivated upon violation of safety conditions, ensuring that recovery-like dynamics do not imply goal-directed healing trajectories. The GENESIS Framework is intended as an epistemically governed modeling blueprint rather than an application-ready tool. It provides a transferable reference architecture for constructing complex system models under high uncertainty, emphasizing interpretability, boundary integrity, and governance in Human-in-the-Loop and multi-AI “tiny team” research settings. Any clinical interpretation or use requires independent external validation beyond the scope of this work.