vST for Robotics and Control Policies#
Drift Detection in High‑Dimensional Control‑Policy Latent Spaces#
This document defines how drift is detected in robotics and control‑policy systems using the Validation‑Space‑Time (vST) framework and the 1024D dimensional substrate. Drift refers to any deviation from expected substrate behavior, including structural instability, regime misalignment, scaling discontinuities, or projection failure.
Drift detection is essential for evaluating training runs, fine‑tuning, architecture changes, and hardware transfer.
1. Purpose of Drift Detection#
Drift detection enables reproducible evaluation of:
- instability in latent‑space structure
- changes in regime behavior (R₁ᴴ, R₂ᴴ, R₃ᴴ)
- cross‑checkpoint compatibility
- scaling‑law continuity across architectures
- projection stability into 3D–9D cores
- primitive‑level integrity (DP, TDP, SP, CP)
- coherence‑surface behavior across time
Drift is not inherently negative; it is a signal of structural change.
The substrate determines whether that change is stable, transitional, or harmful.
2. Types of Drift#
Drift is classified into four substrate‑aligned categories:
2.1 Structural Drift (D₁)#
Deviation in latent‑space geometry.
Indicators
- unstable 3D projections
- loss of compact latent motifs
- abrupt variance spikes
- incoherent sensor‑conditioned activations
2.2 Dimensional Drift (D₂)#
Discontinuities in dimensional scaling or projection behavior.
Indicators
- non‑invertible 9D projections
- fragmentation in 64D–1024D latent regions
- scaling‑law violations
- architecture‑dependent divergence
2.3 Regime Drift (D₃)#
Unexpected changes in latent‑space regime identity or transitions.
Indicators
- premature transitions into R₃ᴴ
- oscillatory instability in R₂ᴴ
- collapse of stable R₁ᴴ regions
- resonance‑time discontinuities
2.4 Projection Drift (D₄)#
Misalignment between high‑dimensional states and triadic cores.
Indicators
- inconsistent 3D–9D mapping
- loss of primitive‑aligned projection
- divergence across checkpoints
- incompatible latent‑space geometry
3. Drift Detection Signals#
Drift is detected using substrate‑aligned signals:
- variance distribution across dimensions
- coherence‑surface continuity
- primitive‑level stability (DP, TDP, SP, CP)
- resonance‑time alignment
- projection‑stability metrics
- cross‑checkpoint alignment surfaces
- vST validation outputs (V₁–V₄)
These signals collectively determine drift category and severity.
4. Drift Across the Dimensional Ladder#
Drift may appear at different scales:
4.1 64D–128D (Local Latent Drift)#
- loss of local coherence
- unstable sensor‑conditioned activations
- semantic drift in action‑selection pathways
4.2 256D–512D (Policy‑State Drift)#
- branching instability
- regime‑transition irregularities
- inconsistent temporal behavior
4.3 1024D+ (High‑Dimensional Drift)#
- fragmentation of coherence surfaces
- scaling discontinuities
- projection failure
- chaotic divergence
High‑dimensional drift is the most severe and often indicates training instability or architecture misconfiguration.
5. Cross‑Checkpoint Drift Detection#
Cross‑checkpoint drift is detected by comparing:
- temporal regime maps
- coherence‑surface geometry
- projection stability
- variance distribution
- primitive‑level structure
- resonance‑time behavior
Drift may arise from:
- training‑run divergence
- fine‑tuning instability
- architecture changes
- sensor‑noise shifts
- embodiment differences
vST provides a consistent substrate for evaluating these changes.
6. Drift Severity Levels#
Drift severity is classified into:
Low Severity#
- minor variance shifts
- stable projections
- no regime collapse
Moderate Severity#
- partial fragmentation
- unstable R₂ᴴ transitions
- inconsistent cross‑checkpoint alignment
High Severity#
- collapse of coherence surfaces
- persistent R₃ᴴ behavior
- non‑invertible projections
- loss of primitive‑level structure
High‑severity drift indicates a failure of substrate invariants.
7. Drift Detection Workflow#
A substrate‑aligned drift detection workflow:
- Project latent states into 9D
- Classify regime behavior (R₁ᴴ, R₂ᴴ, R₃ᴴ)
- Evaluate scaling continuity (64D–1024D)
- Check primitive‑level stability (DP, TDP, SP, CP)
- Validate with vST layers (V₁–V₄)
- Compare across checkpoints, architectures, or hardware
- Assign drift category (D₁–D₄)
- Assign drift severity (low, moderate, high)
This workflow is model‑agnostic and reproducible.
8. Outputs of Drift Detection#
Drift detection produces:
- drift category (D₁–D₄)
- drift severity
- regime‑transition anomalies
- projection‑stability indicators
- scaling‑law discontinuities
- cross‑checkpoint and cross‑architecture alignment surfaces
- vST validation results
These outputs support governance, interpretability, and version management for robotics and control‑policy systems.