---

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

---