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Provisional Title:

CIv6-SBD: Geometric Fault Line Detection via Cybernetic Intelligence v6

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Purpose:

To operationalize CIv6 into a targeted system for identifying regime shifts or structural breaks in time series or textual systems, using internal geometric, topological, and algorithmic signals as fault detectors.

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Core Proposal:

We hypothesize that structural breaks can be directly inferred from internal state distortions in a Cybernetic LLM, by tracking:

1. Loop Geometry Breakdown

   • Monitor semantic ring energy (Wilson loops, motif closures).
   • A break = fragmentation or loss of loop coherence.

2. Curvature Distortion in FIM Landscape

   • Track Fisher Information curvature, spectral entropy, and negative complexity flow.
   • A break = sharp spectral shift or local flattening/spike in information geometry.

3. Collapse of Discrete Concepts

   • Via ECA-LLM lattice: Identify motif dissolution, compression failure, or instability in automata-driven state evolution.
   • A break = motif extinction or topological collapse.

4. Attribution Drift Signatures

   • Use Sakabe et al.’s first-principles attribution analysis to identify when input-output meaning becomes misaligned.
   • A break = divergence in attribution trajectory over similar inputs.

5. Entropy Feedback Divergence

   • Sudden increase or irregularity in token-wise heat flow, implying semantic overfit or chaotic attractors.

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