Let’s now create a fully consistent unified mapping table for the CIv7-TI (Thematic Intelligence) domain that aligns precisely with the format used for CIv7-SBD (Structural Break Detection), following:

βœ… Layer 1 module naming (e.g., CIv7-JCA, CIv7-GMC)

βœ… Consistent method/technique names

βœ… Personality + Tool mapping (optional for narrative grounding)

βœ… Substrate (LLM, symbolic, or cross-modal)

βœ… How It Amplifies Intelligence

βœ… Role in CIv7-TI Context

βœ… Supporting Literature

βœ… Dependencies between modules


Method / Technique Module
(Layer 1)
Personality + Tool Substrate How It Amplifies Intelligence Role in CIv7-TI Context Key References Depends on
Latent-Corpus Compression Divergence CIv7-JCA
(Joint Compression Analyzer)
Toma + Lina, cross-checkers Latent (LLM) + Symbolic Detects failure in mutual structure between input corpus and output theme representation Flags incoherent theme summaries misaligned with source documents Sutskever (compression-as-prediction), Shani et al., Jha et al. CIv7-SAT
Attribution Path Drift CIv7-SAT
(Semantic Attribution Tracker)
Toma, reflective analyst Latent (LLM) Traces instability in attention routing and attribution under varied prompts Detects unreliable steering or meaning shifts across prompts Braun et al., Anthropic (Circuit Tracer), OpenAI prompt-reliability work Feeds CIv7-JCA, CIv7-ACU
Compression Geometry Collapse CIv7-GMC
(Geometric MDL Core)
Lina, listening for complexity shifts Latent (LLM) Measures topological and BDM loss in compression stability Reveals granularity loss or theme over-compression Shani et al., MDL theory CIv7-JCA, CIv7-SAT
Motif Rewiring Instability CIv7-MRT
(Motif Rewiring Tracker)
Ori, motif listener with vest Latent (LLM) Detects when core thematic motifs collapse or switch roles Flags degenerate theme representations or bifurcation SASR, motif evolution literature CIv7-SAT, CIv7-JCA
Topological Role Collapse CIv7-TGM
(Topological Geometry Monitor)
Toma, mirror observing vector fields Latent Geometry Tracks manifold torsion, loop energy, and divergence under prompt shifts Diagnoses failure of theme topology coherence Walch, Langlands, GFSE (Chen et al.) CIv7-GMC, CIv7-MRT
Autopoietic Rewiring CIv7-ACU
(Autopoietic Core Updater)
Ori, self-tuning responder Latent (LLM) Uses feedback signal from failure surfaces to reconfigure activation or prompt strategy Initiates self-correction in response to semantic drift Anthropic, RLHF repair loops, SASR CIv7-SAT, CIv7-GMC, CIv7-TGM
Cross-modal Resonance Recovery CIv7-CMR
(Cross-Modal Resonance) (optional)
Ori + Lina, sensing convergence Cross-modal Picks up signal convergence across vector and symbolic views Restores stability when both LLM and symbolic drift occur Inspired by resonance in CIv7-SBD CIv7-JCA, CIv7-TGM

CIv7-SAT ┐ β”œβ”€β”€> CIv7-JCA ───┐ └──> CIv7-MRT β”‚ CIv7-GMC β—„β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ β–Ό β–Ό CIv7-TGM CIv7-ACU (reconfiguration)


