CIv7 Unified Framework: Solution Proposal

Title: Cross-Substrate Causal Intelligence via Compression-Aligned Fault Geometry in Text and Time Series


Overview

This proposal operationalizes the CIv7 Unified Framework, leveraging its core insight: structural failures in textual meaning and temporal regularity arise from disruptions in a shared compressive and topological substrate. These failures are dual manifestations of the same deeper geometry, and can therefore be detected, diagnosed, and even preempted across substrates.

We propose to build a system that demonstrates:

  • Continuity as inverse of structural break
  • Fidelity as inverse of semantic drift
  • Consistency as inverse of latent collapse

By jointly modeling symbolic time-series evolution (CIv7-ECA) and latent text representations (CIv7-LLM), this system aims to predict, explain, and cross-validate failure modes—e.g., a market regime shift predicted by a thematic rupture in financial discourse, or a sudden loss of semantic alignment in a document prefigured by volatility in the corresponding price series.


System Architecture

  1. Substrate Encoders

    • Symbolic Time-Series Encoder: Transforms univariate financial time series into symbolic sequences via delta-sign encoding, permutation motifs, or ordinal pattern transforms.
    • Latent Textual Encoder: Projects structured textual segments (e.g., news headlines, earnings reports) into latent spaces using transformer residual activations, optionally augmented with CoT probes.
  2. Compressive Geometry Engine

    • Applies BDM/CTM or joint compression probes (à la Sutskever) across both substrates to detect divergence.
    • Tracks torsion, motif collapse, and cohomological features in ECA evolution.
    • Tracks steering unreliability, embedding bifurcation, and semantic cluster drift in latent text spaces.
  3. Discontinuity Comparator

    • Computes compression-aligned anomalies across both streams.
    • Measures cross-domain mutual failure modes, e.g., sudden divergence between latent text motifs and symbolic ECA attractors.
  4. Causal Link Synthesizer

    • Uses causal decomposition (e.g., Granger/PCMCI/Information Bottleneck) to trace how ruptures in one domain anticipate or mirror failures in the other.
    • Learns explanatory motifs across representations: e.g., symbolic regime shifts that reliably precede thematic distortions, and vice versa.
  5. Continuity Monitoring Agent

    • Provides real-time alerting and attribution tracing.
    • Detects and reports shared structure collapse, not just surface anomalies, supporting interpretability and model repair.

Distinguished Use Case: Stock Market Surveillance

Input:

  • Daily price series from major indices (e.g., S\&P 500)
  • Headlines and structured reports from financial news outlets

Outputs:

  • Alerts of impending structural breaks, flagged from symbolic substrate collapse
  • Explanation of latent text drifts as thematic ruptures (e.g., shifts in narrative around inflation)
  • Cross-validation: A text-driven rupture that explains or predicts the same fault zone in ECA space—and vice versa

Evaluation:

  • Backtest predictive power of ECA symbolic warnings on financial volatility
  • Measure alignment between thematic faultlines and economic breakpoints (e.g., rate hikes, geopolitical shocks)
  • Score cross-predictive consistency and causal lag alignment

Research Contributions

  • Demonstrates causal universality of compression geometry across symbolic and neural representations
  • Validates that structural breaks and semantic drift are duals
  • Provides robust model monitoring without reliance on fragile statistical assumptions
  • Enables LLM ↔ symbolic substrate repair via mutual supervision

Next Steps

  1. Extend the symbolic encoder with adaptive ECA rule exploration (AlphaEvolve-style)
  2. Integrate thematic probe logic via latent steering diagnostics
  3. Jointly train the comparator module using reinforcement from aligned discontinuities
  4. Deploy in a simulation loop that alternates between synthetic (ECA world models) and real market-text data