L2-Clean Reinforcement and the Hypercube⁵ Meta-Architecture: Stabilizing Multi-Agent Quantitative Systems without AGI-Level Control

Multi-agent trading systems deployed in non-stationary financial markets exhibit catastrophic behavioral drift due to credit contamination and mutual co-adaptation. We introduce TLBA (Two-Level Bias Adaptation), a structural stabilization mechanism that separates a live policy (L1) from a fixed-environment baseline (L2) and periodically cleanses L1 using L2 as a reference anchor. This structure structurally mitigates non-stationarity and credit assignment problems without relying on model complexity or AGI. Furthermore, we interpret the stabilized behavioral layer as the first hypercube of the THS (Theoretical Harmonic Structure) and propose the CEI Hypercube⁵ Meta-Architecture. This architecture stacks environmental, observational, normative, and hypothetical layers atop the behavioral cube, providing a practical, quasi-AGI framework for high-dimensional financial decision-making. We validate the stability of TLBA through experiments in both moderate and extreme drift scenarios.