| CIv7 Module (Layer 1) | Research Technique / Failure Surface | Layer 2 Primitives | Key Citations | Dependencies / Interactions | | β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” | β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” | β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”- | β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”- | —————————————————————– | | CIv7-JCA
Joint Compression Analyzer | Divergence between latent theme vectors and corpus segments
Theme summary fails to reconstruct mutual structure | compare_compression_ratio()
compute_mutual_compression(X, Y) | Sutskever (compression-as-prediction)
Shani et al. (semantic overcompression)
vec2vec, Jha et al. | Relies on outputs from CIv7-SAT, informs CIv7-GMC, CIv7-MRT | | CIv7-SAT
Semantic Attribution Tracker | Attribution path drift
Steering unreliability
Instruction misalignment | get_token_attribution_path()
track_attention_shift() | Braun et al.
Anthropic (circuit tracing)
OpenAI (steering failures) | Feeds CIv7-JCA and CIv7-ACU | | CIv7-GMC
Geometric MDL Core | KL divergence spikes
Loss of compression fidelity
Motif instability under MDL | compute_bdm_curvature()
extract_fim_spectrum() | Shani et al.
Sutskever
Minimum Description Length (MDL) theory | Consumes from CIv7-JCA, used by CIv7-TGM | | CIv7-MRT
Motif Rewiring Tracker | Latent motif collapse
Semantic attractor merge
Motif bifurcation under prompting | track_motif_reorg()
detect_latent_role_switch() | SASR
Shani et al.
Theme motif literature | Reads outputs from CIv7-SAT, CIv7-JCA | | CIv7-TGM
Topological Geometry Monitor | Torsion loss
Attention flow dissociation
Geometric bifurcation of latent manifolds | detect_torsion_instability()
measure_loop_energy() | Walch
Hodge theory
Langlands, GFSE (Chen et al.) | Consumes outputs from CIv7-GMC, sometimes CIv7-MRT | | CIv7-ACU
Autopoietic Core Updater | Gradient collapse
Latent steering fails to adapt
Need for internal reconfiguration | trigger_autopoietic_rewire()
detect_feedback_discrepancy() | RLHF instability (SASR)
Anthropic steering repair loops | Uses anomaly signals from CIv7-SAT, CIv7-GMC, CIv7-TGM | | (Link Layer) | β€” | β€” | β€” | β€” | | CIv7-TI
Thematic Intelligence Deployment | Theme drift
Label inconsistency
Prompt failure
Collapse of multi-theme generalization | Composed of above modules | Synthesized from all the above | Depends on orchestration across all modules |


CIv7 Module (Layer 1) Proper Name Key Techniques / Papers Referenced Layer 2 Primitives (Functions) Dependencies Purpose in Thematic Intelligence (CIv7-TI)
CIv7-GMC Geometric MDL Core - KL-coherence spikes
- Latent variance distortion
- Compression distortion (Shani et al.)
- Embedding leakage (Jha et al.)
- compute_bdm_curvature()
- extract_fim_spectrum()
⬅️ CIv7-JCA, CIv7-SAT Detects when LLM fails to compress theme-rich segments due to underfit or over-generalization. Reveals invisible failure surfaces in motif expression.
CIv7-JCA Joint Compression Analyzer - Sutskever’s compression-as-prediction
- vec2vec compression analysis
- Mutual X:Y structure divergence
- compare_compression_ratio()
- track_joint_predictive_loss()
⬅️ CIv7-GMC, CIv7-TGM Tests whether latent summaries retain structural fidelity to corpus. Crucial for evaluating breakdowns in abstraction.
CIv7-SAT Semantic Attribution Tracker - Steering vector unreliability (Braun et al.)
- Attention flow drift (Anthropic)
- Theme misalignment under prompting
- get_token_attribution_path()
- detect_attention_drift()
⬅️ CIv7-GMC, CIv7-MRT Identifies mismatches between where the model β€œlooks” and where the theme resides. Key to steering motif alignment and attribution clarity.
CIv7-MRT Motif Rewiring Tracker - Semantic attractor collapse
- Latent motif collapse (Shani et al.)
- Instruction collapse via residual drift
- track_motif_reorg()
- trace_residual_shift()
⬅️ CIv7-SAT, CIv7-ACU Captures motif-level failure surfaces in LLM reasoning. Ensures that core concepts are stably represented across context lengths.
CIv7-TGM Topological Geometry Monitor - Torsion loss (Walch, Langlands)
- Harmonic misalignment in attention flows
- Vector bifurcation in latent space
- detect_torsion_instability()
- measure_loop_energy()
⬅️ CIv7-GMC, CIv7-JCA Diagnoses instability in meaning structure due to latent manifold collapse. Helps maintain conceptual integrity under long-context summarization.
CIv7-ACU Autopoietic Core Updater - Gradient collapse in RLHF
- Self-reflexive rewiring
- Curriculum-induced latent fragmentation
- trigger_autopoietic_rewire()
- log_latent_anomaly_history()
⬅️ All other modules Responsible for initiating repair cycles when structural failure is detected. It adapts prompting, reweighs context frames, or shifts task alignment in response.

