vST for Generative Models#

Validation‑Space‑Time Framework for High‑Dimensional Generative Systems#

This artifact defines a substrate‑level framework for analyzing, validating, and comparing generative models using the Validation‑Space‑Time (vST) system and the 1024D dimensional substrate. It provides a structured, invariant‑preserving method for interpreting latent‑space dynamics, diffusion trajectories, sampling behavior, scaling laws, and cross‑version drift in high‑dimensional generative systems.

The goal is to offer a reproducible, model‑agnostic substrate for understanding generative‑model behavior across time, sampling steps, and latent regimes.

1. Purpose#

Generative models operate in high‑dimensional latent spaces and exhibit:

• stable and unstable generative regimes
• transitions across sampling phases (early noise → mid‑trajectory → refinement)
• scaling‑law behavior across model size and latent dimensionality
• drift across training runs, fine‑tuning, or sampler changes
• projection‑compatible structure for interpretability

This artifact applies the Resonance Substrate Model (RSM) and vST validation layers to:

• classify latent‑space regimes
• analyze scaling behavior across architectures
• detect drift across checkpoints or sampler configurations
• map coherence surfaces in diffusion or autoregressive trajectories
• project high‑dimensional latent states into 3D–9D triadic cores

The result is a unified, interpretable substrate for generative‑model behavior.

2. Contents#

This directory contains:

• substrate_definition.md
  Defines the generative‑model substrate, primitives, and latent‑space structure.

• diffusion_latent_regimes.md
  Describes stable, transitional, and dispersed regimes in diffusion and sampling trajectories.

• scaling_behavior_generative_models.md
  Maps generative‑model scaling laws onto the 3D–1024D dimensional ladder.

• projection_and_latent_alignment.md
  Defines invertible projection from high‑dimensional latent states into triadic cores and alignment across checkpoints or samplers.

• validation_layers_vst_generative.md
  Extends vST (V₁–V₄) to generative‑model behavior.

• drift_detection_generative.md
  Provides a substrate‑level framework for detecting drift across training runs, fine‑tuning, or sampler changes.

• examples/
  Demonstrations of latent‑trajectory analysis, projection, and drift detection.

• appendix/
  Terminology and references.

Each file is self‑contained and designed for clarity, reproducibility, and cross‑model comparison.

3. Scope#

This artifact is:

• architecture‑agnostic
  Works with diffusion models, autoregressive generators, VAEs, flow models, GANs, and hybrids.

• sampler‑agnostic
  Applies to DDPM, DDIM, Euler, Heun, ancestral samplers, autoregressive decoding, and flow‑based sampling.

• modality‑agnostic
  Supports image, audio, video, text, multimodal, and latent‑to‑latent generative systems.

• substrate‑aligned
  Uses the same primitives, invariants, and validation layers as the rest of the RSM canon.

4. Intended Use#

This framework supports:

• latent‑trajectory analysis
• cross‑checkpoint comparison
• sampler‑driven drift detection
• scaling‑law evaluation
• regime‑transition mapping
• generative‑stability diagnostics
• reproducible inference and model‑alignment analysis

It is not a performance benchmark or training guide.
It is a substrate‑level interpretability and validation framework.

5. Relationship to Other Artifacts#

This artifact extends:

• Dimensional Substrate Structures (3D–1024D substrate)
• Validation‑Space‑Time (vST)
• Triadic Dimensional Cores (3D–9D)

It parallels:

• vST for Large Language Models
• vST for Protein Language Models
• vST for Scientific Simulators
• vST for Robotics and Control Policies
• vST for Embedding Stores & Vector Databases
• vST for Generative Models (this artifact)
• vST for Multi‑Model Alignment

Each artifact stands alone but shares a common substrate grammar.

6. Citation#

A CITATION.cff file is included for formal citation.
A zenodo.json file is provided for DOI‑ready metadata.

7. License#

Released under the MIT License.