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podman/docs/continual-learning/policy.md
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2026-06-28 08:06:21 +00:00

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# Continual Learning Policy
Status: demo-backed / active
Scope: what PodMan may learn about a team
## Prime Rule
PodMan learns coordination patterns, not personal surveillance profiles.
## Allowed Memory
PodMan may store:
- File and symbol ownership.
- Active file overlap.
- Repeated collision signatures.
- Intervention history.
- Accepted and dismissed outcomes.
- Routing preferences by event type and severity.
- Summaries of decisions relevant to future coordination.
## Forbidden Memory
PodMan must not store:
- Raw screenshots.
- Screen recordings.
- Secrets or credentials.
- Full terminal logs.
- Personal performance judgments.
- Private content unrelated to the coding task.
## Evidence Policy
| Evidence | Can predict? | Can adapt memory? |
| --- | --- | --- |
| Vision only | Yes, low confidence | No |
| Git watcher | Yes | No, unless repeated |
| GitHub state | Yes | No, unless verified |
| Accepted real outcome | Yes | Yes |
| Dismissed outcome | Yes, for suppression | Yes, as negative signal |
| Verifier result | Yes | Yes |
## Intervention Policy
Use the least intrusive channel:
1. Watch quietly.
2. Card.
3. Hermes message.
4. Voice.
Voice is only for urgent, high-confidence, time-sensitive risks.
## Adaptation Policy
Allowed adaptations:
- Add learned ownership after accepted real outcome.
- Raise confidence for repeated accepted signatures.
- Lower confidence for dismissed signatures.
- Prefer the previously accepted intervention kind.
- Suppress repeated low-value warnings.
Disallowed adaptations:
- Broad threshold changes from one example.
- Treating vector similarity as proof.
- Hiding dismissals.
- Making interruption more aggressive without evidence.
## Retention Policy
Keep:
- Outcomes.
- Signatures.
- Team model memory.
- Strategy metrics.
Summarize or expire:
- Old observations.
- Low-confidence vision-only events.
- Detailed trace text.
Delete immediately:
- Secrets.
- Accidental raw sensitive captures.
## Demo Policy
Seeded data is acceptable only if the demo script is honest about it. Live
learning requires a live or staged outcome write that visibly updates the graph
or future decision.