vST for Large Language Models#

Validation‑Space‑Time Framework for High‑Dimensional LLM Inference#

This artifact defines a substrate‑level framework for analyzing, validating, and comparing Large Language Models (LLMs) using the Validation‑Space‑Time (vST) system and the 1024D dimensional substrate. It provides a structured, invariant‑preserving method for interpreting latent trajectories, regime behavior, scaling patterns, and cross‑version drift in modern LLMs.

The goal is to offer a reproducible, model‑agnostic substrate for understanding high‑dimensional inference systems at scale.

1. Purpose#

LLMs operate in extremely high‑dimensional latent spaces (typically 768D–4096D). These spaces exhibit:

• stable and unstable regions
• regime transitions during inference
• scaling‑law behavior across model sizes
• drift across checkpoints and versions
• projection‑compatible structure

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

• classify latent‑trajectory regimes
• analyze scaling behavior
• detect drift across model versions
• map coherence surfaces in latent space
• project high‑dimensional structure into 3D–9D cores

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

2. Contents#

This directory contains:

• substrate_definition.md
  Defines the LLM substrate, dimensional primitives, and latent‑space structure.

• latent_trajectory_regimes.md
  Describes stable, transitional, and dispersed regimes in LLM inference.

• scaling_behavior_llms.md
  Maps LLM scaling laws onto the 3D–1024D dimensional ladder.

• projection_and_alignment.md
  Defines invertible projection from high‑dimensional latent states into triadic cores.

• validation_layers_vst_llm.md
  Extends vST (V₁–V₄) to LLM‑specific behavior.

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

• examples/
  Reproducible demonstrations of latent‑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 LLM (GPT‑class, LLaMA‑class, Mistral‑class, etc.).

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

• training‑method independent
  Compatible with pretraining, fine‑tuning, RLHF, DPO, and mixture‑of‑experts 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‑space analysis
• cross‑version comparison
• drift detection
• scaling‑law evaluation
• embedding‑space diagnostics
• 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 Protein 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.