CIv7-LLM: Latent Fault Geometry via Compression-Aligned Failure Surfaces in Language Models

General Hypothesis

Structural failures in language model behavior—such as hallucination, steering unreliability, generalisation collapse, and semantic drift—can be robustly detected and interpreted by analyzing latent representations (residual stream activations, attention flows, feedforward layer outputs) as a compressive and topological substrate.

This latent substrate exhibits discontinuities that serve as failure surfaces, exposing breakdowns in causal coherence, conceptual fidelity, and semantic topology. These transitions are algorithmic, geometric, and information-theoretic in nature, and correspond to failures in:

  • Joint compression across unsupervised embeddings and supervised task representations (Sutskever)
  • Latent coherence collapse under perturbations in input, prompting, or fine-tuning strategies
  • Steering vector unreliability due to inconsistency in activation geometry (Braun et al.)
  • Semantic underfitting due to over-compression (Shani et al.)
  • Attractor basin instability in residual stream trajectories under multi-task training (SASR)
  • Loss of torsion and cohomology in attention-induced manifold flows (Walch, Hodge, Langlands)
  • Vector field bifurcations in attention heads during role-switching or instruction generalisation
  • Failure of harmonic alignment between context frames and internal latent circuits (Anthropic, OpenAI)
  • Embedding inversion leakage under cross-model projection (vec2vec, Jha et al.)
  • Topological misalignment in graph-augmented inputs (GFSE, Chen et al.)

These discontinuities manifest in both quantitative terms (e.g., loss of steering reliability, drop in KL-coherence, activation variance spikes) and geometric/algorithmic terms (e.g., manifold folding, torsion loss, compression asymmetry).

Distinguished Application: Textual Thematic Intelligence

In the context of thematic analysis of large text corpora, CIv7-LLM hypothesizes that latent fault geometry can explain and resolve common failures in:

  • Topic drift in long-context summarization
  • Loss of conceptual granularity in supervised fine-tuning
  • Inconsistency in thematic labelling across clusters
  • Collapse of representative motifs under prompt-based reasoning
  • Semantic incoherence in contrastive summarization or multi-perspective generation

By analyzing latent circuit evolution during thematic decomposition, we detect when the model fails to preserve:

  • Representational continuity across similar inputs
  • Compression fidelity when summarizing multi-theme documents
  • Causal semantic consistency across subquestions (e.g., QID-level analysis)

Such analysis can inform corrective strategies like:

  • Injecting latent steering gradients for motif alignment
  • Using MDL-based motif discovery to detect high-likelihood fault transitions
  • Leveraging joint compression insights between X = corpus and Y = theme summary to extract mutual structure (Sutskever’s principle)

Discontinuities as Hallmarks of Meaning-Making Failure

The following are observed as robust indicators of latent structural failure in LLM reasoning:

  1. Steering vector unreliability: Failure to consistently adjust output distribution in target directions.
  2. Joint compression failure: Sudden divergence between latent summaries and their originating corpus segments.
  3. Directional disagreement in latent space: Mismatch between motif activation vectors across prompt formats.
  4. Semantic attractor collapse: When multiple plausible latent interpretations are collapsed into a degenerate summary.
  5. Circuit rewiring: Reversal or dissociation of attention roles in long-sequence contexts (Circuit Tracer analog).
  6. Reality signal confusion: Inability to distinguish hallucinated summaries from factual attribution (Dijkstra et al.).
  7. Gradient-collapse under fine-tuning: Over-specialization of latent channels in RLHF training loops.
  8. Bifurcation in activation space: Emergence of incompatible vector fields for the same motif under prompt shifts.
  9. Latent leakage paths: Information encoded in non-salient neurons becomes invertible across unrelated prompts (Jha et al.).
  10. Conceptual compression distortion: Loss of thematic diversity due to excessive KL minimization (Shani et al.).

Rationale and Theoretical Underpinning

  • Sutskever’s compression-as-prediction model supports the notion that shared structure between unsupervised text (X) and task-target (Y) creates meaningful joint compressors. Failures emerge when this structure is misaligned.
  • Braun et al. show how steering success depends on the latent separability and directionality of activation vectors—making these directly measurable failure surfaces in latent space.
  • Shani et al. demonstrate how human-aligned concept structures are more semantically diffuse, while LLMs over-compress, trading nuance for regularity.
  • Walch and Hodge draw from algebraic topology to explain that geometric invariants (e.g., torsion, harmonic forms) stabilize meaning—but fail under latent folding, as seen during curriculum overfit or instruction collapse.
  • SASR and AlphaEvolve reinforce that training stage transitions are non-linear transformations of latent geometry, often creating irreversible distortions.
  • Jha et al. and vec2vec reveal how universal alignment across embedding spaces can be exploited to detect or project failure modes across models—informing fault diagnosis and repair.