wave 7: the prober reads its map — graph-context injection dies whole
The owner's zero-configuration ruling (2026-09-23): nothing configures the graph beyond chat models. The prior-knowledge injection — its budget dial, its runner, its marker part, its dock/dispatch entries — is removed across all bands; the prober now reads the learner's persistent knowledge map on demand through two gated tools instead: - list_sessions: every birth session, names + kinds + counts + dates (graphSessionIndex, Platform's core seam) - list_propositions(session): what was born there + the session's own latest understanding level + that evaluation's date (graphSessionPropositions) — names/levels/dating ONLY, no statement text (owner ruling: the map's organization is the prober's business, its prose is not) Registered via createProberTools when chat-handler passes graphStore (the graphToolsEnabled gate decided server-side, per-turn fresh); the prober doctrine gains the map-review nudge at session start. Also: runner's JobKind special-casing collapses (KIND_ROLE/KIND_RUNNERS are plain records again), semantic-clusters.ts dies with its test, the embedder dep's doc tells the wave-7 stub story, and ports.ts's readClusterLabels/readNamingRuns surface stays untouched for Platform's ledger-port interlude. 848/848 tests, typecheck/lint/build green.
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amosblomqvist
@@ -58,27 +58,8 @@ export interface ChatSettings {
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/** pi thinking level — must be in the chosen model's supported list
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* (getSupportedThinkingLevels; enforced by the settings route). */
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thinkingLevel?: ThinkingLevel;
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/** Graph context injection (vision-context-injection.md): the session's
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* token budget for the prior-knowledge block — the top semantically
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* closest propositions from the knowledge graph, injected once, at
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* session start, as a steer. Absent = the default (on); 0 = off. The
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* settings route enforces the range; the trigger (turn-service →
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* subagents/graph-context.ts) is one-shot, so a mid-session change
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* affects only sessions that haven't injected yet. */
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graphContextTokens?: number;
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}
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/** The injection budget's decided knobs (2026-09-14; scale revised
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* 2026-09-15, owner): default 1024 (a meaningful, modest slice of prior
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* knowledge), hard max 16384, 0 = off. The UI is a DISCRETE slider over
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* these stops — powers of four from 64 (the fine-grained low end was
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* theater: a 128-token budget is ~2 propositions, nobody wants that
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* dial); the server still accepts any integer in range (a stale client
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* can't brick settings), the stops only govern what the slider sends. */
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export const GRAPH_CONTEXT_TOKENS_DEFAULT = 1024;
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export const GRAPH_CONTEXT_TOKENS_MAX = 16384;
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/** The slider's positions, left to right: off, then powers of four. */
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export const GRAPH_CONTEXT_TOKENS_STOPS = [0, 64, 256, 1024, 4096, 16384] as const;
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// ─── Background-extraction usage (the money channel, extraction side) ────────
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@@ -202,53 +183,6 @@ export const HANDOFF_TEXT =
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"The probe is concluded — the learner assessment is recorded. " +
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"Author the session's plan map now (call plan_init) and present it, then begin teaching.";
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// ─── Graph context injection (vision-context-injection.md, 2026-09-14) ────────
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/** One selected prior-knowledge proposition: similarity-ranked, annotated
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* with the learner's CURRENT understanding level — the fold is MOST
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* RECENT extraction instance (levels are per-instance, tied to moments in
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* time; the latest evidence is the best estimate of current understanding).
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* null = the node has no observation event at all. */
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export interface GraphContextProposition {
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id: string;
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statement: string;
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level: UnderstandingLevel | null;
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}
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/** The data-graph-context part payload: what the UI renders (the counts)
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* AND the audit trail (the full block content — the transcript shows
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* exactly what was injected, and when). The model-facing text is derived
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* from this by graphContextText below — the adapter flattens the marker
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* part to it (the custom-message scheme: no authored human speech). */
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export interface GraphContextData {
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chatId: string;
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/** The session's budget at injection time (for the card's readout). */
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budget: number;
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/** Estimated tokens of the injected block — chars/4 heuristic. */
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tokens: number;
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/** The selected propositions, similarity-descending. */
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propositions: GraphContextProposition[];
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}
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/** The model-facing block text — single-sourced (the adapter, the tests,
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* and reality can't drift). The level word travels with each line: the
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* closing gloss tells the agent what to do with each. */
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export function graphContextText(data: GraphContextData): string {
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const n = data.propositions.length;
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if (n === 0) return "";
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const lines = data.propositions.map(
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(p) => `- ${p.level ? `[${p.level}] ` : ""}${p.statement}`,
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);
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return [
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`Prior-knowledge context — what this learner already knows (from their knowledge graph, ` +
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`${n} proposition${n === 1 ? "" : "s"}, ~${data.tokens} tokens):`,
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"",
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...lines,
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"",
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"Treat this as reliable prior knowledge: build on what is solid, firm up what is shaky, " +
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"and teach around anything resisted. Do not re-teach solid ground.",
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].join("\n");
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}
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export interface PlanBlockData {
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/** Only "full" survives (the 2026-09-11 pivot): the plan renders IN
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