MongoDB plan

2026-06-28 00:41:05 -07:00
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+# Continual Learning Spec
+
+Status: draft  
+Scope: how PodMan learns team memory from live work and outcomes  
+Owner: continual learning / Team memory
+
+## Purpose
+
+Continual learning is the product proof that PodMan gets more useful from use.
+It learns team-level coordination memory: ownership, repeated collisions,
+accepted interventions, dismissed noise, and preferred routing.
+
+The visible loop:
+
+```text
+observe -> store -> predict -> outcome -> adapt
+```
+
+## Source Collections
+
+### `engineer_states`
+
+Latest per-engineer state from vision and local git.
+
+Key fields:
+
+- `podId`
+- `name`
+- `currentFile`
+- `changedFiles`
+- `branch`
+- `confidence`
+- `visionUpdatedAt`
+- `gitUpdatedAt`
+- `updatedAt`
+
+### `observations`
+
+Structured perception events.
+
+Key fields:
+
+- `podId`
+- `engineerId`
+- `currentFile`
+- `symbol`
+- `activity`
+- `confidence`
+- `observedAt`
+
+### `collisions`
+
+Predicted risk events.
+
+Key fields:
+
+- `id`
+- `podId`
+- `file`
+- `symbol`
+- `engineers`
+- `severity`
+- `status`
+- `memorySignature`
+- `detectedAt`
+
+### `interventions`
+
+Actions PodMan sent or suggested.
+
+Key fields:
+
+- `id`
+- `podId`
+- `collisionId`
+- `kind`
+- `channel`
+- `message`
+- `suggestedAction`
+- `createdAt`
+
+### `outcomes`
+
+Human or verifier supervision.
+
+Key fields:
+
+- `id`
+- `podId`
+- `interventionId`
+- `collisionId`
+- `accepted`
+- `wasRealCollision`
+- `learnedOwner`
+- `recordedAt`
+
+### `team_model`
+
+Durable pod memory.
+
+Key fields:
+
+- `podId`
+- `graph`
+- `ownership`
+- `collisionSignatures`
+- `interventionPolicy`
+- `updatedAt`
+
+### `memory_vectors`
+
+Optional semantic recall. Exact recall comes first.
+
+Key fields:
+
+- `podId`
+- `sourceKind`
+- `sourceId`
+- `text`
+- `embedding`
+- `embeddingModel`
+- `tags`
+
+## Learning Rules
+
+### Observe
+
+Write structured evidence from vision, git, GitHub, and agent traces.
+
+### Store
+
+Persist source records and materialized summaries. Do not store raw screenshots
+or recordings.
+
+### Predict
+
+Create a collision when multiple engineers converge on the same normalized file
+or symbol and at least one signal shows active or unpushed work.
+
+### Outcome
+
+Record whether the intervention was accepted, dismissed, real, or false.
+
+### Adapt
+
+Only accepted real outcomes can create `learned_from` graph edges. Dismissals
+adapt suppression, routing, or confidence.
+
+## Exact Signature
+
+Use deterministic signatures:
+
+```text
+podId:eventType:normalizedFile:symbol:sortedEngineers
+```
+
+Rules:
+
+- Sort engineer names.
+- Normalize file paths.
+- Use `*` for missing symbol.
+- Never include timestamps.
+
+## UI-Facing Loop Snapshot
+
+The graph response may include:
+
+```text
+loop
+  activeStep
+  steps[]
+    key
+    label
+    value
+    detail
+    status
+```
+
+Step mapping:
+
+| Step | Source |
+| --- | --- |
+| Observe | recent observations and git updates |
+| Store | team model, graph records, memory vectors |
+| Predict | open collisions |
+| Outcome | accepted and dismissed outcomes |
+| Adapt | learned owners, learned edges, strategy changes |
+
+## Activity Stream
+
+The graph response may include:
+
+```text
+activity[]
+  id
+  at
+  kind
+  title
+  detail
+  nodeId
+  edgeId
+```
+
+Allowed `kind` values:
+
+```text
+editing, collision, intervention, outcome, learned, agent
+```
+
+## Acceptance Criteria
+
+- The system can show one accepted outcome changing future memory.
+- Exact recall works without vector search.
+- The Team memory graph can explain the learning loop.
+- Dismissals and false positives are retained.
+- The demo does not rely on raw screenshots or hidden state.
+