🔁 CIv10-ECA Essential Hypothesis: Emergent Symbolic Substrate via Byte-Level Compression and Multiscale Semantic Alignment

🧠 Hypothesis Statement

Intelligence involves a symbolic substrate that emerges from hierarchical compression patterns within raw byte sequences. This substrate encodes causal skeletons of experience by identifying minimal, self-organizing motifs—formed not by predefined token units, but through dynamic split hierarchies learned from data. In CIv10, the symbolic substrate retains its autopoietic character, but now evolves from a byte-driven, multiscale attention architecture (e.g. AU-Net), enabling motif formation, refinement, and failure detection in a language-agnostic, token-free context.

Intelligence is the ability to extract and reorganize causal motifs from the unsegmented flow of data—where compression failure is a structural clue, not a semantic mistake.

🔬 Mechanism

  • The symbolic substrate is no longer built atop tokenized text, but emerges from a multiscale, autoregressive byte hierarchy (e.g., AU-Net).
  • At each layer of this hierarchy (e.g., byte → word → phrase), semantic boundaries are inferred through learned pooling/splitting functions—not hand-defined vocabularies.
  • Structural motifs are identified by monitoring compression shift signals (BDM, CTM, entropy gradients) and topological features (e.g., torsion collapse) across scales.
  • Motifs are updated when entropy flux, gradient discontinuities, or compression failure indicate a breakdown in prior causal structure.
  • The symbolic grammar is dynamically refactored as these hierarchical zones evolve, with curriculum guidance and internal selection pressures modeled after SEAL-style self-editing and entropy-regularized sampling.

🧩 Role of the Symbolic Substrate

  • Acts as a semantic memory structure formed from compression-coherent motifs across data scales (e.g., AU-Net 1–4 stage output layers).
  • Enables the system to reconstruct meaning from signal discontinuities—where no tokens or dictionaries are assumed.
  • Serves as the structure-recognition and hypothesis-space manager, identifying which compressive motifs explain the data—and where they fracture.
  • Can support symbolic rerouting and curriculum mutation based on subsymbolic tension zones, aligning with downstream mesoscopic feedback.

🌀 Intelligence Redefined

Intelligence is instantiated not in symbolic rules per se, but in the emergence, collapse, and self-restructuring of symbol-like motifs grounded in compressive alignment.

Where motifs fail, the system attends. Where structure compresses, the system learns.

And where symbolic segmentation can no longer explain compression dynamics, new boundaries emerge—defined not by human tokens, but by internal compressive geometry.

🧱 Supporting Research (Expanded)

Source Contribution
AU-Net (2025) Eliminates fixed token boundaries; enables learnable, multistage symbolic emergence from raw bytes
Zenil et al. (2015–2020) BDM complexity analysis for symbolic failure detection
Crutchfield & Young (1994) ε-machines as causal grammars — now applied to learned byte segments
Walch & Grosse (2024–25) Topological fault geometry as signal of semantic motif drift
SEAL (2024) Symbolic self-editing and curriculum evolution
From Bytes to Ideas (AU-Net) Emergence of symbolic layers from pooled attention without tokenization
Schmidhuber (1997) Compression as cognition — generalized to symbol-free emergence of motifs

🔬 Notation Sketch (Updated)

Let:

  • B = {b₁, …, bₙ} be a raw byte sequence
  • Hᵢ = AU-Net layer i, producing pooled representations at increasing semantic scope
  • Mᵢ = motif candidates from Hᵢ, aligned to split boundaries
  • C(Mᵢ) = compression cost of motif Mᵢ via BDM or CTM
  • ΔCᵢ = C(Mᵢ[t]) - C(Mᵢ[t−1])
  • T(Mᵢ) = torsion in motif boundary geometry

Then:

  • A symbolic fault occurs where: |ΔCᵢ| > ε or |T(Mᵢ[t]) - T(Mᵢ[t−1])| > δ

  • This defines a compression-aligned symbolic regime boundary, marking a shift in the internal structure of meaning without needing tokens.

🧬 CIv10-Specific Extensions

  • Symbolic structures now form across compression-aligned scales, not token-level boundaries.
  • Byte-to-Concept emergence is driven by latent pooling dynamics and entropy-aware segmentation.
  • Symbolic failures are now linked to substrate-level compression divergence, not token sparsity or vocabulary limits.
  • The symbolic memory becomes multiresolutional, matching the AU-Net expansion-contraction stack.
  • Enables cross-lingual, morphologically rich, and low-resource reasoning without retraining tokenizers.