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.

README.md

@@ -156,7 +156,7 @@ data/                           chats/ · graph/ (six ledgers + positions/source
 
 **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; 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`. 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.
+**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). 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.