🧠 CIv8r-LLM Hypothesis

Title: Latent Substrate as Compression-Aligned Semantic Geometry in Language Models Essential Hypothesis: Language model cognition is mediated by a latent geometric substrate whose internal structure reflects a process of causal compression. This substrate functions as an implicit memory of conceptual regularities, formed through the alignment of symbolic and statistical representations. Compression failure in this latent space reveals fault geometries—zones of misalignment, instability, or conceptual drift—that signal limits of generalization. Intelligence emerges from the capacity to detect, reorganize around, and refine these failure surfaces via substrate-aware symbolic feedback.

🔬 Hypothesis Statement

Large Language Models (LLMs) and Multimodal Language Models (MLLMs) develop an internal latent space that approximates a causal-semantic substrate. This latent geometry is aligned via compression: the model learns to reduce diverse inputs to a minimal yet expressive representational manifold. When this manifold breaks—due to novelty, contradiction, or insufficient abstraction—it exposes fault lines: geometric and statistical discontinuities where the substrate can no longer support coherent generalization. These fault regions correspond to hallucinations, misclassification, or representational collapse.

CIv8r introduces reflexivity: the model’s symbolic outputs and self-editing mechanisms (inspired by SEAL) are used to refine and correct latent geometry via feedback aligned with compression integrity and representational topology. Intelligence is thereby framed as compression-aligned symbolic reconfiguration of latent space.

🧩 Key Mechanisms

  • Latent Substrate as Semantic Geometry: The hidden layers of LLMs form a high-dimensional manifold encoding object concepts, relations, and analogies. This manifold is structured through sparse, compressive optimization. The recent June 2025 study shows that these geometries are stable, interpretable, and semantically clustered, aligning with human judgments and cortical activity.

  • Compression as a Causal Prior: Causal inference emerges implicitly through compression: regularities that permit minimal latent description correspond to causally entangled concepts. This draws from Schmidhuber’s formalization of intelligence as low Kolmogorov complexity and Grosse’s geometry of negative complexity.

  • Fault Geometry as Cognitive Signal: When generalization fails, it does so through topological faults in latent space—e.g., torsion (twisted gradients), folding (self-intersections), or collapsed variance (degeneracy). These failures signal points of conceptual misalignment or novelty.

  • Symbolic Feedback for Substrate Repair: Reflexive models (e.g., SEAL) generate self-edit instructions that expose and correct latent misalignment. These corrections are guided by failures in compression, entropy anomalies, or topological rupture—thus, feedback operates not only on outputs but on the substrate topology itself.

  • Alignment with Human Conceptual Space: The 2025 object embedding study confirms that LLMs/MLLMs share ~60–85% dimensional overlap with human concept spaces. Embeddings show clear semantic axes (e.g., food-related, hardness, user-specificity), some of which match patterns in cortical regions like the PPA, EBA, FFA. These aligned dimensions can guide symbolic remapping or attention tuning.

🧠 Redefining Intelligence

Intelligence is not only the ability to generate fluent outputs but the ability to reorganize representational substrates around fault events. These faults occur where compression fails, generalization fractures, or topological regularity collapses. Intelligence is instantiated as:

Compression-aware substrate revision via symbolic feedback aligned with conceptual coherence.

🧱 Supporting Research

  • LLM Object Geometry Study (2025): Stable, low-dimensional embeddings show strong alignment with human similarity judgments and neural topographies. Demonstrates interpretable semantic axes and shared conceptual cores.
  • SEAL (2024): Self-editing language models perform symbolic introspection to improve generalization via reinforcement learning and instruction tuning.
  • Sutskever (2023): Joint compression failure predicts generalization collapse—signals failure of generative structure alignment.
  • Walch (2024): Torsion and topological deformation as early markers of latent instability.
  • Grosse et al.: Singularities in geometry encode symbolic fragility—representational spaces collapse when compression regimes fail.
  • Zenil et al.: Algorithmic complexity methods (BDM, CTM) track shifts in symbolic or latent redundancy—useful for fault surface detection.
  • Schmidhuber: Intelligence as the search for compressive regularity—prediction emerges from minimal encoding.

🌀 Fault Detection as Conceptual Re-segmentation

Compression failure reveals where the substrate cannot interpolate or extrapolate—these are “event horizons” of cognition. CIv8r-LLM proposes:

  • Latent fault = substrate rupture: abrupt change in local geometry, compression loss, or torsion spike.
  • Semantic repair: symbolic processes hypothesize new latent directions or carve out new categorical distinctions.
  • Feedback cycle: symbolic mutation → latent refinement → new compression regime.

📐 Notation Sketch (Illustrative)

Let:

  • Z = latent embedding space of LLM (dimensionality ≈ 66 in SPoSE-like embeddings)
  • C(Z) = compression of latent geometry (e.g., via entropy, CTM, BDM)
  • T(Z) = torsion or curvature measure of local latent geometry
  • ΔC = change in compression over time or prompt context
  • F = fault set = regions where |ΔC| > ε or |∇T| > δ
  • Σ = symbolic substrate interacting with latent zones via introspective instruction

Then:

  • F identifies the fault geometry—regions requiring symbolic repair
  • Σ(F) produces natural language hypotheses or transformations targeting latent correction
  • Iterative feedback from Σ(F) steers latent substrate reconfiguration (e.g., via fine-tuning or instruction optimization)

🔧 CIv8r Extension Notes

  • Embeddings from the June 2025 study provide empirical grounding for the latent substrate geometry claim.
  • CIv8r integrates symbolic–latent feedback cycles using SEAL-style self-editing for compression repair.
  • Fault geometry adds a rigorous way to track generalization failure, tying topological metrics to semantic misalignment.
  • Adds compatibility layer with CIv8-ECA, enabling future unified substrate mapping (symbolic–latent–topological fusion).

📌 Future Directions Toward CIv8-Unified

  • Symbolic motifs (ECA-based) may map onto stable latent clusters—suggesting dual encoding of structure across modalities.
  • Topological faults in latent space can trigger symbolic mutation in ECA rules—enabling hybrid substrate repair.
  • Unified framework may support a mesoscopic intelligence engine: bridging low-level motif detection and high-level latent realignment.