canon: Discernment Transfer Ladder method + rulebook-transfer evidence (COO probe)

canon/methods/discernment-transfer-ladder.md

@@ -0,0 +1,79 @@
+---
+uri: klappy://canon/methods/discernment-transfer-ladder
+title: "Method: The Discernment Transfer Ladder — Empirical Rulebook Transfer Across Adjacent Tiers"
+audience: canon
+exposure: nav
+tier: 2
+voice: neutral
+stability: provisional
+target_repo: "outcomes-driven-development"
+date: 2026-06-30
+derives_from: "canon/principles/rulebook-transfer, canon/principles/discernment-layer, canon/values/axioms, canon/constraints/measure-before-you-object, canon/methods/revision-lens-sequence, canon/principles/verification-requires-fresh-context"
+status: proposed
+---
+
+# Method: The Discernment Transfer Ladder
+
+> Discernment a frontier model performs once can be written down as a rulebook a lesser model runs — `rulebook-transfer` states the principle. This method is the empirical procedure that *derives* that rulebook from real evidence, and the observation that the same procedure runs at every adjacent rung of a ladder whose top is a human and whose floor is reality itself.
+
+> This method **operationalizes** `klappy://canon/principles/rulebook-transfer` (Tier-1 principle, E0010). It does not restate the theory — it is the empirical *procedure* that derives and validates the rulebooks the principle describes. All theory (articulability, step-size, the execution/stewardship split, the authority asymmetry) defers to that principle.
+
+## Summary
+
+A task that a higher tier performs by judgment can be handed to a lower tier — but only by writing the judgment down, only between **adjacent** tiers, and only for the part of the judgment that is writable at all. The transfer is not assumed; it is **measured and compensated** in a loop. The same loop runs at every rung: human → SOTA model → mid model → small model. Each rung treats the tier above as its reference and the tier below as its student. The ladder bottoms out not at the human but at **reality** — the only sovereign reference (Axiom 1). The output is a per-task **tier-assignment map**: the cheapest tier that clears the fidelity bar for each task, and the rulebook that gets it there.
+
+## The recursion, who authors, and the bottleneck
+
+The theory — n-tier recursion, why hops have a maximum step size, the split between **execution capacity** (run a rulebook from above; extends far down) and **stewardship capacity** (author a rulebook *for the tier below*; scarcer, fewer rungs) — lives in `rulebook-transfer` and is not restated here. Canon names the dominant unresolved question: **star or chain** — does authoring concentrate at the frontier (one steward writes for everyone) or re-instantiate at each tier (each authors for the one below)?
+
+This method answers it not as a *capability* question but as a *constraint* one, per the operating contract's third pillar (operator attention is the bottleneck). The scarcest resource is not model capability — it is the human's stewardship time. So the topology is decided by **availability × proven stewardship**, not tier-distance:
+
+- **The human authors only for the frontier.** Stewardship-capable but maximally scarce, the human spends their authoring time only where it is irreplaceable: writing the rulebooks the top models run (Fable, Opus). Human time cannot reach lower and should not try.
+- **The frontier authors for everyone below.** Fable and Opus have *observed* stewardship capacity and *abundant* availability, so the authoring of lower-tier rulebooks (for Sonnet, Haiku) is delegated to them. Authoring labor flows to the highest tier that has both proven stewardship and spare time.
+- **Mid-tier stewardship is unproven and not depended on.** There is no evidence yet that Sonnet can author a working rulebook for Haiku. The topology does not assume it: if the frontier has the availability, the chain need not route authoring through the mid tier at all.
+
+The result is a **shallow star**: human → {frontier}, thin and bottleneck-rationed; {frontier} → {everything below}, abundant and delegated. This is canon's star/chain question answered by the constraint — *author as high as the bottleneck forces, and delegate authoring downward to the most-capable tier that has time.*
+
+Evidence so far (this session, honest): the v2 compensation rulebook was authored by the top tier and run directly by Haiku — supporting **frontier → bottom authoring** (the star arm). Whether a mid tier can author for the tier below it — the chain arm — was not tested. The COO project is itself an instance of the top arm: Wickman, Grove, Geary, and Gawande are human experts who wrote their discernment into **books** — frontier-grade rulebooks — and the model now runs them.
+
+## The loop at a single rung
+
+1. **Establish the reference and the answer key — and keep them separate.** Run the task on the higher tier to produce a reference output. Separately establish a held-out, ground-truth answer key checked against reality (or the closest available proxy). The higher tier's output seeds the rulebook's worked examples; it is **not** the answer key. Tuning the student to match the reference transfers the reference's *errors* too.
+2. **Measure the gap on two axes.** *Structural* — counts, tiering, budget adherence, edge integrity; cheap and scriptable. *Semantic fidelity* — is the content true to the source/reality; requires an independent grader with fresh context (`verification-requires-fresh-context`). Structural pass without fidelity pass is shape posing as substance.
+3. **Write the missing discernment down.** Diagnose the gap and compensate the rulebook with explicit, writable rules: hard budgets and caps, decision tests, anti-fragmentation rules, worked examples at the target grain, and self-count gates the student runs before returning.