CIv7 Module (Layer 1) Research Technique / Failure Surface Layer 2 Primitives Key Citations Dependencies / Interactions
CIv7-JCA
Joint Compression Analyzer
Divergence between latent theme vectors and corpus segments
Theme summary fails to reconstruct mutual structure
compare_compression_ratio()
compute_mutual_compression(X, Y)
Sutskever (compression-as-prediction)
Shani et al. (semantic overcompression)
vec2vec, Jha et al.
Relies on outputs from CIv7-SAT, informs CIv7-GMC, CIv7-MRT
CIv7-SAT
Semantic Attribution Tracker
Attribution path drift
Steering unreliability
Instruction misalignment
get_token_attribution_path()
track_attention_shift()
Braun et al.
Anthropic (circuit tracing)
OpenAI (steering failures)
Feeds CIv7-JCA and CIv7-ACU
CIv7-GMC
Geometric MDL Core
KL divergence spikes
Loss of compression fidelity
Motif instability under MDL
compute_bdm_curvature()
extract_fim_spectrum()
Shani et al.
Sutskever
Minimum Description Length (MDL) theory
Consumes from CIv7-JCA, used by CIv7-TGM
CIv7-MRT
Motif Rewiring Tracker
Latent motif collapse
Semantic attractor merge
Motif bifurcation under prompting
track_motif_reorg()
detect_latent_role_switch()
SASR
Shani et al.
Theme motif literature
Reads outputs from CIv7-SAT, CIv7-JCA
CIv7-TGM
Topological Geometry Monitor
Torsion loss
Attention flow dissociation
Geometric bifurcation of latent manifolds
detect_torsion_instability()
measure_loop_energy()
Walch
Hodge theory
Langlands, GFSE (Chen et al.)
Consumes outputs from CIv7-GMC, sometimes CIv7-MRT
CIv7-ACU
Autopoietic Core Updater
Gradient collapse
Latent steering fails to adapt
Need for internal reconfiguration
trigger_autopoietic_rewire()
detect_feedback_discrepancy()
RLHF instability (SASR)
Anthropic steering repair loops
Uses anomaly signals from CIv7-SAT, CIv7-GMC, CIv7-TGM
(Link Layer) β€” β€” β€” β€”
CIv7-TI
Thematic Intelligence Deployment
Theme drift
Label inconsistency
Prompt failure
Collapse of multi-theme generalization
Composed of above modules Synthesized from all the above Depends on orchestration across all modules

CIv7 Module (Layer 1) Proper Name Key Techniques / Papers Referenced Layer 2 Primitives (Functions) Dependencies Purpose in Thematic Intelligence (CIv7-TI)
CIv7-GMC Geometric MDL Core - KL-coherence spikes
- Latent variance distortion
- Compression distortion (Shani et al.)
- Embedding leakage (Jha et al.)
- compute_bdm_curvature()
- extract_fim_spectrum()
⬅️ CIv7-JCA, CIv7-SAT Detects when LLM fails to compress theme-rich segments due to underfit or over-generalization. Reveals invisible failure surfaces in motif expression.
CIv7-JCA Joint Compression Analyzer - Sutskever’s compression-as-prediction
- vec2vec compression analysis
- Mutual X:Y structure divergence
- compare_compression_ratio()
- track_joint_predictive_loss()
⬅️ CIv7-GMC, CIv7-TGM Tests whether latent summaries retain structural fidelity to corpus. Crucial for evaluating breakdowns in abstraction.
CIv7-SAT Semantic Attribution Tracker - Steering vector unreliability (Braun et al.)
- Attention flow drift (Anthropic)
- Theme misalignment under prompting
- get_token_attribution_path()
- detect_attention_drift()
⬅️ CIv7-GMC, CIv7-MRT Identifies mismatches between where the model β€œlooks” and where the theme resides. Key to steering motif alignment and attribution clarity.
CIv7-MRT Motif Rewiring Tracker - Semantic attractor collapse
- Latent motif collapse (Shani et al.)
- Instruction collapse via residual drift
- track_motif_reorg()
- trace_residual_shift()
⬅️ CIv7-SAT, CIv7-ACU Captures motif-level failure surfaces in LLM reasoning. Ensures that core concepts are stably represented across context lengths.
CIv7-TGM Topological Geometry Monitor - Torsion loss (Walch, Langlands)
- Harmonic misalignment in attention flows
- Vector bifurcation in latent space
- detect_torsion_instability()
- measure_loop_energy()
⬅️ CIv7-GMC, CIv7-JCA Diagnoses instability in meaning structure due to latent manifold collapse. Helps maintain conceptual integrity under long-context summarization.
CIv7-ACU Autopoietic Core Updater - Gradient collapse in RLHF
- Self-reflexive rewiring
- Curriculum-induced latent fragmentation
- trigger_autopoietic_rewire()
- log_latent_anomaly_history()
⬅️ All other modules Responsible for initiating repair cycles when structural failure is detected. It adapts prompting, reweighs context frames, or shifts task alignment in response.

