The judge cannot see the margin: a paired ablation of a mechanical proof step in a model-run design pipeline

Desk Studio runs a design job as a loop of model calls: a builder polishes a card, a browser renders it, a proof step measures the rendered pixels, and a Creative Director scores the screenshots against a rubric and signs off or sends the round back. Proof is the one step that never calls a model. I asked whether it changes anything when a strong vision model is already looking. I added a flag, --no-proof, that skips the checks and leaves everything else in place, and ran 15 cases in both arms and in two model families (claude-opus-5-5 and gpt-6-astra as directors), 60 jobs in 30 pairs, with four hypotheses written down before any job finished. Without proof the director signed off 17 files and all 17 failed a hidden audit. With proof, 0 of 17. Against my prediction, proof cost less, 0.735 against 0.985 USD a job at list price in the Anthropic family (exact p = 0.064) and 1.074 against 1.311 pooled (p = 0.021), because a failed proof round costs a builder call and not a director call. The director's final rubric averages did not differ, 8.49 with proof and 8.43 without (p = 0.307), so its scores carry no trace of the faults. Without proof it named a mechanical fault in 13% of its notes, against 1% with proof, and still signed the faulty files. In 19 of the 30 proof-on jobs a later polish round broke a check an earlier round had passed. The rule I take from this is that anything measurable belongs in a check on the rendered file, run after every round, and the judge keeps what only it can see.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23778626
Primary Topic
Artificial Intelligence Applications
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

The judge cannot see the margin: a paired ablation of a mechanical proof step in a model-run design pipeline

Aditya Reddy
1 links
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence Applications
preprint

The judge cannot see the margin: a paired ablation of a mechanical proof step in a model-run design pipeline

Aditya Reddy
preprint en

Abstract

Desk Studio runs a design job as a loop of model calls: a builder polishes a card, a browser renders it, a proof step measures the rendered pixels, and a Creative Director scores the screenshots against a rubric and signs off or sends the round back. Proof is the one step that never calls a model. I asked whether it changes anything when a strong vision model is already looking. I added a flag, --no-proof, that skips the checks and leaves everything else in place, and ran 15 cases in both arms and in two model families (claude-opus-5-5 and gpt-6-astra as directors), 60 jobs in 30 pairs, with four hypotheses written down before any job finished. Without proof the director signed off 17 files and all 17 failed a hidden audit. With proof, 0 of 17. Against my prediction, proof cost less, 0.735 against 0.985 USD a job at list price in the Anthropic family (exact p = 0.064) and 1.074 against 1.311 pooled (p = 0.021), because a failed proof round costs a builder call and not a director call. The director's final rubric averages did not differ, 8.49 with proof and 8.43 without (p = 0.307), so its scores carry no trace of the faults. Without proof it named a mechanical fault in 13% of its notes, against 1% with proof, and still signed the faulty files. In 19 of the 30 proof-on jobs a later polish round broke a check an earlier round had passed. The rule I take from this is that anything measurable belongs in a check on the rendered file, run after every round, and the judge keeps what only it can see.

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon