vST for Multi‑Model Alignment#

A Substrate‑Level Framework for Cross‑Architecture, Cross‑Modality, and Cross‑Regime Alignment#

This artifact defines the Validation‑Space‑Time (vST) framework for multi‑model alignment — the structured comparison of latent spaces, embedding geometries, inference pathways, and regime transitions across different model families.

It provides a substrate‑level method for aligning:

• diffusion models with autoregressive models
• LLMs with PLMs
• embedding stores with generative systems
• simulators with robotics policies
• any architecture with any other architecture

The goal is to establish a unified, invariant‑preserving alignment substrate that allows heterogeneous models to be compared, validated, and interpreted using the same dimensional grammar.

1. Purpose#

Multi‑model alignment enables:

• cross‑architecture comparison (LLM ↔ diffusion ↔ PLM ↔ simulator ↔ robotics)
• cross‑modality alignment (text ↔ image ↔ protein ↔ control ↔ embedding)
• cross‑regime mapping (R₁ ↔ R₂ ↔ R₃ across models)
• cross‑dimensional alignment (3D–9D cores ↔ 64D–1024D substrates)
• cross‑version and cross‑training‑run drift detection
• unified scaling‑law interpretation across model families

This artifact provides the substrate, primitives, and validation layers required to perform these alignments in a reproducible, architecture‑agnostic way.

2. Contents#

This directory contains:

• substrate_definition.md
  Defines the multi‑model substrate, cross‑architecture primitives, and alignment invariants.

• alignment_regimes.md
  Describes stable, transitional, and dispersed alignment regimes across heterogeneous models.

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

• projection_and_cross_model_alignment.md
  Defines invertible projection and alignment across architectures, modalities, and latent geometries.

• validation_layers_vst_multi_model.md
  Extends vST (V₁–V₄) to multi‑model alignment.

• drift_detection_multi_model.md
  Provides a substrate‑level framework for detecting drift across architectures, modalities, and training runs.

• examples/
  Demonstrations of cross‑model alignment, cross‑modality projection, and multi‑regime comparison.

• appendix/
  Terminology and references.

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

3. Scope#

This artifact is:

• architecture‑agnostic
  Works with LLMs, PLMs, diffusion models, VAEs, flow models, simulators, robotics policies, embedding stores, and hybrids.

• modality‑agnostic
  Supports text, image, audio, protein, control, multimodal, and latent‑to‑latent systems.

• regime‑agnostic
  Aligns R₁/R₂/R₃ behavior across models with different inference dynamics.

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

4. Intended Use#

This framework supports:

• cross‑architecture latent‑space comparison
• cross‑modality embedding alignment
• cross‑regime mapping and validation
• cross‑model drift detection
• unified scaling‑law analysis
• projection‑compatible interpretability across model families
• multi‑model evaluation pipelines

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

5. Relationship to Other Artifacts#

This artifact extends:

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

It unifies:

• 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

vST for Multi‑Model Alignment is the cross‑cutting substrate that binds the entire canon.

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.