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Teleodynamic AI resource-bounded learning research

Fast loop / slow loop

Two-Timescale Reference Architecture

Teleodynamic architecture separates continuous parametric adaptation from discrete structural evolution and gates structural change with local objective and internal resource state.

Resource Economy Operator Library

Architecture overview

The fast loop adapts the current temporary structure. The slow loop proposes split, merge, add, retire, or no-op only after evidence accumulates.

Fast Loop

Continuous parametric adaptation over fixed temporary structure. It may use natural-gradient style updates on a parameter manifold and cross-entropy loss. Structural action cost is zero during this loop because structure is not being mutated.

Slow Loop

Discrete structural evolution. It proposes mutations only after accumulated confusion, novelty, redundancy, or utility evidence. Actions are gated by local objective and resource state.

Math-friendly reference blocks

These formula blocks are accessible approximations for the architecture. Formula rendering should be manually verified against the cited paper before major public launch.

Inner dynamicsθ(t+1) = θ(t) − η · G(θ)⁻¹∇L(θ)

Parametric update under the current temporary structure.

Outer dynamicss(t+1) = A(s(t), candidate, R(t))

Structural step selected from split, merge, add, retire, or no-op.

Resource lawR(t+1)=R(t)+gain−decay−actionCost−maintenance

Viability budget increases with success and decreases with work.

Local objectiveJ = expectedLoss + λ·complexity + μ·maintenance + reviewCost

Slow-loop candidates are myopic and local, not a global NAS search.

Engineering boundary

Architecture diagrams on this site are explanatory patterns, not deployed proof.

No safety guarantee

The site does not claim current systems are proven safe for unmonitored production runtime. Structural edits still require evidence, audit traces, and human governance.

Deep route polish

Two-loop architecture narrative

The route visual anchors the fast-loop, slow-loop, resource-manager, registry, and trace-logger model as a system diagram rather than a loose metaphor.

Written narrative

Architecture is where the framework becomes operational. The fast loop adapts current scores and representations. The slow loop asks whether the structure itself deserves a change. The resource manager prevents every novelty from becoming permanent debt.

Concrete example

A glyph relation repeatedly causes unresolved outputs. The slow loop compares split, merge, add, retire, and no-op candidates, then records the selected action with cost, expected gain, and warnings.

Two-loop architecture narrative comparison notes
FocusWhat to inspect
Fast loop Updates current parameters and local predictions.
Slow loop Evaluates structural edits and no-op choices.
Trace logger Keeps the decision reconstructable for review.

Evidence note

The architecture is a public blueprint and educational implementation target. It is not a hosted runtime guarantee.

Explore operators Read integration path

Internal reading path

/resource-economy/ Resource-Bounded Learning and the R(t) Economy How Teleodynamic AI uses internal resource state, viability floors, action costs, maintenance burden, and no-op dominance. /work-constraint-cycle/ Work-Constraint Cycle for Self-Maintaining AI Systems A practical explanation of the work-constraint cycle behind Teleodynamic AI and adaptive structure under viability pressure. /evaluation-lab/ Evaluation Lab for Interpretable Systems Metrics, gates, plots, human review worksheets, and audit tests for Teleodynamic AI and semantic glyph interpretation.
Next Teleodynamic AI Strategy for Resource-Bounded Learning A build strategy for Teleodynamic AI using two-timescale adaptation, endogenous resource budgets, self-maintaining AI systems, and auditable structural change. Next Developer Integration Guide for Interpretable AI Architecture How to integrate Teleodynamic AI pages, glyph interpretation objects, evidence payloads, and Protocol5-style converter diagnostics. Next Resource-Bounded Learning and the R(t) Economy How Teleodynamic AI uses internal resource state, viability floors, action costs, maintenance burden, and no-op dominance.

Next step flow

Keep the review path visible.

Continue through related pages, then capture decisions as static evidence packets. This flow stays non-executing, review-gated, and bounded to public research language.

Open packet builder
/theoretical-strategy/ Teleodynamic AI Strategy for Resource-Bounded Learning A build strategy for Teleodynamic AI using two-timescale adaptation, endogenous resource budgets, self-maintaining AI systems, and auditable structural change. /developer-integration/ Developer Integration Guide for Interpretable AI Architecture How to integrate Teleodynamic AI pages, glyph interpretation objects, evidence payloads, and Protocol5-style converter diagnostics. /resource-economy/ Resource-Bounded Learning and the R(t) Economy How Teleodynamic AI uses internal resource state, viability floors, action costs, maintenance burden, and no-op dominance.