+4. **Re-run the student AND regression-run the reference.** Confirm the student converged — and that the new constraints did not lower the higher tier's ceiling. A compensation that helps the weak tier can over-constrain the strong one; lift the floor without dropping the ceiling.
+5. **Loop over UNSEEN cases, and know when to stop.** Tune on case A, then run the *frozen* rulebook on an unseen case B (train/test split) — convergence on the unseen case is the only honest signal; tuning and testing on the same case is memorization. If after N rounds a capability still will not transfer to a tier, that capability is **not writable-down for that tier**: assign it permanently to a higher tier and stop spending on compensation.
+
+## Ground truth is tiered; reality is sovereign
+
+At each rung the reference is the tier-above's *approximation*, not Truth. The human is the closest rung to reality but is still approximating it. Therefore: validate against **reality** wherever it can be reached (does the checklist actually work in the field; did the distilled procedure produce the right action), and fall back to the tier-above only when reality is out of reach. This is Axiom 1 applied to the ladder — observe before asserting, at every rung.
+
+This is the same loop Gawande prescribes for checklists: *first drafts fall apart in the real world; test, study the failures, revise, re-test until it works.* The method we are encoding is checklist-design applied to the transfer of discernment itself — and the COO foldout that surfaced it contains the very row (`tra-`/`gaw-` test-and-refine) that states it. The method is self-evidencing.
+
+## Output: the tier-assignment map
+
+The loop does not crown one model. It produces a portfolio: for each task or lens, the cheapest tier that clears the fidelity bar, plus the rulebook that gets it there, plus the capabilities that refused to transfer and stay up-tier. Cost selection becomes a measured decision (`measure-before-you-object`), not a guess.
+
+## Evaluation transfers too — as bounded delegated stewardship
+
+Production is not the only thing that moves down the ladder. **Evaluation and ratification** — the act of judging an output, shaping it, and admitting it to a store — transfer by the same loop. But they transfer under a shape canon already names. Per `klappy://canon/decisions/models-do-not-mutate-canon`: *a model may hold delegated, bounded, revocable stewardship over a sub-scope — governing within it, never above it, never over itself.* The empirical loop is how that stewardship is **earned**: the steward's verdicts are measured against the tier above (and reality), its evaluation rulebook is compensated, and only once it converges on unseen cases is the authority granted — and it stays revocable.
+
+Three guardrails travel with the delegation, each already in canon:
+
+- **Within scope, never above it (domain bound).** A steward ratifies only artifacts inside the sub-scope it was granted. The COO agent may ratify COO-domain state; it does not ratify universal canon or another domain's. Higher-tier artifacts carry higher epistemic obligation (`klappy://canon/definitions/epistemic-obligation-and-document-tiers`), and the obligation rises faster than the delegation does.
+- **Never over itself (independence).** A steward cannot ratify its own production. The critic cannot be the resolver and the creator cannot be its own critic (`klappy://canon/constraints/critic-cannot-be-resolver`, `klappy://canon/principles/verification-requires-fresh-context`). Delegated evaluation runs in a separate context/instance from the one that produced the artifact.
+- **Proportional to stakes (calibration).** The ratification bar scales with the artifact's tier and reversibility (`klappy://odd/challenge/stakes-calibration`). Low-stakes, reversible, in-domain, high-frequency items are delegable to the tier whose eval rulebook clears the bar; high-stakes, irreversible, novel, or cross-domain items escalate up the ladder — ultimately to the human, who alone ratifies Tier-1 canon.
+
+There is therefore **no conflict** with "models do not mutate canon." That decision forbids governing *above* one's scope and *over* oneself; it explicitly permits delegated, bounded, in-domain stewardship. The human keeps two things no rung below inherits: ratification of the top tier (universal canon), and the authority to grant and revoke every steward's scope.
+
+This is not abstract for Covenynt. The COO's own design is this principle instantiated at the business layer: act-with-approval, solo-approve for low-stakes, 2-of-3 partners for high-stakes (`0 Context/about.md`). The agent is a bounded, revocable steward of the COO domain — ratifying the reversible in-domain work itself, escalating the rest. The ladder and the COO approval model are the same structure at two altitudes.
+
+## Worked example (the session that produced this method)
+
+Task: fold one chapter of a COO book into a Kirigami foldout. Reference: Opus, 41 rows / 11 tier-1. Students under rulebook v1: Sonnet 37/9 (converged — adjacent tier), Haiku 88/4 (collapsed — two tiers down, judgment did not transfer; *format* — JSON, edge syntax — did transfer). Compensation v2: budgets, tier-1 test, anti-fragmentation rule, worked example, self-count gate. Students under v2: Haiku 45/10, back in the reference band. Not yet done: the semantic-fidelity gate (Step 2 axis two) and the unseen-case test (Step 5). Predicted non-transferable rung: cross-book reconciliation (L6) stays on the top model tier.
+
+## Open
+
+- **Semantic fidelity unproven.** Convergence so far is structural only. The fidelity grader against a held-out answer key has not run.
+- **N is uncalibrated.** How many compensation rounds before declaring a capability non-transferable is not yet known.
+- **Reality-grounding for this domain.** For the COO, the ultimate answer key is whether the agent's actions are right in the field — which only the pilot (Brief 07 Step 5 / Brief 08 Phase 2) supplies.