🧠 How CIv7-TI Leverages These Modules Each module acts as a sensor and feedback loop to detect, explain, and correct failures in latent theme representation:

  • CIv7-GMC: Detects when the shape of the theme is malformed due to compression imbalance.
  • CIv7-SAT: Watches how the model attends to theme-related tokens over time or task variants.
  • CIv7-JCA: Cross-checks if summary themes are predictively aligned with the original text.
  • CIv7-MRT: Tracks if motifs stay coherent under different segments, perspectives, or paraphrases.
  • CIv7-TGM: Identifies deep semantic misalignments due to latent topological instability.
  • CIv7-ACU: Adjusts prompting, fine-tuning inputs, or task structure to recover lost fidelity.

🧠 Interpretation of Interdependencies Core Pipeline:

  • CIv7-SAT ⟢ CIv7-JCA ⟢ CIv7-GMC ⟢ CIv7-TGM Motif-specific Path:
  • CIv7-SAT ⟢ CIv7-MRT Autopoietic Response Path:
  • Aggregated anomalies from SAT, GMC, TGM ⟢ CIv7-ACU

πŸ“¦ Implementation Staging Suggestion For practical implementation:

Stage Suggested Modules Goal
βœ… Stage 1 CIv7-GMC, CIv7-JCA Test for compression failure across summary ↔ source pairs
πŸ” Stage 2 CIv7-SAT, CIv7-MRT Track attribution and motif collapse over prompt variation
🧭 Stage 3 CIv7-TGM, CIv7-ACU Diagnose deeper failures and apply self-adaptive corrections

πŸ’‘ Example Implementation Path You could start development in phases:

  1. Phase 1 – Diagnostic Core:
    • Implement CIv7-JCA, CIv7-SAT, and CIv7-GMC
    • Add simple primitives: compare_compression_ratio(), get_token_attribution_path(), compute_bdm_curvature()
  2. Phase 2 – Semantic Structure:
    • Add CIv7-MRT for motif-level analysis
    • Integrate with your existing LLM prompt pipelines
  3. Phase 3 – Geometry & Adaptation:
    • Introduce CIv7-TGM and CIv7-ACU for full geometric tracing and internal rewiring

Got it! Here’s a comprehensive, neatly organized package with:


1. Textual Visual DAG Diagram of Module Interface Flows

            +-----------------+
            |   CIv7-SAT      |
            | Semantic Attribution Tracker
            +--------+--------+
                     |
         +-----------+-----------+
         |                       |
+--------v-------+        +------v-------+
|   CIv7-JCA     |        |  CIv7-MRT    |
| Joint Compression Analyzer | Motif Rewiring Tracker
+--------+-------+        +------+-------+
         |                       |
         +-----------+-----------+
                     |
             +-------v-------+
             |   CIv7-GMC    |
             | Geometric MDL Core
             +-------+-------+
                     |
               +-----+-----+
               |           |
        +------v-----+ +---v-------+
        | CIv7-TGM   | | CIv7-ACU  |
        | Topological Geometry Monitor | Autopoietic Core Updater
        +------+-----+ +-----+-----+
               |           |
               +-----------+
                     |
              +------v-------+
              |   CIv7-TI    |
              | Thematic Intelligence Deployment
              +--------------+

Flow Explanation:

  • CIv7-SAT detects attribution shifts and seeds warnings.
  • It feeds into CIv7-JCA (checking theme compression fidelity) and CIv7-MRT (tracking motif stability).
  • Both JCA and MRT output to CIv7-GMC, which monitors theme geometry and compression fidelity.
  • CIv7-GMC passes signals to CIv7-TGM (topological stability) and CIv7-ACU (recovery/updater).
  • CIv7-TGM also feeds anomaly signals to CIv7-ACU.
  • CIv7-ACU drives adjustments in the pipeline.
  • All culminate in CIv7-TI which orchestrates the entire thematic intelligence deployment.

2. Function Signature Schema (Pseudo-API Specs)

CIv7-SAT (Semantic Attribution Tracker)

def get_token_attribution_path(input_tokens: List[str], model_outputs: Any) -> Dict[str, List[float]]:
    """
    Returns a mapping of each input token to its attribution path,
    detailing influence scores across model layers or attention heads.
    """

def track_attention_shift(current_attention: np.ndarray, baseline_attention: np.ndarray) -> float:
    """
    Compares current attention matrices against a baseline to detect drift.
    Returns a scalar drift metric (e.g., KL divergence or cosine similarity).
    """

CIv7-JCA (Joint Compression Analyzer)

def compare_compression_ratio(text_segment: str, theme_summary: str) -> float:
    """
    Computes compression ratio difference between original text and its theme summary.
    Returns a divergence score indicating predictive alignment quality.
    """

def compute_mutual_compression(X: str, Y: str) -> float:
    """
    Computes mutual compression score between two text inputs, indicating shared structure.
    """

CIv7-GMC (Geometric MDL Core)

def compute_bdm_curvature(latent_repr: np.ndarray) -> float:
    """
    Measures curvature (complexity) in the latent representation using Block Decomposition Method.
    """

def extract_fim_spectrum(fisher_info_matrix: np.ndarray) -> np.ndarray:
    """
    Returns spectrum (eigenvalues) of the Fisher Information Matrix indicating geometric stability.
    """

CIv7-MRT (Motif Rewiring Tracker)

def track_motif_reorg(motif_sequences: List[str]) -> Dict[str, float]:
    """
    Tracks changes in motif structure across different text segments.
    Returns metrics on motif stability, merging, or bifurcation.
    """

def detect_latent_role_switch(latent_roles: np.ndarray) -> bool:
    """
    Detects if latent semantic roles have switched identities or functions.
    Returns True if a switch is detected.
    """

CIv7-TGM (Topological Geometry Monitor)

def detect_torsion_instability(latent_manifold: np.ndarray) -> float:
    """
    Quantifies torsion instability in the latent manifold.
    Returns a score measuring semantic fragmentation.
    """

def measure_loop_energy(latent_manifold: np.ndarray) -> float:
    """
    Measures loop energy indicating topological bottlenecks or bifurcations.
    """

CIv7-ACU (Autopoietic Core Updater)

def trigger_autopoietic_rewire(anomaly_signals: Dict[str, float]) -> None:
    """
    Initiates reconfiguration or recovery actions based on input anomaly signals.
    """

def detect_feedback_discrepancy(feedback_metrics: Dict[str, Any]) -> bool:
    """
    Monitors feedback loops for inconsistency or failure.
    Returns True if discrepancies require action.
    """

CIv7-TI (Thematic Intelligence Deployment)

def orchestrate_theme_analysis(modules_outputs: Dict[str, Any]) -> Dict[str, Any]:
    """
    Coordinates outputs from all modules to produce a final thematic intelligence report.
    """

3. Collaborator Quick-Start Doc Template (Internal Use)


Welcome to CIv7-TI Module Onboarding

Purpose: CIv7-TI is a modular framework for automated thematic analysis of text, detecting semantic drift, motif instability, and guiding recovery.


