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