extractor redesign: chains are the deliverable (owner-approved, goal-first)

README.md

@@ -154,7 +154,7 @@ data/                           chats/ · graph/ (six ledgers + positions/source
 
 **The async sub-agent protocol (ack-and-steer).** `research` / `demo` / `visual` are server-executed tools: the call resolves immediately mid-turn with a fire-and-forget ack and the teacher keeps teaching; a background runner works in parallel (the client polls `/api/activity` and `/api/jobs`). On completion the runner builds a **steer message** — a user-role `UIMessage` whose text part is the model-facing result and whose `data-subagent-artifact` part carries the full result for the client — and the **wake pipeline** delivers it: a per-chat serialized queue waits for the turn to end and no question to be pending, then fires a server-side teacher turn that weaves the result into the lesson. Delivery is exactly-once (deterministic steer ids, delivered-flag checks at every claim point); stalled jobs are recovered on read; aborted jobs deliver an abort steer. The client adopts the wake turn through the same `turnInFlight` machinery as returning mid-stream (busy shimmer, composer gate) and renders steers as artifacts — never a bubble. Sub-agents **verify, never one-shot**: the demo-builder headlessly executes its HTML in system Chrome before delivery; the visual-maker renders each SVG and looks at it (vision-capable model — the `vision` slot in settings).
 
-**The knowledge graph (two channels, one writer).** The write channel: the extraction pipeline — extractor (LLM: sees only the session, rendered as readable markdown by the shared `core/session-markdown.ts` engine — propositions as true statements, `follows`/`supports` edges with verbatim evidence; fully trusted, graph-blind) → auditor (LLM, an agentic loop that never sees the source: explores the existing graph through `find` / `list_sessions` / `list_propositions` tools, stamps each proposition with an identity verdict — link to an existing node (equivalence bias: paraphrases merge) or create — and may draw bridging edges between existing nodes and the new batch) → merger (`mergeFindings`, deterministic: applies the verdicts, link-appends phrasings/provenance, resolves and dedupes edges, checkpoints coverage) → layout settle. No quiz outcomes or understanding assessments live in the graph (2026-09-27 cleanup); practice (`/practice`) consumes the graph as feedback-only problem sets and writes nothing back. Triggered when a chat's **uncovered tokens** (message-view fingerprints, `coverage.jsonl`) cross `graph.extractionChunkTokens` (a settings knob, default 4096, floor 500 — read fresh per turn); failure checkpoints nothing, the next trigger re-runs. The same pipeline serves the **notes front door** (`POST /api/graph/import`: any `*.md`/`*.txt` becomes a source; re-imports are idempotent by path hash). The read channel: `GET /api/graph` derives the snapshot from the ledgers — a pure read; the home view is a hand-rolled Canvas 2D renderer (one rAF loop, dirty-flag idle, d3-zoom camera, zoom-gated labels, cluster hulls) over a d3-force layout, with server-persisted render settings and drag-committed positions. Clusters are **birth-session groupings**: a node joins the cluster of the session that birthed it (a chat, or an imported notes file as a file-kind session), derived at read — no embedding pass, no naming pass. The teacher reaches the graph on its own terms through the gated prober tools (`list_sessions` / `list_propositions`, the `agent.graphTools` setting) — there is no automatic context injection.
+**The knowledge graph (two channels, one writer).** The write channel: the extraction pipeline — extractor (LLM: sees only the source — the session's teacher prose or a notes chunk — fully trusted, graph-blind; its deliverable is propositions + reasoning chains: premises that jointly entail a conclusion, submitted as chains and expanded to `follows`-edges at the seam, owner redesign 2026-09-30) → auditor (LLM, an agentic loop that never sees the source: explores the existing graph through `find` / `list_sessions` / `list_propositions` tools and stamps each proposition with an identity verdict — link to an existing node (equivalence bias: paraphrases merge) or create) → merger (`mergeFindings`, deterministic: applies the verdicts, link-appends phrasings/provenance, resolves and dedupes edges, checkpoints coverage) → layout settle. No quiz outcomes or understanding assessments live in the graph (2026-09-27 cleanup); practice (`/practice`) consumes the graph as feedback-only problem sets and writes nothing back. Triggered when a chat's **uncovered tokens** (message-view fingerprints, `coverage.jsonl`) cross `graph.extractionChunkTokens` (a settings knob, default 4096, floor 500 — read fresh per turn); failure checkpoints nothing, the next trigger re-runs. The same pipeline serves the **notes front door** (`POST /api/graph/import`: any `*.md`/`*.txt` becomes a source; re-imports are idempotent by path hash). The read channel: `GET /api/graph` derives the snapshot from the ledgers — a pure read; the home view is a hand-rolled Canvas 2D renderer (one rAF loop, dirty-flag idle, d3-zoom camera, zoom-gated labels, cluster hulls) over a d3-force layout, with server-persisted render settings and drag-committed positions. Clusters are **birth-session groupings**: a node joins the cluster of the session that birthed it (a chat, or an imported notes file as a file-kind session), derived at read — no embedding pass, no naming pass. The teacher reaches the graph on its own terms through the gated prober tools (`list_sessions` / `list_propositions`, the `agent.graphTools` setting) — there is no automatic context injection.
 
 **Practice (problem sets).** The graph's `follows`-edges become reasoning chains; the `/practice` creation dialog picks a birth-session cluster (the graph view's own groups) and freezes its propositions + chains into a self-contained `ProblemSet`. Three named-level axes steer generation (ladder rung 0–4, cue distance 0–2, Theory|Application — never a number to interpolate; defaults Direct exercise · Adjacent context · Theory), plus an optional free-form note the agents weigh but need not follow. Creation has two entries: the cluster picker on `/practice`, or the graph view's chain table — a full-bleed sortable table of every reasoning chain, grouped by birth cluster under the shared top bar, selecting chips, chains, or whole clusters, whose floating selection bar shows the exact `N nodes → M chains` the agents will receive — handing a free-form node selection to the same dialog — and carries the fenced Delete action, making it the graph's management view (whole-cluster cleanup through the same full-death confirm dialog). A partitioner agent covers every chain exactly once, one generator per partition writes problems against the owner's problem-engineering framework (commit-before-pose: statement + solution + rubric together), and grading is **strengthening, not assessment** — one attempt per problem with its own submit button, instant solution/rubric reveal, then a score 0–10 with brief reasoning landing live; nothing ever written back to the graph. Runs are registry-backed background jobs on the activity dock (no steer, never wakes the teacher); sets live in `data/practice`, crash-swept on boot.