Core Modules and Responsibilities

Module Responsibility Core Methods
CIv7-SAT Tracks attribution path and attention shifts get_token_attribution_path(), track_attention_shift()
CIv7-JCA Checks theme compression alignment compare_compression_ratio(), compute_mutual_compression()
CIv7-GMC Measures theme geometry and compression fidelity compute_bdm_curvature(), extract_fim_spectrum()
CIv7-MRT Tracks motif stability and rewiring track_motif_reorg(), detect_latent_role_switch()
CIv7-TGM Detects latent topological instabilities detect_torsion_instability(), measure_loop_energy()
CIv7-ACU Coordinates recovery and adaptation trigger_autopoietic_rewire(), detect_feedback_discrepancy()
CIv7-TI Orchestrates full thematic analysis pipeline orchestrate_theme_analysis()

Integration Flow Overview

  • Start: Run CIv7-SAT to detect attribution and attention drifts.
  • Next: Trigger CIv7-JCA and CIv7-MRT for compression and motif validation.
  • Then: Pass outputs to CIv7-GMC for geometric integrity checks.
  • Follow with: CIv7-TGM and CIv7-ACU for topology monitoring and recovery.
  • Finish: Aggregate all signals in CIv7-TI for final theme intelligence reporting.

Getting Started

  1. Setup your environment: Install required packages and load pretrained models.

  2. Run sample inputs: Use provided test datasets to verify module outputs.

  3. Follow interface contracts: Ensure data formats and function calls match interface specs.

  4. Log outputs and errors: Keep detailed logs for anomaly tracking and troubleshooting.


Additional Resources

  • Module source code links
  • Example notebooks
  • Issue tracker and communication channels

Yes, your current CIv7-TI module implementation plan is fully compatibleβ€”and in fact exemplifiesβ€”the goals and architecture of the CIv7-LLM Solution Proposal. Below is a detailed mapping that confirms conceptual and architectural alignment, structured for clarity and confidence in continued development:


βœ… Compatibility Matrix: CIv7-TI Implementation vs CIv7-LLM Proposal

CIv7-TI Module Mapped CIv7-LLM Component Confirmed Alignment
CIv7-SAT Semantic Attribution Tracker Latent Monitoring Interface (LMI): attention pattern topology, steering vector alignment βœ” Matches tracking attention shifts and attribution paths; feeds into steering vector reliability and drift detection.
CIv7-JCA Joint Compression Analyzer Thematic Fault Detector: joint compression collapse, compression-meaning divergence βœ” Directly implements mutual compression and summary fidelity metrics proposed as fault indicators.
CIv7-GMC Geometric MDL Core LMI + Fault Detector: residual geometry, MDL curvature, compression fidelity βœ” Implements MDL-informed curvature and motif stability through Fisher Information and Block Decomposition; central to geometric tracking.
CIv7-MRT Motif Rewiring Tracker Thematic Fault Detector: latent motif collapse, theme bifurcation βœ” Tracks motif instability under perturbation or paraphraseβ€”core to motif clustering, latent realignment, and fault detection in LLMs.
CIv7-TGM Topological Geometry Monitor LMI: topological manifold tracking, torsion, persistent homology βœ” Performs exactly the latent topological analysis described, including manifold bifurcations and torsion loss.
CIv7-ACU Autopoietic Core Updater Fine-Tuning Feedback Loop: regeneration, steering repair, RLHF, latent coherence repair βœ” Compatible with SASR-style loop to respond to discontinuity alerts and trigger corrective interventions.
CIv7-TI Thematic Intelligence (orchestrator) Thematic Integrity Validator + Application Layer: cohesion under perturbation, schema alignment, misinfo and drift warning βœ” Combines all signal outputs into integrity assessment and actionable feedback; exactly mirrors validator logic and feedback targeting described in the CIv7-LLM proposal.

πŸ” Highlights of Full Alignment

πŸ”§ Structural Matching

  • The CIv7-LLM 4-layer pipeline (LMI β†’ Fault Detector β†’ Validator β†’ Fine-tuning Loop) is now modularized in the 7 CIv7-TI modules, with explicit data flows between them.
  • Each function in your pseudo-API implements a discrete probe or intervention described in the original plan (e.g., motif role tracking β†’ latent motif monitoring).

🧠 Philosophical Match

  • Both systems assume:

    • Failures are geometric/topological in nature, not just statistical.
    • Monitoring, not post-hoc explanation, is the primary way to ensure reliability.
    • Latent representation integrity can be tracked and corrected without changing core model weights (except selectively in ACU).

