Case 01
Machine Governor P0-RL
Current programme result, 24 August 2026
| Mechanisms mapped | 35 |
|---|---|
| Current firing/output | 21 |
| L2 loops | 0 |
| L3 loops | 0 |
| L4 loops | 0 |
| Formal closure | Held |
The problem
SAGE contained many components described as learning, recursive, adaptive, replay-based, experimental, or self-improving. The load-bearing question was not whether those components existed. It was whether a verified result from an external worker changed how the next worker was treated.
The method
The P0-RL pass classified parameter learning, meta-learning, structural learning, closure-derived learning, experimental learning, and repair/policy learning. It then traced whether each mechanism merely recorded an event, changed internal SAGE behavior, changed a later worker invocation, produced independently verified improvement, or transferred retained learning across providers.
L0 record, classify, queue, or preview
L1 result is consumed by an internal SAGE mechanism
L2 retained result materially changes a later worker invocation
L3 the change improves an independently verified held-out result
L4 the retained improvement transfers across workers/providersThe finding
SAGE already has a substantial active internal learning ecology. Hebbian Paths A and C, SAGERecursive._meta_update, SSM, DDSS modulation, MML, Discovery, G1, shadow learning, KAS replay, repair logic, SCAL, ProofKit, and related processes had current evidence or current output.
But the programme found no current mechanism that learns how to treat the next external worker differently. Internal learning was real; worker-steering learning was absent.
The engineering decision
The correct reuse decision is to compose Track-2, G1, and KAS with one deterministic host-owned steering consumer. No new learning database or general learning engine is required.
Rejected architectural shortcuts
- Do not connect worker closure directly into Hebbian learning.
- Do not use SSM or DDSS parameter modulation as worker-task steering memory.
- Do not use MML or Discovery as a substitute for tool, context, budget, and verification policy.
- Do not call the replay queue worker steering merely because it stores replayable events.
- Do not put another AI model in charge of the first steering consumer.
What this demonstrates
Complex-system re-derivation, semantic discrimination between different kinds of learning, multi-worker programme direction, architecture reduction, and the ability to preserve a zero result instead of converting activity into an improvement claim.