Method: The Discernment Transfer Ladder

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 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.

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.

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.

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.

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.

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.

• 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.