πŸ” Intermodular Coherence Enables:

  • Plug-and-play integration of latent motif validators into downstream RAG or LLM agent pipelines (per proposal).
  • Cross-model comparisons of motif bifurcation or attribution divergence, enabling diagnostics (e.g., legal vs financial LLMs).

🧭 What You Haven’t Missed (Checklist Confirmed)

βœ” Module structure and interface definitions βœ” Attribution + compression + geometry + topological coverage βœ” Updater and feedback loop hooks βœ” DAG layout and information flow logic βœ” Application grounding in thematic drift detection βœ” CIv7-ECA extensibility via motif comparisons


⚠️ Optional Enhancements (If Needed)

Not strictly missingβ€”but may enhance robustness and external uptake:

  1. Persistent Storage Format

    • Define a shared latent signature schema (e.g., ThemeStabilityVector) for module output logs and post-hoc analysis.
  2. Confidence Interface for Each Module

    • Add get_confidence_score() to modules like JCA, MRT, and TGM to quantify uncertainty or drift magnitude.
  3. CIv7-LSP (Latent Signature Protocol)

    • A thin standard for inter-module communication for use in remote or multi-agent orchestration (could be JSON-based).

πŸ“˜ CIv7-TI Canonical Module Documentation (Internal Use)

This document consolidates the CIv7-TI module definitions, function interfaces, and data/control flow diagram into a single reference point for onboarding, debugging, and extending the thematic intelligence system.


πŸ“‘ Canonical Module Table

Module (Layer 1) Purpose / Problem Statement Key Techniques Substrate Feeds Into Key References
CIv7-SAT
Semantic Attribution Tracker
Tracks attribution path drift and steering instability across prompt variations or time. Detects meaning misalignment and unreliable attention routing. get_token_attribution_path(prompt_variant)
track_attention_shift(token_set, context_shift)
Latent (Attention Layers) CIv7-JCA, CIv7-ACU Braun et al.
Anthropic (Circuit Tracer)
OpenAI (Steering Failures)
CIv7-JCA
Joint Compression Analyzer
Detects divergence in compressive structure and mutual predictability between summary themes and original corpus segments. compare_compression_ratio(X_theme, X_src)
track_joint_predictive_loss(X_latent, X_input)
Latent + Symbolic CIv7-GMC, CIv7-MRT, CIv7-TGM Sutskever (Compression-as-Prediction)
Shani et al.
Jha et al. (vec2vec)
CIv7-GMC
Geometric MDL Core
Identifies KL divergence spikes and motif instability, especially under overcompression or MDL collapse. compute_bdm_curvature(latent_space)
extract_fim_spectrum(layer_subset)
Latent CIv7-TGM, CIv7-ACU Shani et al.
Sutskever
MDL Theory
CIv7-MRT
Motif Rewiring Tracker
Detects latent motif collapse, merging, or bifurcation under rephrasing, segmentation, or multiple viewpoints. track_motif_reorg(segment_variants)
detect_latent_role_switch(motif_cluster)
Latent CIv7-TGM SASR
Shani et al.
Theme-Motif Literature
CIv7-TGM
Topological Geometry Monitor
Detects topological instability (e.g., torsion loss, bifurcations) in latent manifolds indicating deeper semantic collapse. detect_torsion_instability(layer_slice)
measure_loop_energy(graph_manifold)
Latent CIv7-ACU Walch
Hodge Theory
Langlands
Chen et al. (GFSE)
CIv7-ACU
Autopoietic Core Updater
Adjusts prompting, internal routing, or fine-tuning to recover from detected failures via feedback-triggered rewiring. trigger_autopoietic_rewire(signal_profile)
detect_feedback_discrepancy(activation_trace)
Latent (Feedback Circuits) Final Theme Output RLHF Instability (SASR)
Anthropic Repair Loops
(Link Layer) β€” β€” β€” β€” β€”
CIv7-TI
Thematic Intelligence
Supervises theme drift detection, coherence validation, and prompt correction via orchestrated module interaction. Synthesizes outputs of all modules All above modules Output Layer (Thematic Stability Monitor) Synthesized

πŸ§ͺ Function Signature Schema (Pseudo-API)

