practice — discretized parameter axes: named levels, never a number (owner-approved 2026-09-29)

Contracts: LadderRung 0-4, CueDistance 0-2, Presentation theory|
application; PracticeParams carries the three named levels. Doc
comments hold the three definition tables verbatim (rung names +
definitions from the ladder doctrine; cue levels; presentation
levels).

Core: LADDER_RUNGS/CUE_LEVELS/PRESENTATIONS tables in src/core/
practice.ts (definitions verbatim) — ONE source spoken by the agents'
prompts, the dialog's stepped controls, and the exam header; core
barrel re-exports practice.ts (pure domain, browser-safe).

Agents: Part I's ladder intro now 'five rungs, numbered; the request
arrives as a rung' with bullets numbered 0-4 (definitions untouched);
Part III's parameters block replaced with the named-level doctrine
(rung = exact class, no continuum; cue counts lesson examples AND
previous problems' situations; presentation dresses, never changes);
partitioner's RESPECT THE PARAMETERS replaced (low rungs = many small
assignments, high = larger; far cue makes confusables-together MORE
important; presentation does not shape the partition). renderParams
outputs the exact named-level block. Anchor/0..1 language swept dead.

Service + store: strict union validation — floats between levels,
out-of-range, wrong strings, legacy dial shapes all 400 at the API
and fail isProblemSet (legacy sets skipped from lists; no migration —
data/practice is disposable scratch, owner wipes before testing).

UI: dialog = 5-detent ladder slider + 3-detent cue slider (each shows
the level NAME + one-line definition) + Theory|Application segmented
toggle; defaults rung 2 / cue 1 / theory; Create posts the new shape.
Exam header gains the levels line ('Direct exercise · Adjacent
context · Theory').

Tests: renderParams exact-block pin; strict-validation pins (new
tests/practice-service.test.ts — bad unions 400, legacy shape 400);
store pins (legacy floats + 2.5 rung fail); render pins (detents +
names + definitions, defaults, detent-swap, toggle active segment,
levels line); every params fixture moved off floats. 1138/1138, tsc
0, lint 0, build 0.

Docs: architecture.md — discretization paragraph (compact tables,
named-level invocation, defaults, one-table source), UI mentions;
README axes sentence.

docs/architecture.md

@@ -374,13 +374,15 @@ Practice turns the graph into problem sets the learner reasons through out loud.
 
 **Self-contained sets.** A `ProblemSet` freezes its world at creation: the selected node ids, a **propositions snapshot** (`Record<nodeId, {name, statement}>`), and the derived chains all persist into the set (`data/practice/*.json`, one file per set, `PracticeStore` port). Generation, grading, coverage, and the UI read ONLY the set — later extractions can never shift the ground under a live or historical set.
 
