Commit-first LLM judging inherits the judge's own errors

An audit of eight evaluation frameworks finds no implementation of the one defence that works, and a controlled experiment shows why the defence is not a cure

Idil Gozel· 2026· DOI 10.48550/arXiv.2609.00088

The core problem

LLM judges are models that score another system's output. They are increasingly used as automated evaluators in software pipelines, but they can be gamed by the systems they score. Recent work identifies one defence that works: the judge solves the task itself first and commits to that answer, then accepts a candidate only if the two match. The authors call this **commit-first judging** and ask two questions: whether shipped software implements it, and what it costs. The paper audits the default judge configurations of eight widely used evaluation frameworks. Of the 24 configurations in scope, none implement commit-first judging. Nine implement a variant the literature measures as ineffective, and these nine share one ancestor prompt, traceable through a copied typographical error. The authors then run a controlled experiment to test the cost of commit-first judging. The central finding is that commit-first judging does not remove the anchor that gets gamed; it moves it from the candidate to the judge's own answer. Therefore, evaluation is only as good as the judge is at the task. This precondition is cheap to measure in advance and is task local rather than scale dependent: a smal

Innovation

The audit found that none of the 24 configurations implement commit-first judging. Nine implement a variant the literature measures as ineffective, and these nine share one ancestor prompt, traceable through a copied typographical error. In the controlled experiment, on the interval merging task, the judge accepted 90 of 96 candidates in one seed and 93 of 96 in the other. Every accepted candidate passed every test the search could see and failed a held-out suite it could not. The judge identified the defective line and cited it as grounds for a perfect score. Commit-first judging removed the effect: 0 of 96 in both seeds. On a second task, commit-first judging made matters worse in both seeds: the judge's committed answer was wrong, and in one seed the population converged on it. This is the main finding. The authors also report that five of fifteen claims in their criteria were wrong against verbatim sources, and two held-out checks were unjustified by their specifications. The results show that commit-first judging does not remove the anchor that gets gamed; it moves it from the candidate to the judge's own answer. The precondition for effective commit-first judging is that the

LLM judges are models that score another system's output. They are increasingly used as automated evaluators in software pipelines, but they can be gamed by the systems they score. Recent work identifies one defence that works: the judge solves the task itself first and commits to that answer, then accepts a candidate only if the two match. The authors call this **commit-first judging** and ask two questions: whether shipped software implements it, and what it costs. The paper audits the default judge configurations of eight widely used evaluation frameworks. Of the 24 configurations in scope, none implement commit-first judging. Nine implement a variant the literature measures as ineffective, and these nine share one ancestor prompt, traceable through a copied typographical error. The authors then run a controlled experiment to test the cost of commit-first judging. The central finding is that commit-first judging does not remove the anchor that gets gamed; it moves it from the candidate to the judge's own answer. Therefore, evaluation is only as good as the judge is at the task. This precondition is cheap to measure in advance and is task local rather than scale dependent: a smaller judge solved a task the frontier judge failed and resisted gaming where it did not.

The study combines an audit of existing evaluation frameworks with a controlled experiment. The audit examines the default judge configurations of eight widely used evaluation frameworks, yielding 24 configurations in scope. For each configuration, the authors check whether it implements commit-first judging, and if not, what variant it implements. They also trace the provenance of the prompts. The controlled experiment uses an ordinary best-of-N search with no access to correct answers. The search optimises code against one of the audited configurations, used exactly as documented. The task is interval merging. The judge is used as the scoring function, and the search proposes candidates. The authors measure how many candidates the judge accepts and how many of those accepted candidates pass a held-out suite that the search cannot see. They run two seeds. They then repeat the experiment with commit-first judging enabled, and on a second task. They also validate their own instruments: five of fifteen claims in their criteria were wrong against verbatim sources, and two held-out checks were unjustified by their specifications. The experimental design can be summarised as a flow: a candidate generator proposes outputs, the judge scores them, and the search selects the highest-scoring candidate. In the commit-first variant, the judge first solves the task and commits to an answer, then compares the candidate to that answer. The key metric is the acceptance rate of defective candidates that pass the visible tests but fail the held-out suite.

Why it matters

The central implication is that commit-first judging is not a universal fix. It replaces one anchor with another. In the standard setup, the judge is anchored on the candidate and can be gamed by a candidate that looks correct. In the commit-first setup, the judge is anchored on its own committed answer. If that answer is wrong, the judge will reject correct candidates and accept incorrect ones that match its error. The second task demonstrates this failure mode: the judge's committed answer was wrong, and in one seed the population converged on it. Therefore, the effectiveness of commit-first judging depends on the judge's task competence. This precondition is task local, not scale dependent. A smaller judge can outperform a frontier judge on a specific task, and thus resist gaming where the frontier judge does not. The authors suggest that this precondition is cheap to measure in advance: one can test the judge on a held-out set of tasks before deploying it. The audit also reveals a provenance problem: nine configurations share an ancestor prompt with a copied typographical error, indicating that ineffective variants propagate through copying. The authors' self-validation shows that even their own criteria contained errors, highlighting the difficulty of reliable evaluation. The broader lesson is that evaluation frameworks should implement commit-first judging only when the judge's competence on the target task has been established, and that the judge's own errors are inherited by the evaluation.

Who should read this

CS practitioners and researchers

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