Cybernetic Intelligence

CIv7-LLM: Solution Proposal

Latent Fault Geometry and Meaning Stability in Large Language Models

✅ Objective

Design an end-to-end framework that detects and preempts latent failures in language models (LLMs)—such as hallucination, steering vector unreliability, and misaligned generalisation—by tracking geometric, algorithmic, and topological discontinuities in latent activations. We target applications in textual thematic intelligence, including thematic drift detection in legal, financial, and policy corpora.

🧠 Core Premise

Latent layers of LLMs encode not just meaning, but compressive and geometric regularities. Failures in reasoning, inference, and semantic coherence are not random: they emerge from topological instabilities, directional drift, and compression collapse. These failures are detectable, traceable, and—when properly modelled—correctable.

🔧 Implementation Plan

1. Latent Monitoring Interface (LMI)

A plugin layer that tracks the following in real-time:

Residual stream activations across selected layers (e.g., L12–L28 in GPT-style models).
Attention pattern topology (e.g., persistent homology of attention maps).
Logit dynamics and entropy shifts (predictive stability).
Steering vector alignment metrics (directional agreement + separability, per Braun et al.).

2. Thematic Fault Detector

For each text segment or generation pass:

Calculate latent motifs (e.g., via activation clustering or circuit tracing).
Score discontinuity indicators:
- Joint compression collapse (e.g., reconstruction divergence from base representation).
- Steering irreliability (anti-steerable rate, directional variance).
- Compression-meaning divergence (compare LLM cluster fidelity to human taxonomies, as in Shani et al.).
- Reality signal drift (inconsistency between latent content and input semantics; cf. Dijkstra et al.).
Output: theme stability vector + discontinuity alert per token or phrase.

3. Thematic Integrity Validator

A post-generation checker that cross-validates:

Consistency with prior themes or user-defined constraints.
Cohesion of latent representation under perturbation (e.g., dropout, rephrasing, negation).
Compatibility with prior CoT traces or schema graphs (circuit validation).

4. Fine-tuning Feedback Loop

Based on discontinuity alerts:

Regenerate from stable checkpoints when theme drift is detected.
Apply adaptive RL fine-tuning (SASR-inspired) to correct instabilities.
Prioritise gradient updates that reinforce latent coherence, steering stability, and compression robustness.

🧭 Application: Textual Thematic Intelligence System

Target domains:

Regulatory impact assessments
Legal and policy corpus synthesis
Financial narrative forecasting
Real-time misinformation detection

Functionality:

Detects “semantic stress points” before hallucination or misclassification.
Warns users when a generated theme has crossed a latent discontinuity.
Recovers coherent narratives by steering away from unstable latent manifolds.
Performs cross-model diagnosis—e.g., comparing a policy model’s latent faults to those in a financial LLM for correlated break predictions.

🔁 Feedback-Driven Extension

Pair with symbolic substrate from CIv7-ECA to detect mutual breakpoints.
Cross-validate latent motif shifts (LLM) against symbolic motif phase transitions (ECA).
Embed self-supervised latent validators into RAG pipelines or legal QA agents.

🚨 Outcome

By implementing the CIv7-LLM solution:

Textual systems become robust against theme drift and hallucination.
Steering interventions are guided by reliable topological markers.
Failure surfaces are no longer black-box—they become diagnosable, model-agnostic geometries.