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