CIv7-ECA: Solution Proposal

Symbolic Substrate Diagnostics for Structural Break Detection

1. Objective

To implement an end-to-end system that applies the CIv7-ECA hypothesis to:

  • Detect structural breaks in univariate time series (e.g., stock prices)
  • Interpret them as algorithmic discontinuities, topological bifurcations, or semantic collapses
  • Provide early warnings of instability and generate predictive fault geometries
  • Serve as a causal mirror for parallel interpretability in text-based systems (e.g., financial news)

2. System Architecture

2.1 Input Encoding Pipeline

  • Raw Input: Univariate time series (e.g., daily closing prices)

  • Symbolisation Layer:

    • Apply multiple encodings: Delta Sign Encoding, Permutation Patterns, Quantised Returns
    • Output: symbolic sequences (e.g., ‘U’, ‘D’, ‘F’) with tunable resolution

2.2 Substrate Evolution Engine

  • Cellular Automata Layer:

    • Apply Class IV Elementary Cellular Automata (e.g., Rule 110, Rule 54) over sliding windows
    • Encode symbol streams as 2D evolution diagrams
    • Apply motif tracking across generations

2.3 Multimodal Fault Detection

Evaluate the evolved substrate using:

  • Algorithmic Compression Layer:

    • Compute BDM or CTM complexity over the 2D substrate
    • Track derivative shifts, motif entropy, and compressibility gradients

  • Topological Invariant Layer:

    • Track motif torsion, bifurcations, and attractor collapse
    • Use persistent homology (via topological data analysis) to identify phase transition candidates

  • MDL-Based Divergence Tracker:

    • Implement predictive coding using universal NML codes (Grünwald)
    • Compute divergence between actual vs. encoded sequences to detect statistical instability

  • Motif Fault Geometry Extractor:

    • Apply motif clustering and construct symbolic fault manifolds
    • Annotate symbolic transitions where circuit rewiring or attractor collapse occurs

3. Discontinuity Classification & Explanation Module

3.1 Fault Typology Classifier

  • Map transitions to the discontinuity types outlined in the hypothesis:

    • Compression collapse
    • Topological bifurcation
    • Motif entropy jump
    • Steering analogue failure (symbolic motifs fail to generalise)
    • Edge-of-chaos degeneracy

3.2 Causal Annotation Engine

  • Generate interpretable summaries (e.g., “breakpoint due to collapse in motif class entropy at t=167”)
  • Trace fault geometry paths over time as semi-symbolic narratives

3.3 Cross-Modality Bridge

  • Accept external latent encodings from CIv7-LLM textual systems
  • Identify isomorphic failure surfaces (e.g., text theme drift aligns with price structure break)

4. Prediction & Early Warning

4.1 Causal Attractor Projection

  • Use current ECA motif evolution to project likely attractor zones
  • Estimate risk of transition to new causal regime

4.2 Generative Scenario Simulation

  • Generate plausible post-break symbolic evolutions under varying CA rule sets
  • Identify candidate causes via symbolic ablation

5. Integration and Interfaces

  • Output Dashboards:

    • Breakpoint timelines, motif maps, torsion heatmaps

  • APIs:

    • For passing symbolic encodings to CIv7-LLM systems
    • For retrieving semantic correlates from financial news themes

6. Benefits

  • Model-Agnostic: Works as an interpretability shell around black-box models
  • Symbolic Transparency: Provides traceable fault paths instead of opaque anomaly flags
  • Causal Compression Diagnostics: Not only detects breaks but infers why compressibility failed
  • Cross-Substrate Harmony: Can inform LLMs of breaks, and vice versa