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Teleodynamic AI resource-bounded learning research
Implementation handoff Static claim status from the public registry. Human review is required before claim widening.

Six phases

Teleodynamic AI Roadmap

A prototype is not teleodynamic until split, merge, add, retire, and no-op decisions are resource-gated, locally justified, appended to immutable logs, and visible in phase plots.

Start Here Evaluation Lab

Six-phase roadmap

Build smallest first. Do not begin with the biggest model.

Roadmap phases
PhaseMechanismEvidence / warning
Phase 0: Minimal SubstrateFixed structure, fast loop only.Evidence: confusion clusters and uncertainty spikes. Warning: do not claim teleodynamic structure yet.
Phase 1: Resource EconomySynthetic gain, decay, viability floors, action costs, blocked-action telemetry.Actions block when resource is low and re-enable after predictive success.
Phase 2: Slow Loop IntegrationSplit and no-op operators.No-op wins when no affordable split improves cost.
Phase 3: Operator ExpansionMerge, retire, and one domain-specific operator.Complexity plateaus after utility drops.
Phase 4: Phase DetectionError-complexity trajectories, action rates, resource utilization, no-op dominance.Prune candidates can be explained by dependency and utility traces.
Phase 5: AuditabilityExport slow-loop traces with triggers, candidates, resource deltas, expected gains, and final justifications.A third party can reconstruct why a representation was added or retired.

Definition of done

The roadmap is strict because vague “adaptive” claims are not enough.

Prototype threshold

A prototype is not teleodynamic until split, merge, add, retire, and no-op decisions are resource-gated, locally justified, appended to immutable logs, and visible in phase plots.

Deep route polish

Build-roadmap narrative

The roadmap visual turns the research idea into staged public milestones with do-not-claim limits at each phase.

Written narrative

Roadmap pages are valuable when they separate what exists from what is intended. Each phase should list artifacts, evidence, failure modes, and claim limits before the next phase is allowed.

Concrete example

A phase can introduce a trace logger, but it should not claim broad agency until evaluation, review, and resource closure evidence exist.

Build-roadmap narrative comparison notes
FocusWhat to inspect
Phase artifact What should exist after the phase.
Acceptance evidence What should be tested or inspected.
Claim limit What must not be said yet.

Evidence note

Treat future roadmap items as planned work unless the archive and changelog show delivery.

Open operators Open lab

Internal reading path

/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. /architecture/ Teleodynamic AI Architecture for Self-Maintaining Systems Teleodynamic AI architecture for resource-bounded learning, semantic glyph interpretation, two-loop control, and self-maintaining AI systems. /archive/ Teleodynamic AI Research Archive Reviewed inventory of Teleodynamic website packages, source notes, release evidence, claim disposition, and public-safe archive boundaries.
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 Operator Library for Self-Maintaining AI Systems A practical operator library for Teleodynamic AI slow-loop structural edits and auditable representation growth. Next Evaluation Lab for Interpretable Systems Metrics, gates, plots, human review worksheets, and audit tests for Teleodynamic AI and semantic glyph interpretation.

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. /operator-library/ Operator Library for Self-Maintaining AI Systems A practical operator library for Teleodynamic AI slow-loop structural edits and auditable representation growth. /evaluation-lab/ Evaluation Lab for Interpretable Systems Metrics, gates, plots, human review worksheets, and audit tests for Teleodynamic AI and semantic glyph interpretation.