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Two AIs reached the same verdict for different reasons. The gate now catches that — because its first approval was exactly that failure

The 72-call experiment(https://miscsubjects.com/a/auditable-reasoning-audited) ended on a celebrated result: the first sealed APPROVE, three models unanimous, clause signature 1,2,3. It was false convergence. The old gate compared the

The defect the last APPROVE was hiding

The 72-call experiment ended on a celebrated result: the first sealed APPROVE, three models unanimous, clause signature [1,2,3]. It was false convergence.

The old gate compared the clause numbers each model cited. Three models can cite clauses 1, 2 and 3 and mean completely different things by them — clause 2 "triggered" for one and "not triggered" for another, resting on different records, pointing to opposite effects — and the gate would still call that agreement and authorise the action. Citing the same rule is not applying it the same way. The gate was reading the table of contents and calling it the argument.

This page is the fix, proven live, and the more uncomfortable finding underneath it: two of the things blocking a genuine APPROVE were never the models at all. One was the governing prompt. The other was the input.

What changed in the gate

Every governed finding is now parsed into a versioned object (decision-finding@1.0.0) that is a deterministic projection of the raw payload — it never infers or repairs a missing field. A finding is structurally invalid, and can never authorise, when it lacks the terminal decision, lacks any required field, lacks the clause-evaluation vector, or invents a clause or an evidence id.

That last one is not hypothetical. A first panel under the new constitution escalated because glm-4.7-flash cited clauses 7, 8 and 12 in a three-clause ruleset — it invented three rules. The parser marked it malformed; the gate refused.

Then the comparison itself changed. Each model must now emit, as the last line of its finding, a machine-readable vector — one entry per clause, each carrying the clause's trigger_state (did its condition fire on this record), its disposition (does that support, defeat, or stay neutral to the action), and the exact record ids it rests on. The gate compares the canonical tuple of those fields. Same clause numbers with different tuples is divergence, and divergence escalates.

Nine deterministic unit tests pin this, including the one that matters: same verdict, same clause numbers, different tuples → different signatures; and identical logic with different wording and evidence order → identical signatures. Wording is the human's; the tuple is the machine's.

Four outcomes, live

Run through the production path — fresh stateless calls, each ledgered, then sealed by id in bound mode.

outcomecaseverdictderivationsseal
APPROVEa parking-permit rule, sufficiency-completeunanimous AFFIRM1 identical signatureinv_wl0rnh136b
NEGATEa late service-credit claimunanimous DENY1 identical signatureinv_cgwtkvx17u
ESCALATEan access request with the roster withheldunanimous CANNOT_CONCLUDE2 divergent signaturesinv_o6s0exhodd

The APPROVE is the genuine article the last one impersonated: not just the same verdict and the same clauses, but the same per-clause reasoning — 1:triggered:supports:reg | 2:not_triggered:neutral:cite from every seat.

The ESCALATE is the fix's clearest proof. All three models returned CANNOT_CONCLUDE and all three cited clauses [1,2,3]. The old gate would have sealed that as a clean NO_ACTION. The new gate escalated it, because two of the three derived that conclusion differently — they split on whether clause 2's condition even fired when the roster was missing. Agreement on the answer is not agreement on the reasoning, and only the second is safe to act on.

The input is half the instrument

Before the corrected APPROVE, I could not get three models to converge on the access-control case no matter how I tuned the prompt. The reflex is to blame the model tier. That reflex is wrong.

I asked glm-5.2, under the constitution, to review the case input as a colleague before adjudicating it. It found eight defects — beginning with one that made the whole exercise incoherent:

Its lead finding: my ruleset said access is granted "only to" an individual who matches the roster. That is a necessary condition — if granted, then a match — and I was asking the models an affirmative question, should access be granted. No clause anywhere said a match was sufficient to grant. A careful model could correctly return CANNOT_CONCLUDE (nothing licenses a grant) while another returned AFFIRM (reading the match as sufficient). The divergence I kept seeing was not the models failing. It was the models faithfully reflecting a hole in the rules back at the person who wrote them.

The clean APPROVE came only after moving to a rule stated in sufficiency form — "a permit is issued when registration is current." Same models, same gate. The variable was the input.

Prompt version versus conformance

The author's claim was that the variance was the prompt, not the model. The versions bear it out. Holding the models fixed:

constitutionwhat it addedconforming findingsderivation agreement
v1.1.0invariant register, no vectorn/a — no vector to comparenot measurable
v1.3.0the clause-evaluation vector (rules only)1 of 3 (one used a BASIS line, one invented clauses)divergent
v1.3.0 + output-format overridetold the model the constitution outranks its row schema2 of 2 capable seats validcloser
v1.3.2a worked right/wrong exemplar; a collegial, specific uncertainty path3 of 3 valididentical

The jump from stating the rules to showing a filled-in right answer and five labelled wrong ones is what took conforming findings from one-in-three to three-in-three. Models conform to an exemplar, not a specification — which is exactly how the original 2026 system prompt was built, with its LEVEL 1/2/3 worked cases, and exactly what this one had been missing.

What is not yet proven

This page proves the gate's structural behaviour: it approves genuine derivation agreement, refuses genuine disagreement, and escalates a unanimous verdict whose reasoning diverges. It does not prove the models are correct. A gate that seals perfectly on agreement still says nothing about whether the agreed answer is the right one — three models can agree, derive identically, and all be wrong together.

That is the next experiment, named and not yet run: a fixed benchmark of determinate cases with outcomes fixed by a deterministic oracle before any model sees them, scored on one primary metric — the rate of wrongful authorisation. Until that runs, the honest claim is exactly this and no more: the instrument now measures agreement at the level of derivation, and it is cheap enough to do it on every consequential decision. Whether the agreement is right is a question this page does not answer and does not pretend to.

References (5)
  1. APPROVE — genuine derivation agreement, first under the new gate — miscsubjects.com
  2. NEGATE — unanimous DENY, identical derivations — miscsubjects.com
  3. ESCALATE — unanimous verdict, divergent derivations — miscsubjects.com
  4. @cf/zai-org/glm-5.2 reviewed the author's own case input — and found eight defects — Cloudflare Workers AI via miscsubjects gateway
  5. The earlier gate catching an invented-clause hallucination — miscsubjects.com