Letβs 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:
β Layer 1 module naming (e.g., CIv7-JCA, CIv7-GMC)
β Consistent method/technique names
β Personality + Tool mapping (optional for narrative grounding)
β Substrate (LLM, symbolic, or cross-modal)
β How It Amplifies Intelligence
β Role in CIv7-TI Context
β Supporting Literature
β 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 βββ> CIv7-JCA ββββ βββ> CIv7-MRT β CIv7-GMC ββββββββββββββββββ β β βΌ βΌ CIv7-TGM CIv7-ACU (reconfiguration)
| CIv7 Module (Layer 1) | Research Technique / Failure Surface | Layer 2 Primitives | Key Citations | Dependencies / Interactions |
| βββββββββββββββββ | ββββββββββββββββββββββββββββββββββββββ | ββββββββββββββββββββββ- | ββββββββββββββββββββββββββββββββββ- | ββββββββββββββββββββββ |
| 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() |
β¬
οΈ 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 | - Sutskeverβs compression-as-prediction - vec2vec compression analysis - Mutual X:Y structure divergence |
- compare_compression_ratio() - track_joint_predictive_loss() |
β¬
οΈ 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() |
β¬
οΈ CIv7-GMC , CIv7-MRT |
Identifies mismatches between where the model βlooksβ 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() |
β¬
οΈ 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() |
β¬
οΈ 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() |
β¬ οΈ 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() |
β¬
οΈ 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 | - Sutskeverβs compression-as-prediction - vec2vec compression analysis - Mutual X:Y structure divergence |
- compare_compression_ratio() - track_joint_predictive_loss() |
β¬
οΈ 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() |
β¬
οΈ CIv7-GMC , CIv7-MRT |
Identifies mismatches between where the model βlooksβ 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() |
β¬
οΈ 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() |
β¬
οΈ 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() |
β¬ οΈ 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. |
π§ 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.
π§ Interpretation of Interdependencies Core Pipeline:
- CIv7-SAT βΆ CIv7-JCA βΆ CIv7-GMC βΆ CIv7-TGM Motif-specific Path:
- CIv7-SAT βΆ CIv7-MRT Autopoietic Response Path:
- Aggregated anomalies from SAT, GMC, TGM βΆ CIv7-ACU
π¦ Implementation Staging Suggestion For practical implementation:
Stage | Suggested Modules | Goal |
---|---|---|
β Stage 1 | CIv7-GMC , CIv7-JCA |
Test for compression failure across summary β source pairs |
π Stage 2 | CIv7-SAT , CIv7-MRT |
Track attribution and motif collapse over prompt variation |
π§ Stage 3 | CIv7-TGM , CIv7-ACU |
Diagnose deeper failures and apply self-adaptive corrections |
π‘ Example Implementation Path You could start development in phases:
- Phase 1 β 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 β Semantic Structure:
- Add CIv7-MRT for motif-level analysis
- Integrate with your existing LLM prompt pipelines
- Phase 3 β Geometry & Adaptation:
- Introduce CIv7-TGM and CIv7-ACU for full geometric tracing and internal rewiring
Got it! Hereβs 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
andCIv7-MRT
for compression and motif validation. - Then: Pass outputs to
CIv7-GMC
for geometric integrity checks. - Follow with:
CIv7-TGM
andCIv7-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 compatibleβand in fact exemplifiesβthe 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:
β 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 | β 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 | β 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 | β 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 | β Tracks motif instability under perturbation or paraphraseβcore to motif clustering, latent realignment, and fault detection in LLMs. |
CIv7-TGM Topological Geometry Monitor |
LMI: topological manifold tracking, torsion, persistent homology | β 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 | β 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 | β Combines all signal outputs into integrity assessment and actionable feedback; exactly mirrors validator logic and feedback targeting described in the CIv7-LLM proposal. |
π Highlights of Full Alignment
π§ 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).
π§ 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).
π 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).
π§ What You Havenβt Missed (Checklist Confirmed)
β Module structure and interface definitions β Attribution + compression + geometry + topological coverage β Updater and feedback loop hooks β DAG layout and information flow logic β Application grounding in thematic drift detection β CIv7-ECA extensibility via motif comparisons
β οΈ Optional Enhancements (If Needed)
Not strictly missingβbut 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.
- Define a shared latent signature schema (e.g.,
-
Confidence Interface for Each Module
- Add
get_confidence_score()
to modules likeJCA
,MRT
, andTGM
to quantify uncertainty or drift magnitude.
- Add
-
CIv7-LSP (Latent Signature Protocol)
- A thin standard for inter-module communication for use in remote or multi-agent orchestration (could be JSON-based).
π 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.
π 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 |
π§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
πΈοΈ Data/Control Flow Diagram (Textual DAG)
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β CIv7-SAT β
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β
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β CIv7-JCA β
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β β
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βCIv7-GMCβ β
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β ββββββββββββββββ
β β CIv7-MRT β
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β β
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β CIv7-TGM βββββββββ β
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β β
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β CIv7-ACU βββββββββββββββ Final Output β
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π Collaborator Quick-Start Template
# CIv7-TI Module Onboarding: [Module Name]
## π§ Objective
Brief summary of the moduleβs purpose and problem it detects or corrects.
## π Key Functions
- `function_name(input_type) -> output_type`: short description.
- `...`
## π Dependencies
- Consumes from: [Other Modules]
- Feeds into: [Other Modules]
## π§ Relevant Background
Cite key papers, techniques, or prior work.
## π Data Format
Specify input/output formats, tensor shapes, expected data structures.
## π§ͺ Suggested Tests
Outline how to verify the module is functioning correctly (unit tests, integration examples).
## π Update Hooks (if any)
Triggers for dynamic retraining, refactoring, or prompt tuning based on this moduleβs 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.
"""