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digest-aware serialized extraction + chain simplification replace the spine stage

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Owner live-test verdict on the spine (2026-09-30): no perceptible gain —
the real problems (restatement nodes, shallow re-derivations, same
conclusion from many angles) are mint-time issues a post-hoc chain
minter cannot fix. This arc moves assembly INTO the extractor and adds
the deterministic dedupe the owner specified:

- Digest-aware extraction: each stretch's extractor prompt carries THE
  GRAPH SO FAR (its own source's merged propositions + chains, ids
  verbatim); build on existing ids instead of restating, lean on
  established conclusions as premises. Mixed-namespace chain endpoints
  (fresh p-ids OR existing n_-ids) validated at the submit seam.
- Serialized per source: a source's batches/chunks run strictly in
  order (each extract reads every earlier merge); cross-source runs
  stay parallel; source-wide abort stops the loop. The spine stage,
  role, phase, usage stage, cost row, and setup roster are gone.
- Core-ideas prompt: goal reframed as uncovering core ideas and showing
  how everything derives from the foundations; direction upward, prefer
  lean derivations, never re-derive what the graph already holds.
- Chain simplification (merger maintenance): simplifyChains drops
  provably redundant chains at every merge — prefer-fewest-premises +
  ancestry-cover ([A,B]⟹X with [X,C]⟹Y covers [A,B,C]⟹Y, transitive);
  same-key provenance lines drop together; rewriteLedgers under the
  audit lock; droppedChains counted separately in stats.

Chunk budget stays 8192 (owner: keep for now, maybe lower later).
This commit is contained in:
amosblomqvist
2026-09-30 17:44:37 +03:00
parent 52c02533d1
commit 91341a93da
18 changed files with 734 additions and 879 deletions
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README.md
+4 -4
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@@ -68,7 +68,7 @@ flowchart TB
end
subgraph LIBRARY["LIBRARY — world model (owns data/graph ledgers)"]
EXTRACT["extraction pipeline:<br/>extractor → spine → auditor → merger<br/>(the SOLE graph writer)"]
EXTRACT["extraction pipeline:<br/>extractor → auditor → merger<br/>(the SOLE graph writer, serial per source)"]
DERIVE["derivation — graphSnapshot · birth-session clusters<br/>(pure reads)"]
LEDG[("append-only ledgers<br/>data/graph/*.jsonl")]
end
@@ -93,7 +93,7 @@ flowchart TB
HOST -.->|"hosts the server side"| TURN
```
**Core invariant:** the extraction pipeline (extractor → spine → auditor → merger) is the sole graph writer — tree-blind and post-hoc, triggered by token coverage, not quiz counts. The LLM stages propose, verify, and judge identity (link-or-create against the current graph; each finding carries verbatim evidence and message-granular provenance); the deterministic merger owns every ledger append (ids, provenance, chain dedupe — the same argument re-asserted merges under one `arg_` id). Every node is an objective, world-facing fact; the learner's relationship to it lives entirely in practice records — feedback-only, never written back. The teacher stays a teacher; everything below the ledgers is derived.
**Core invariant:** the extraction pipeline (extractor → auditor → merger) is the sole graph writer — tree-blind and post-hoc, triggered by token coverage, not quiz counts. The LLM stages propose, verify, and judge identity (link-or-create against the current graph; each finding carries verbatim evidence and message-granular provenance); the deterministic merger owns every ledger append (ids, provenance, chain dedupe — the same argument re-asserted merges under one `arg_` id). Every node is an objective, world-facing fact; the learner's relationship to it lives entirely in practice records — feedback-only, never written back. The teacher stays a teacher; everything below the ledgers is derived.
## Layout
@@ -114,7 +114,7 @@ src/
tools/web.ts web_search (Google CSE) + web_fetch (researcher only)
subagents/ researcher · demo-builder · visual-maker
· pdf-reader,
extraction pipeline (extractor/spine/auditor + merger handoff),
extraction pipeline (extractor/auditor + merger handoff),
runner (async protocol), headless Chrome helper,
abort registry
server/chat-handler.ts THE turn composition root (liveness · steers · persist · stream)
@@ -154,7 +154,7 @@ data/ chats/ · graph/ (nodes/chains/sessions/coverage
**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 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 stored as chains — `chains.jsonl` is the truth, edges a derived view; owner redesign + chains-primary, 2026-09-30) → spine (LLM, strong tier: sees the same-source digest + the fresh stretch, never the source; mints only what spans them — cross-stretch chains + connective propositions, span law enforced at the submit seam; owner stage, 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 chains — identical arguments merge under one `arg_` id — 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 8192 since the spine stage, 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 (`deriveView` rebuilds the lossy image: each chain an implication node `⟹` with premise→implication→conclusion arrows, so two arguments for one conclusion stay two diamonds); 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 the source — the session's teacher prose or a notes chunk — plus its own source's digest (**THE GRAPH SO FAR**: propositions + chains already extracted from this source, ids verbatim — digest-aware redesign, 2026-09-30 evening: the extractor IS the assembler, referencing existing ids instead of restating, leaning on established conclusions as premises); fully trusted; its deliverable is core ideas + reasoning chains: premises that jointly entail a conclusion, submitted as chains and stored as chains — `chains.jsonl` is the truth, edges a derived view; owner redesign + chains-primary, 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 chains — identical arguments merge under one `arg_` id; then the chain-simplification pass strips provably redundant derivations — prefer-fewest-premises + ancestry-cover — checkpoints coverage) → layout settle. A source's batches run strictly in order (each extractor continues the digest of every earlier merge). 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 8192, 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 (`deriveView` rebuilds the lossy image: each chain an implication node `⟹` with premise→implication→conclusion arrows, so two arguments for one conclusion stay two diamonds); 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).** A node selection resolves to the STORED reasoning chains it fully contains (a chain rides iff every member is selected; unchained nodes ride as singletons); 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.