CIv7 Unified Framework: Compression-Informed Intelligence over Symbolic and Latent Substrates

Core Proposition CIv7 posits that intelligence—whether manifest in symbolic systems (like ECA evolution) or high-dimensional latent models (like LLMs)—emerges through mechanisms that minimise description length, detect and adapt to structural discontinuities, and operate near the critical boundary between order and chaos. This framework integrates two mirror hypotheses:

CIv7-ECA explores structural break detection using symbolic substrates evolved through cellular automata.
CIv7-LLM generalises the same principles to latent substrates of large language models (LLMs), highlighting breakdowns in internal coherence, compression, and semantic fidelity.

These two modes are coupled through Sutskever’s Joint Compression Hypothesis: when two data sources (X, Y) share structure, the joint compression of X and Y reveals that structure. In CIv7, symbolic and latent systems serve as mutual projections, compressing each other’s irregularities to expose the algorithmic, topological, and causal scaffolds underpinning intelligence.

CIv7-ECA: Symbolic Substrate Hypothesis (Summary)

Structural breaks are detectable as discontinuities in symbolic sequences evolved by ECAs:

Collapse in compressibility (BDM/CTM)
Topological signal loss (torsion, bifurcation)
Motif rewirings and entropy shifts
Prediction breakdown at the edge of chaos

These fault geometries reveal the limits of causal coherence in symbolic evolution. The ECA substrate is not just representational—it expresses regime shifts in algorithmic, logical, and conceptual dynamics.

Key Tools:

ECA evolution from symbolic encodings (e.g., delta-sign)
Algorithmic complexity via BDM
MDL-based motif discovery and divergence tracking
Fault geometry: curvature, bifurcation, torsion, motif disalignment

CIv7-LLM: Latent Substrate Hypothesis (Summary)

Latent representations in LLMs encode meaning via geometric and algebraic structure. Discontinuities—when reasoning or coherence collapses—can be tracked as:

KL-divergence spikes between prediction and data
Collapse of Chain-of-Thought (CoT) consistency
Attribution rewiring in attention graphs (Anthropic-style tracers)
Compression-meaning divergence (Shani et al.)
Failure of joint latent code reuse (joint compression gaps)

These breakdowns correspond to latent fault lines, algorithmic tipping points within the model’s internal representation manifold.

Key Tools:

CoT tracing and collapse detection
Latent curvature and torsion from attention-MLP dynamics
Information Bottleneck (RDT) metrics across tokens/thoughts
Langlands-style algebra-geometry dualities in reasoning
SFT-RL hybrid regime tracking (SASR)

Integrated View: Joint Compression as Shared Discovery Engine

Sutskever’s Principle: If symbolic and latent systems compress each other’s data, their failure modes expose the underlying structure they both encode.

Dimension	CIv7-ECA	CIv7-LLM
Substrate Type	Symbolic (discrete, interpretable)	Latent (continuous, distributed)
Evolution Engine	ECA Rule Set	Transformer Attention + MLP
Regime Shift Signal	Motif bifurcation, topological torsion	KL Divergence, CoT Collapse
Complexity Metric	BDM, MDL, motif entropy	RDT, IB, compression-distortion curve
Fault Geometry	Phase transitions in symbolic canvas	Latent manifold torsion + attribution drift
Semantic Collapse Signal	Motif rewire, attractor loss	Latent inconsistency, attention misalignment
Joint Compression View	X = symbolic evolution, Y = LLM response	X = latent reasoning, Y = symbolic skeleton

Application Domains (Non-Exhaustive)

Structural Break Detection in time series (ECA + BDM + motif torsion)
Thematic Segmentation in text corpora (LLM + RDT + attribution collapse)
Alpha Discovery in financial symbolic languages (ECA motifs + LLM code evaluation)
Scientific Reasoning Models with open symbolic prompts
Latent Fault Tolerance Testing in safety-critical LLM systems

The Twin Loop

Each hypothesis compresses the other:

CIv7-ECA models can symbolically simulate or perturb latent breakdowns in LLM outputs.
CIv7-LLMs can interpret, generalise, or explain symbolic dynamics within the ECA substrate.

Together, they act as a compression-reflection engine—each probing the other’s causal scaffolding to surface discontinuities, infer latent dynamics, and repair faulty reasoning.

This is the CIv7 vision: an intelligence framework grounded not in data scale alone, but in compressive coherence, reflective structure discovery, and fault-aware generalisation.