Teleodynamic AI self-maintaining learning systems

System model

Teleodynamic architecture for self-maintaining structure

A practical blueprint for two-timescale learning, endogenous resource state, semantic glyph interpretation, and audit traces that explain why structure changed.

Two coupled timescales.

The fast loop adapts weights, rankings, embeddings, and local hypotheses. The slow loop changes representational structure: it may split a glyph class, add an ontology relation, merge redundancy, retire an unstable path, or no-op.

Resource accounting is the mediator. R(t), uncertainty, complexity, review effort, retrieval cost, and governance risk become visible variables that decide whether a new distinction is worth maintaining.

Fast loop
parameter updates Slow loop
structure changes Resource
budget

Architecture layers.

Fast loop

Continuous inference, scoring, embedding updates, and local prediction on the current structure.

Resource manager

Maintains R(t), viability floor, action costs, maintenance burden, decay, and uncertainty reserve.

Slow loop

Evaluates split, merge, add, retire, and no-op candidates under local cost.

Constraint registry

Represents structures and dependencies as a graph of mutually maintained affordances.

Trace logger

Captures trigger, alternatives, R before/after, cost, expected gain, phase, and result.

Public explanation

Reports bounded interpretations, warnings, unresolved fields, and source-routed evidence.

Open the theoretical strategy for the five non-negotiable commitments and local objective.

The glyph semantics kernel.

The kernel owns visual decomposition, primitive graphs, embedding fusion, ontology validation, and diagnostic traces. The public output layer remains conservative: assigned Unicode characters and valid public sequences.

Input Unicode and SVG canonicalization Primitive extraction Multi-vector retrieval Ontology filter Canonical expression Public rendering

Four-layer glyph object.

Do not force glyph meaning into a single row or label. Store the public surface and internal semantic evidence as separate but linked layers.

01

Surface

Unicode sequence, SVG hash, rendered preview, font and render profile.

02

Structure

Path commands, primitives, relations, bounding boxes, symmetries, containment.

03

Embedding

Visual, structural, semantic-text, and ontology-projected vectors.

04

Canonical

Ontology-validated expression, confidence, warnings, and public-output eligibility.

{
  "glyphId": "TRANSFORM_MARK",
  "surface": { "unicode": "U+27E1", "normalization": "NFC" },
  "structure": { "primitives": ["diamond", "centered mark"] },
  "vectors": {
    "visual": "vector-ref",
    "structural": "vector-ref",
    "semantic": "vector-ref",
    "ontology": "vector-ref"
  },
  "publicOutputEligible": true,
  "status": "emerging"
}

Phase-lock is an operational metric, not a metaphysical claim.

A glyph is phase-locked only when it repeatedly converges on compatible ontology-validated meanings across contexts, renderings, model revisions, and human review.

Context stability

Does the same glyph settle into compatible meanings across surrounding phrases or symbol neighbors?

Model stability

Does interpretation survive version changes and embedding refreshes?

Human stability

Do target reviewers converge on the intended meaning above a stated threshold?