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