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:

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

✅ Consistent method/technique names

✅ Personality + Tool mapping (optional for narrative grounding)

✅ Substrate (LLM, symbolic, or cross-modal)

✅ How It Amplifies Intelligence

✅ Role in CIv7-TI Context

✅ Supporting Literature

✅ 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 ┐ ├──> CIv7-JCA ───┐ └──> CIv7-MRT │ CIv7-GMC ◄────────────────┘ │ │ ▼ ▼ CIv7-TGM CIv7-ACU (reconfiguration)

| 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) | — | — | — | — | | 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 “looks” 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)	—	—	—	—
`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 “looks” 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

📦 Implementation Staging Suggestion For practical implementation:

Stage	Suggested Modules	Goal
✅ Stage 1	`CIv7-GMC`, `CIv7-JCA`	Test for compression failure across summary ↔ source pairs
🔍 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

💡 Example Implementation Path You could start development in phases:

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()
Phase 2 – Semantic Structure:
- Add CIv7-MRT for motif-level analysis
- Integrate with your existing LLM prompt pipelines
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

Setup your environment: Install required packages and load pretrained models.
Run sample inputs: Use provided test datasets to verify module outputs.
Follow interface contracts: Ensure data formats and function calls match interface specs.
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 compatible—and in fact exemplifies—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:

✅ 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	✔ 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	✔ 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	✔ 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	✔ Tracks motif instability under perturbation or paraphrase—core to motif clustering, latent realignment, and fault detection in LLMs.
`CIv7-TGM` Topological Geometry Monitor	LMI: topological manifold tracking, torsion, persistent homology	✔ 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	✔ 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	✔ Combines all signal outputs into integrity assessment and actionable feedback; exactly mirrors validator logic and feedback targeting described in the CIv7-LLM proposal.

🔍 Highlights of Full Alignment

🔧 Structural Matching

The CIv7-LLM 4-layer pipeline (LMI → Fault Detector → Validator → 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 → 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).

🔁 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)

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

⚠️ Optional Enhancements (If Needed)

Not strictly missing—but may enhance robustness and external uptake:

Persistent Storage Format
- Define a shared latent signature schema (e.g., ThemeStabilityVector) for module output logs and post-hoc analysis.
Confidence Interface for Each Module
- Add get_confidence_score() to modules like JCA, MRT, and TGM to quantify uncertainty or drift magnitude.
CIv7-LSP (Latent Signature Protocol)
- A thin standard for inter-module communication for use in remote or multi-agent orchestration (could be JSON-based).

📘 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.

📑 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)	—	—	—	—	—
`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

🧪 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

🕸️ Data/Control Flow Diagram (Textual DAG)

            ┌──────────────┐
            │  CIv7-SAT    │
            └─────┬────────┘
                  ↓
            ┌──────────────┐
            │  CIv7-JCA    │
            └────┬──┬──────┘
                 ↓  ↓
         ┌────────┐ └────────────────┐
         │CIv7-GMC│                 │
         └────┬───┘                 ↓
              ↓               ┌──────────────┐
              ↓               │  CIv7-MRT    │
              ↓               └────┬─────────┘
              ↓                    ↓
        ┌────────────┐       ┌──────────────┐
        │  CIv7-TGM  │◄──────┘              │
        └─────┬──────┘                      │
              ↓                             ↓
        ┌──────────────┐             ┌──────────────┐
        │  CIv7-ACU    │◄────────────┘ Final Output │
        └──────────────┘             └──────────────┘

📂 Collaborator Quick-Start Template

# CIv7-TI Module Onboarding: [Module Name]

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

## 🔍 Key Functions
- `function_name(input_type) -> output_type`: short description.
- `...`

## 📎 Dependencies
- Consumes from: [Other Modules]
- Feeds into: [Other Modules]

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

## 📁 Data Format
Specify input/output formats, tensor shapes, expected data structures.

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

## 🔁 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.
    """