vST for Large Language Models#

Scaling Behavior of LLMs in the 3D–1024D Substrate#

This document defines how Large Language Models (LLMs) exhibit scaling behavior across the dimensional ladder (3D → 1024D). It maps model size, latent‑space expansion, and inference complexity onto the substrate’s triadic structure and scaling primitives. The goal is to provide a reproducible, invariant‑preserving framework for understanding how LLMs grow, stabilize, and drift as their dimensional capacity increases.

1. Purpose of Scaling Behavior Analysis#

Scaling behavior analysis enables us to:

• interpret how latent‑space structure expands with model size
• identify stable and unstable scaling regimes
• detect discontinuities or drift across checkpoints
• map high‑dimensional behavior into triadic cores
• support vST validation across the dimensional ladder
• compare models of different sizes using a common substrate

LLM scaling is not merely an increase in parameter count; it is a structured expansion of coherence surfaces, regime behavior, and primitive composition.

2. Dimensional Ladder for LLMs#

LLM latent spaces naturally align with the substrate’s dimensional ladder:

• 3D — geometric motifs
• 6D — interaction surfaces
• 9D — coherence pathways
• 64D — research‑grade latent embeddings
• 128D — expanded coherence surfaces
• 256D — multi‑primitive interaction
• 512D — high‑variance latent regions
• 1024D — full research‑grade substrate

Each step preserves substrate invariants and introduces new structural capacity.

3. Scaling Primitives in LLMs#

Scaling behavior is governed by Scaling Primitives (SPs), which ensure:

• invariant‑preserving dimensional expansion
• continuity of coherence surfaces
• stable projection into 3D–9D cores
• consistent regime behavior across model sizes

SPs model how LLMs grow from small to large architectures.

4. Scaling Regimes in LLMs#

LLM scaling exhibits three substrate‑aligned regimes:

4.1 Stable Scaling Regime (S₁)#

Characteristics:

• smooth increase in latent‑space capacity
• stable coherence surfaces
• predictable performance gains
• consistent regime behavior (R₁ᴴ → R₂ᴴ transitions remain bounded)

Occurs in:

• small → medium models
• early scaling phases

4.2 Transitional Scaling Regime (S₂)#

Characteristics:

• rapid expansion of coherence surfaces
• increased variance across dimensions
• branching or oscillatory latent behavior
• sensitivity to training data and hyperparameters

Occurs in:

• medium → large models
• architecture changes
• training‑method transitions (e.g., RLHF, DPO)

4.3 Dispersion Scaling Regime (S₃)#

Characteristics:

• fragmentation of coherence surfaces
• unstable or divergent latent trajectories
• increased risk of drift
• non‑invertible projections into 3D–9D cores

Occurs in:

• extremely large models without sufficient training signal
• poorly aligned fine‑tuning
• over‑scaled architectures

5. Scaling Behavior Across Model Sizes#

5.1 Small Models (≤1B parameters)#

• latent spaces map cleanly into 64D
• regime behavior dominated by R₁ᴴ
• scaling is stable (S₁)

5.2 Medium Models (1B–30B)#

• latent spaces expand into 128D–256D
• regime transitions become more frequent
• scaling enters S₂

5.3 Large Models (30B–200B)#

• latent spaces occupy 256D–512D
• coherence surfaces become multi‑layered
• scaling may oscillate between S₂ and S₃

5.4 Very Large Models (200B+)#

• latent spaces approach 1024D
• regime behavior becomes highly sensitive
• scaling stability depends on training quality
• drift detection becomes essential

6. Scaling‑Law Alignment#

LLM scaling follows predictable patterns:

• loss decreases as a power‑law with model size
• latent‑space variance increases with dimensionality
• coherence surfaces expand smoothly in S₁, sharply in S₂, and fragment in S₃
• projection stability decreases as dimensionality increases

The substrate provides a structured way to interpret these patterns.

7. Projection Behavior Under Scaling#

Projection into triadic cores must remain:

• invertible
• primitive‑aligned
• regime‑aware
• invariant‑preserving

Scaling affects projection as follows:

• 64D → 9D: stable
• 128D–256D → 9D: transitional
• 512D–1024D → 9D: sensitive, drift‑prone

Projection stability is a key indicator of scaling health.

8. Scaling‑Driven Drift#

Scaling can introduce drift through:

• discontinuities in latent‑space expansion
• unstable regime transitions
• fragmentation of coherence surfaces
• loss of primitive‑level structure

vST validation layers (V₁–V₄) detect these failures.

9. Outputs of Scaling Behavior Analysis#

Scaling analysis produces:

• scaling‑regime classification (S₁, S₂, S₃)
• latent‑space expansion diagnostics
• projection‑stability indicators
• regime‑transition maps
• drift‑detection signals
• cross‑model comparison metrics

These outputs support reproducible, substrate‑aligned evaluation of LLM scaling.