-**Two-stage generation.** One **partitioner** agent reads the chains plus the three creator-set dials (`searchSolutionRatio`, `cueDistance`, `presentation` — each 0..1, shown as anchor text in the UI, never raw numbers) and submits the true partition; each partition becomes one problem's target. Then **one generator agent per problem**: its prompt carries the problem-engineering framework verbatim (the owner's doctrine — the three distances, the search-to-solution axis, the rung ladder, reverse/discriminate/probe-the-premises, the standing prohibitions), **YOUR TARGET** (the assigned chains echoed verbatim — the echo law: the submit's chain set must equal the assignment exactly), and **THE FULL SET** (the whole selection: outside propositions are supporting material the generator may draw on with minimal use and maximal focus; the never-announce law covers ALL propositions). The submit is commit-before-pose — statement, solution, and rubric arrive together (the same sycophancy mitigation the quiz/explain mechanic uses); a problem never exists without its pre-written answer. Generators run in parallel (a process-wide 3-permit semaphore shared with grading) with per-problem failure isolation: one bad problem errors alone; the set still reaches `ready`. **Dedup across sets**: each generator also sees the problems of the ≤5 most recent sets sharing a node, so situations don't repeat between sets; within a set, parallel generators can't see each other — accepted for V1 (owner ruling).
+**The three axes are discretized named levels** (owner, 2026-09-29 — never a number to interpolate; there is no continuum between levels): the **ladder rung** (the main axis — 0 Worked example, 1 Completion, 2 Direct exercise, 3 Composition, 4 Path-finding; the invocation names the rung and the generator builds exactly that CLASS of problem), the **cue distance** (0 Lesson context — the textbook/toy-example shape in the teaching's own framing; 1 Adjacent context — a genuinely new situation built from the teaching's own world; 2 Far context — the edge of the recognizable band, where the proposition must still genuinely apply and be findable by looking), and the **presentation** (Theory — the subject's own terms, or Application — a concrete worldly affair in which the proposition's structure appears in the wild; it dresses the situation, never changes the rung or the cue distance). The names + one-line definitions live in one place (`src/core/practice.ts`'s level tables) and are spoken by everything: the agents' prompts (`renderParams`), the creation dialog's stepped controls, the exam header's levels line. Creation defaults: rung 2 Direct exercise · cue 1 Adjacent context · Theory. The API validates the exact unions — a float between rungs is a 400; legacy float-params sets fail the store validator and are skipped (no migration — `data/practice` is disposable scratch).
+
+**Two-stage generation.** One **partitioner** agent reads the chains plus the three NAMED-LEVEL parameters and submits the true partition; each partition becomes one problem's target. Then **one generator agent per problem**: its prompt carries the problem-engineering framework verbatim (the owner's doctrine — the three distances, the search-to-solution axis, the rung ladder, reverse/discriminate/probe-the-premises, the standing prohibitions), **YOUR TARGET** (the assigned chains echoed verbatim — the echo law: the submit's chain set must equal the assignment exactly), and **THE FULL SET** (the whole selection: outside propositions are supporting material the generator may draw on with minimal use and maximal focus; the never-announce law covers ALL propositions). The submit is commit-before-pose — statement, solution, and rubric arrive together (the same sycophancy mitigation the quiz/explain mechanic uses); a problem never exists without its pre-written answer. Generators run in parallel (a process-wide 3-permit semaphore shared with grading) with per-problem failure isolation: one bad problem errors alone; the set still reaches `ready`. **Dedup across sets**: each generator also sees the problems of the ≤5 most recent sets sharing a node, so situations don't repeat between sets; within a set, parallel generators can't see each other — accepted for V1 (owner ruling).
 
 **Grading is feedback-only.** Submitting answers fires one **grader** job per answered problem: the grader sees the problem, the propositions its chains touch, the pre-written solution and rubric, and the learner's answer — never the full set (minimal, by ruling). It returns a verdict (`solid`/`partial`/`missed`) and feedback that names the failed recall and teaches the boundary. The verdict lands on the problem and the UI reveals it with the solution and rubric; the answer locks. Nothing reaches the graph, no level, no count, anywhere. A grader failure leaves the answer gradeless and re-submittable.
 
 **The jobs pattern — registry, not JobRecords.** Practice runs are NOT Workbench jobs: no `JobStore` records, no steer messages, nothing ever wakes the teacher — a practice run isn't chat-scoped (a set outlives its birth session and has no lesson to re-enter; its result is itself, persisted in its own store and read by its own page). What it shares with the Workbench is the observability surface: a `globalThis` run registry (the module-instance idiom — `__lhPracticeRuns`), one row per set-phase (`setId:generation`, `setId:grading`), feeding the activity dock (GraduationCap rows; abort is set-scoped and idempotent; a boot sweep marks orphaned `partitioning`/`generating` sets as `error "Interrupted (server restarted)"` — the job runner's crash-honesty at set grain). Store writes serialize behind a per-set mutex; the pipeline is the sole writer of `data/practice`.
 
