Internal Technical Whitepaper

Title: From Theory to Submission: Implementing CIv8r Structural Break Detection on ADIA Challenge Data (Colab Edition)

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1. Purpose & Implementation Goals

This internal whitepaper translates the theoretical constructs of the CIv8r hypothesis suite—CIv8-ECA, CIv8r-LLM, and CIv8-unified—into a concrete, reproducible, and high-performance implementation for the ADIA Lab Structural Break Challenge.

Goals:

• Achieve a top-tier ROC AUC score for structural break detection.
• Implement symbolic-latent detection pipeline using open-source repositories.
• Leverage our Colab-based distributed task orchestration layer.
• Ensure reproducibility and checkpointed training within Colab runtime constraints.

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2. Architecture Overview

A. Symbolic Pipeline (CIv8-ECA Layer)

• Detects motif transitions, compressibility shifts across boundary.
• Uses ECA rules (e.g. 30, 110) and symbolic grammar features.

• Repos:

  • eca-rule-transform
  • glenford-symbolic-ts

B. Latent Embedding Pipeline (CIv8r-LLM Layer)

• Learns latent topologies and detects geometric discontinuities.
• Applies attention-based encoder and UMAP-style topology modeling.

• Repos:

  • timeseries-transformer
  • topo-ml

C. Unified Scoring Layer (CIv8-unified)

• Fuses symbolic and latent signals into a final structural break score.
• Implements scoring fusion logic using time-windowed alignment.

• Repos:

  • tsflex

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3. Repository Integration Map

Each module integrates key GitHub repositories:

Module	Repository	Source	Used For
symbolic_features.py	eca-rule-transform, glenford-symbolic-ts	Braun et al.	ECA motif extraction
latent_encoder.py	timeseries-transformer	Shani et al.	Transformer-based latent drift
topo_analysis.py	topo-ml (UMAP)	Walch et al.	Latent curvature divergence
fusion_score.py	tsflex	Predict IDLab	Feature fusion and ROC optimization
orchestration_server.py	Custom	Algoplexity	Task scheduler & checkpointing

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4. Module-by-Module Implementation

4.1 symbolic_features.py

• Accepts per-series dataframe (with period column).
• Applies ECA rules (30, 110, randomized) to symbolic discretization.
• Outputs: motif frequency delta, entropy difference, compression delta.

4.2 latent_encoder.py

• Applies pretrained transformer to before/after segments.
• Measures latent space shift via cosine distance and attention flow.

4.3 topo_analysis.py

• Projects residual stream or hidden state via UMAP.
• Computes pre/post boundary manifold curvature.

4.4 fusion_score.py

• Aligns symbolic and latent feature vectors.
• Trains lightweight model (logistic/XGBoost) or uses calibrated weighting.
• Outputs a probability score: 0 (no break) – 1 (break).

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5. Train/Infer Adaptation

train()

• Generates lookup tables or classifier weights.
• Optionally saves pre-computed symbolic/latent feature sets.

infer()

• Receives time series (via ADIA test format).
• Runs symbolic + latent extraction.
• Scores using fusion model.
• Outputs prediction using ADIA required interface.

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6. Evaluation Strategy

• Local testing using crunch.test()
• ROC AUC maximization through score fusion tuning
• False positive control via symbolic confidence thresholds
• Checkpoint and resume logic embedded in worker process

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7. Task Sequence for Implementation

Phase	Depends On	Description
Baseline Forking & Setup	None	Reproduce and verify ADIA starter baseline
Symbolic Module	Baseline	Implement ECA rule encoding, motif deltas
Latent Module	Symbolic Module	Train transformer + derive latent deltas
Topological Divergence	Latent Module	Add UMAP/curvature-based fault geometry
Unified Fusion	Symbolic + Latent	Score fusion model with symbolic-latent alignment
ROC Optimization	Unified Fusion	Tune classifier thresholds, improve AUC
Final Submission Wrapping	All modules	Convert to single submission-ready notebook with backup

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8. Appendix A: GitHub Citation Table

Component	Repo	Paper	Used For
Symbolic ECA	eca-rule-transform	Braun et al.	ECA motif deltas
Grammar Extraction	glenford-symbolic-ts	Shani & Braun	Symbolic sequences
Latent Attention	timeseries-transformer	Shani et al.	Latent flows
Topological Divergence	topo-ml	Walch et al.	Curvature shifts
Feature Alignment	tsflex	IDLab	Time-windowed fusion
ADIA Baseline	crunchdao/quickstarters	ADIA Lab	Base pipeline

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9. Appendix B: Submission Checklist

• Notebook conforms to train() and infer() API
• ROC AUC locally exceeds baseline (0.8+ target)
• Colab runtimes checkpoint every 10 hours
• All workers authenticated to central coordinator
• Backup notebook ready for Sept 30

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End of Whitepaper