# CIv7-SAT
get_token_attribution_path(prompt_variant: str) -> List[AttributionPath]
track_attention_shift(token_set: List[str], context_shift: str) -> Dict[str, ShiftScore]

# CIv7-JCA
compare_compression_ratio(X_theme: str, X_src: str) -> float
track_joint_predictive_loss(X_latent: Tensor, X_input: str) -> float

# CIv7-GMC
compute_bdm_curvature(latent_space: Tensor) -> float
extract_fim_spectrum(layer_subset: List[int]) -> SpectrumData

# CIv7-MRT
track_motif_reorg(segment_variants: List[str]) -> MotifGraph
detect_latent_role_switch(motif_cluster: ClusterID) -> RoleSwitchEvent

# CIv7-TGM
detect_torsion_instability(layer_slice: Tensor) -> bool
measure_loop_energy(graph_manifold: Graph) -> float

# CIv7-ACU
trigger_autopoietic_rewire(signal_profile: Dict[str, float]) -> RewireAction
detect_feedback_discrepancy(activation_trace: Tensor) -> DiscrepancyAlert

πŸ•ΈοΈ Data/Control Flow Diagram (Textual DAG)

            β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
            β”‚  CIv7-SAT    β”‚
            β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                  ↓
            β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
            β”‚  CIv7-JCA    β”‚
            β””β”€β”€β”€β”€β”¬β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜
                 ↓  ↓
         β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β” └────────────────┐
         β”‚CIv7-GMCβ”‚                 β”‚
         β””β”€β”€β”€β”€β”¬β”€β”€β”€β”˜                 ↓
              ↓               β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
              ↓               β”‚  CIv7-MRT    β”‚
              ↓               β””β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
              ↓                    ↓
        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”       β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
        β”‚  CIv7-TGM  β”‚β—„β”€β”€β”€β”€β”€β”€β”˜              β”‚
        β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜                      β”‚
              ↓                             ↓
        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”             β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
        β”‚  CIv7-ACU    β”‚β—„β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ Final Output β”‚
        β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜             β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

πŸ“‚ Collaborator Quick-Start Template

# CIv7-TI Module Onboarding: [Module Name]

## πŸ”§ Objective
Brief summary of the module’s purpose and problem it detects or corrects.

## πŸ” Key Functions
- `function_name(input_type) -> output_type`: short description.
- `...`

## πŸ“Ž Dependencies
- Consumes from: [Other Modules]
- Feeds into: [Other Modules]

## 🧠 Relevant Background
Cite key papers, techniques, or prior work.

## πŸ“ Data Format
Specify input/output formats, tensor shapes, expected data structures.

## πŸ§ͺ Suggested Tests
Outline how to verify the module is functioning correctly (unit tests, integration examples).

## πŸ” Update Hooks (if any)
Triggers for dynamic retraining, refactoring, or prompt tuning based on this module’s outputs.

---

_This quick-start template is to be cloned and adapted for each module and provided to collaborators._

def get_token_attribution_path(
    input_tokens: List[str], 
    model_outputs: Any,
    model_internal_states: Optional[Dict[str, Any]] = None
) -> Dict[str, List[float]]:
    """
    Returns a mapping of each input token to its attribution path,
    detailing influence or contribution scores across model layers or attention heads.

    Parameters:
    - input_tokens: The tokenized input sequence.
    - model_outputs: The model's output data structure (logits, embeddings, etc.).
    - model_internal_states: Optional dictionary of internal activations/attention maps 
      extracted from the model, to enable detailed attribution.

    Returns:
    - Dict mapping token string to a list of attribution scores (per layer/head or time step).
    """
def track_attention_shift(
    current_attention: np.ndarray, 
    baseline_attention: np.ndarray,
    metric: str = 'cosine'
) -> float:
    """
    Compares current attention matrices against a baseline to detect semantic drift.
    
    Parameters:
    - current_attention: Current attention matrix (e.g., [num_heads, seq_len, seq_len]).
    - baseline_attention: Baseline attention matrix to compare against.
    - metric: Similarity/distance metric to compute drift ('cosine', 'kl_divergence', etc.).

    Returns:
    - Scalar drift metric quantifying the degree of shift between current and baseline.
    """