-**API + UI.** Creation is GRAPH-DOMAIN, not chat-domain (owner ruling 2026-09-29: "problem sets always take in nodes — graph objects"): `POST /api/practice/sets` takes `{nodeIds, params, title?}` ONLY — no chatId, no clusterId; the UI resolves a picked cluster to its member node ids and passes the cluster's label as the title. `GET /api/graph/clusters` serves the picker: the birth-session partition itself (`birthSessionPartition`, same ordering — size desc, ties by id) — EVERY group regardless of size (owner ruling, same evening: MIN_CLUSTER_SIZE is a hull-drawing rule; practice availability must not inherit a visual threshold), each as `{key, kind: "chat"|"notes", label, nodeIds, nodeCount}`; a cluster whose naming record is gone keeps `label: null` — honest, never guessed. 202 `{id}` with generation fire-and-forget; 400 empty selection (empty intersection with live nodes — the vanished-nodes edge) / bad params; 503 model/key refusal. `GET /api/practice/sets` · `GET/DELETE /api/practice/sets/[id]` (delete aborts live runs first) · `POST /api/practice/sets/[id]/answers` (202; 409 when not ready). Surfaces: `/practice` (the list — status/counts rows, delete confirm, the new-set dialog with the three dials and the cluster picker — the ONLY creation entry for now; the graph-view cluster entry comes later with the free-form selection UI) and `/practice/[id]` (the exam page — numbered problems, markdown statements with math healing at the display seam, a textarea + focus-gated dictation bar per problem, one Submit per set, the post-grade reveal). Entry: the sidebar's Practice nav row.
+**API + UI.** Creation is GRAPH-DOMAIN, not chat-domain (owner ruling 2026-09-29: "problem sets always take in nodes — graph objects"): `POST /api/practice/sets` takes `{nodeIds, params, title?}` ONLY — no chatId, no clusterId; the UI resolves a picked cluster to its member node ids and passes the cluster's label as the title. `GET /api/graph/clusters` serves the picker: the birth-session partition itself (`birthSessionPartition`, same ordering — size desc, ties by id) — EVERY group regardless of size (owner ruling, same evening: MIN_CLUSTER_SIZE is a hull-drawing rule; practice availability must not inherit a visual threshold), each as `{key, kind: "chat"|"notes", label, nodeIds, nodeCount}`; a cluster whose naming record is gone keeps `label: null` — honest, never guessed. 202 `{id}` with generation fire-and-forget; 400 empty selection (empty intersection with live nodes — the vanished-nodes edge) / bad params; 503 model/key refusal. `GET /api/practice/sets` · `GET/DELETE /api/practice/sets/[id]` (delete aborts live runs first) · `POST /api/practice/sets/[id]/answers` (202; 409 when not ready). Surfaces: `/practice` (the list — status/counts rows, delete confirm, the new-set dialog with the named-level controls (a 5-detent ladder slider, a 3-detent cue slider, a Theory|Application toggle — each showing the level's name + definition) and the cluster picker — the ONLY creation entry for now; the graph-view cluster entry comes later with the free-form selection UI) and `/practice/[id]` (the exam page — a levels line in the header, numbered problems, markdown statements with math healing at the display seam, a textarea + focus-gated dictation bar per problem, one Submit per set, the post-grade reveal). Entry: the sidebar's Practice nav row.
 
 **Model wiring.** Three roles — `partitioner`, `problemGenerator`, `grader` — all pinned to the **strong** slot (quality-critical, one settings edit from cheap) and in `BACKGROUND_MODEL_ROLES` (per-role overrides). The stage agents are single-submit loops on the shared `runSubmitAgent` seam (`submit-agent.ts`, extracted from extraction for exactly this): a `validate` hook turns a bad submit into a tool-input error the model retries against, within `maxTurns 4`; exhaustion fails the stage honestly (the set says so, the chains re-run cleanly on a new set).