Directional Early Warning: Detecting Structural Drift Before Critical Slowing Down

Boundary note (added 2026-07-23): the Ω-centered empirical layer was self-retracted on 2026-05-31. Ω-centered validation is not currently supported as the active empirical basis; this record remains a historical, fixed, Ω-centered record and must not be cited as current validation. See the VOT Empirical Layer Boundary Note — https://doi.org/10.5281/zenodo.20474232. A later four-dataset head-to-head found that order gated by critical slowing does not beat free-energy signals (10.5281/zenodo.21422215, negative result). This paper proposes the concept of directional early warning for detecting systemic instability before conventional early warning signals become observable. Research on early warning signals in complex systems has primarily focused on phenomena such as Critical Slowing Down (CSD), where recovery from perturbations becomes progressively slower as systems approach tipping points. However, such signals typically emerge only relatively close to collapse. Building on the concept of Structural Drift and the distinction between state variables and directional variables, this paper argues that collapse dynamics can be understood as a three-stage sequence: Structural Drift → Critical Slowing Down → Collapse. During Structural Drift, directional alignment within a system gradually deteriorates while observable state indicators remain stable. Directional variables therefore provide a potential framework for earlier detection of systemic fragility. The paper outlines a conceptual framework for directional early warning and discusses how directional deterioration may be detected through proxy measurements even when alignment variables cannot be measured directly. This work forms part of a conceptual trilogy introducing a directional dynamics framework for understanding system collapse: 1. Structural Drift: Directional Misalignment as a Precursor to System Collapse 2. State Variables vs Direction Variables: Toward a Directional Dynamics Framework 3. Directional Early Warning: Detecting Structural Drift Before Critical Slowing Down