🧠 CIv7-ECA: Symbolic Substrate as Causal Compression Memory

Version: CIv7-ECA v1.0

Hypothesis: Intelligence involves a symbolic substrate that encodes the causal skeleton of observed dynamics through discrete compression. This substrate operates as a semantic memory layer, using minimal symbolic rules (e.g. ECA) to detect, store, and reason over the structural patterns embedded in sequential data. Intelligence emerges through the ability to detect and adapt to compression failure points—the discontinuities in symbolic causal structure.

🔬 Mechanism:

  • The symbolic substrate is implemented using Elementary Cellular Automata (ECA) or similar discrete systems that generate symbolic dynamics from minimal rulesets.
  • These dynamics form motif sequences or topological patterns representing the causal structure within temporal or sequential data.
  • Using Block Decomposition Method (BDM) and Minimum Description Length (MDL) criteria, the system continuously compresses symbolic streams, detecting where compression efficiency breaks down as a sign of causal or structural shift.
  • Each compression failure segment implies a symbolic fault surface, mapping to discontinuities in the generative process—what might be seen as a “regime shift” or causal reconfiguration in more classical terms.

🧩 Role of the Symbolic Substrate:

  • The symbolic substrate acts as a low-complexity interpreter of system history—a memory that stores compressible rules and exposes structural motifs.
  • By transforming input sequences into symbolic rule-based descriptions (via ECA transitions), the system can build an interpretable model of causality and detect structural instability over time.
  • This substrate is ideal for enumerative generalization, where every shift in the motif topology represents a hypothesis about the underlying process.

🧠 Intelligence, in this view, is:

The ability to compress symbolic sequences into minimal causal rules, detect where such compression fails, and adapt to or segment the experience accordingly.

🧱 Supporting Research:

  • Wolfram (2002): Demonstrated that ECA rules can capture a wide variety of complex behaviors through minimal programs, bridging the gap between computation and physics.
  • Zenil et al. (2015–2020): Developed the Block Decomposition Method (BDM) for approximating Kolmogorov complexity in empirical data—providing a method for fault detection via symbolic shifts.
  • Jürgen Schmidhuber (1997, 2007): Framed intelligence as the search for compressible regularities in experience; compression as cognition.
  • Crutchfield & Young (1994): Showed that computational mechanics can infer causal states from symbolic time series using ε-machines—highly related to ECA-based motifs.

🌀 Compression Failure = Intelligence Event

A symbolic fault is not noise—it is the event horizon of a deeper causal realignment. In the symbolic substrate, the intelligence system identifies:

  • Where motifs stop repeating
  • Where topologies shift in grammar-like space
  • Where the inferred causal rules can no longer explain new data

These events mark attention points for a larger cognitive system—triggering retuning, segmentation, or memory consolidation.

🧬 Notation Sketch (Illustrative):

Let:

  • S = {s₁, s₂, ..., sₙ} be a symbolic sequence derived from an encoder over raw input
  • ECA(r) = symbolic generator from Rule r in the 256 rule space
  • C(S) = BDM complexity of S
  • ΔC = C(S_t) - C(S_{t−1}) represents a compression shift

Then:

  • A structural fault is defined as |ΔC| > ε over a moving window
  • Motif boundaries: {b₁, b₂, ..., bₖ} where bᵢ marks zones of maximum ΔC
  • These boundaries encode a topological map of inferred causal segments