canon/principles/rulebook-transfer.md

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 A further hazard sharpens both: an articulated rulebook can be fluent and still not describe what its author actually did. A confabulated rulebook reads well and reproduces badly. Fidelity is therefore never inferred from how good the rulebook reads; it is measured by testing that the lesser tier makes the same cuts — consistent with [Code Claims Require Code Observation](klappy://canon/principles/code-claims-require-code-observation): the claim is verified against observation, not against its own prose.
 
+### Second probe — COO canon, 2026-06-30 (real corpus, three tiers)
+
+A live cross-model run folded chapters from four COO books through one lens, graded by a fresh-context grader against held-out truth (`kirigami://docs/eval/coo-corpus-fidelity-run-2026-06-30`). It extends both evidence layers:
+
+**Execution transfer — strengthened.** Under a sufficiently explicit lens a Haiku-class model reached ship-grade fidelity (8/8 held-out questions on the hardest chapter). The *articulability step-size* mechanism was observed directly: under a thin lens Haiku collapsed (88 rows, 4 tier-1 — a starved skeleton); writing the missing discernment down (budgets, tier test, anti-fragmentation rule, worked example, self-count gate) moved the same model into the frontier band (45 rows, 10 tier-1) with no model change. Articulability, not raw capability, was the binding variable — exactly as the principle predicts.
+
+**Stewardship transfer — first data on the chain arm.** The prior evidence was N=1 at the top rung. This probe tested the rung below: a mid-tier model (Sonnet) *authored* a compensating rule for a Haiku failure, and it worked when Haiku ran it (the targeted hallucination and orphan edges disappeared) — but it **over-corrected**, dropping coverage. A frontier model (Opus), given the identical brief, authored the same fix **surgically** — failure removed, coverage preserved. Reading: stewardship *does* transfer below the top rung, but it is **lossy in the tacit dimension** — the mid tier fixes the articulable bug and fumbles the balance the frontier holds. This is a first, concrete data point on the **star-or-chain** question: frontier→bottom authoring is clean (the star arm holds); mid-tier→bottom authoring is real but degraded — consistent with the claim that the delegation ladder has fewer rungs than the execution ladder.
+
+**Still open.** The device tier is untested (all three models are cloud). The multi-rung chain remains thin: one rung of mid-tier stewardship, observed once, lossy. Star-vs-chain is now *informed*, not resolved.
+
 ## Open questions
 
 - **Star or chain?** Does stewardship capacity re-instantiate at each tier (a chain — every tier stewards the one below), or does it concentrate at the frontier (a star — one steward authoring for everyone)? The single observed rung does not distinguish them. This is the dominant empirical question for the whole model.