practice slice 6 — docs: architecture practice section, README feature line

docs/architecture.md: new §7 'Practice — strengthening, not assessment'
(purpose ruling + parked exam machinery; the chain bijection with
follows-edges, supports invisible, exactly-once partition; self-
contained sets frozen at creation; two-stage generation — partitioner,
one generator per problem with YOUR TARGET echo law + THE FULL SET
minimal-use doctrine, commit-before-pose, failure isolation, dedup
across sets; feedback-only grading; registry-not-JobRecords jobs
pattern and WHY — not chat-scoped, never wakes the teacher, boot
sweep; API + UI surfaces; three strong-pinned roles on the shared
runSubmitAgent seam). Sections 7-13 renumbered to 8-14; the §9
cross-ref in the deferred list updated. Cross-referenced enumerations
updated: §1 six-systems table + row, §2 layer count 25→28 routes,
§2.2 seven contract files + practice.ts, §3 PracticeStore port +
adapter + composition-root bullets + PORTSBOX, §4 six-systems heading
+ PRACTICE mermaid box + practice-request protocol, §6 practice-
territory sentence now points at the landed module, §10 observability
practice rows + abort kinds, §11 testing strata, §12 seam directory
row.

README.md: practice paragraph in Key mechanisms at the same grain
(feedback-only problem sets, partitioner/generator/grader, registry
jobs); the graph paragraph's 'assessment becomes practice-module
territory' forward-reference resolved to the landed module.

Docs-only — no code changes. 1129/1129, tsc 0, lint 0, build 0.

README.md

@@ -154,7 +154,9 @@ 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); assessment becomes practice-module territory. 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 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.
+
+**Practice (problem sets).** The graph's `follows`-edges become reasoning chains; a creation flow (sidebar → a session, or `/practice`) freezes the selection's propositions + chains into a self-contained `ProblemSet`. 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 grader per answer, verdict (`solid`/`partial`/`missed`) + feedback, post-grade reveal, 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.
 
 **Dictation.** Optional speech-to-text: the server mints short-lived Deepgram tokens; audio streams browser→Deepgram over WebSocket, never through the server.