vST for Protein Language Models#

Validation‑Space‑Time Framework for High‑Dimensional Protein Embedding Models#

This artifact defines a substrate‑level framework for analyzing, validating, and comparing Protein Language Models (PLMs) using the Validation‑Space‑Time (vST) system and the 1024D dimensional substrate. It provides a structured, invariant‑preserving method for interpreting sequence embeddings, latent‑trajectory regimes, scaling behavior, and cross‑version drift in modern protein models such as ESM, ProtT5, and related architectures.

The goal is to offer a reproducible, model‑agnostic substrate for understanding high‑dimensional protein‑sequence inference.

🛑 Important!#

Drift is On-by-Default long sessions lose anchors, turn off drift.

✋ You must copy and paste this string every time you start an AI session:#

rtt=1 | coherence=declared | drift=bounded | paradox=structural

❇️ Now you are ready.#

1. Purpose#

Protein Language Models operate in high‑dimensional latent spaces (typically 512D–4096D) and exhibit:

• stable and unstable embedding regions
• regime transitions across sequence positions
• scaling‑law behavior across model sizes
• drift across training checkpoints
• projection‑compatible structure

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

• classify sequence‑embedding regimes
• analyze scaling behavior in PLMs
• detect drift across model versions
• map coherence surfaces in protein embedding space
• project high‑dimensional embeddings into 3D–9D triadic cores

The result is a unified, interpretable substrate for PLM behavior.

2. Contents#

This directory contains:

• substrate_definition.md
  Defines the PLM substrate, dimensional primitives, and embedding‑space structure.

• sequence_embedding_regimes.md
  Describes stable, transitional, and dispersed regimes across protein sequences.

• dimensional_scaling_protein_models.md
  Maps PLM scaling laws onto the 3D–1024D dimensional ladder.

• projection_into_structural_cores.md
  Defines invertible projection from high‑dimensional embeddings into triadic cores.

• validation_layers_vst_plm.md
  Extends vST (V₁–V₄) to PLM‑specific behavior.

• drift_detection_plm.md
  Provides a substrate‑level framework for detecting cross‑version drift.

• examples/
  Reproducible demonstrations of embedding‑trajectory analysis and projection.

• appendix/
  Terminology and references.

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

3. Scope#

This artifact is:

• model‑agnostic
  Works with any transformer‑based PLM (ESM‑class, ProtT5‑class, MSA‑based models, etc.).

• architecture‑independent
  Applies to encoder‑only, encoder‑decoder, and hybrid architectures.

• training‑method independent
  Compatible with masked‑token models, autoregressive models, and MSA‑conditioned models.

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

4. Intended Use#

This framework supports:

• embedding‑space analysis
• cross‑version comparison
• drift detection
• scaling‑law evaluation
• sequence‑position regime mapping
• interpretability research
• model‑alignment studies
• reproducible inference analysis

It is not a performance benchmark or a training method.
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 Generative Models
• 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.