podman/docs/continual-learning/plan.md

Continual Learning Plan

Status: draft
Goal: prove PodMan learns from outcomes in the hackathon demo

Must-Have Demo Loop

1. Observe two engineers touching the same file.
2. Store the observation and git state in MongoDB.
3. Predict a collision.
4. Send a card or Hermes message.
5. Record accept or dismiss outcome.
6. Adapt team_model.
7. Show the learned graph edge or changed future behavior.

Build Order

R1: Make exact recall reliable

• Normalize file paths.
• Build stable memory signatures.
• Look up prior accepted and dismissed outcomes.
• Prefer exact recall over vector recall.

R2: Make outcomes update memory

• Accepted real collision creates or strengthens ownership.
• Accepted real collision creates learned_from.
• Dismissed outcome lowers confidence or suppresses route.

R3: Expose loop data to the graph

• Add optional loop snapshot.
• Add optional activity stream.
• Keep existing PodGraph fields stable.

R4: Show the observatory

• Render observe/store/predict/outcome/adapt.
• Show recent activity.
• Make selected-node detail explain why memory changed.

R5: Prepare a clean demo chain

• Ensure one collision -> intervention -> accepted outcome exists.
• Ensure repeated signature recalls prior memory.
• Verify graph shows learned ownership.

Nice-to-Have

• Atlas Vector Search over memory summaries.
• Confidence scoring per ownership edge.
• Per-file memory timeline.
• Strategy promotion tied to outcomes.

Cut

• Raw screenshot storage.
• Full autonomous training.
• Broad dashboard metrics.
• Multi-pod learning generalization.

Acceptance Criteria

• A judge can see what changed in memory.
• The second similar event behaves differently.
• Exact MongoDB records prove the loop.
• The graph remains legible with real data.