[Submitted on 23 Sep 2026]
Title:When Is a Multi-Agent Code Judge Actually Grounded? Two Label-Free Measurements, and a Judge That Declines to Guess
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Abstract:When one language model judges whether another's code is correct, it does not report the absence of evidence. It returns a confident verdict with reasoning attached, indistinguishable from a verdict it had grounds for. Multi-agent verification, which decomposes a judgment into checkable claims and verifies each against evidence, is a promising response and works well when the evidence is a set of retrieved documents.
We argue such methods require two things of their evidence: it must be independent of the answer under review, and it must differ between the two candidates being compared. The second condition holds automatically with retrieved documents and stops holding in code judging.
Running MARCH, a published framework unmodified over 80 condition-by-cell measurements on two code judging benchmarks, we find it declares both solutions equally good on 78 to 95% of comparisons, reaching 4.4% accuracy where the same model asked directly reaches 43.7%. Neither easier problems nor a larger judge changes this. Two measurements taken from the pipeline's own logs explain it without needing labels.
Gating on one of them, the pipeline declines the comparisons it cannot make and raises its accuracy from 20.7 to 36.9% while still answering half of all comparisons. The contribution is not a more accurate judge, but a label-free way to tell when a judge has no basis for its answer.
Comments: Accepted at the 21st Women in Machine Learning Workshop (WiML), NeurIPS 2026
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
Cite as: arXiv:2609.30328 [cs.AI]
(or arXiv:2609.30328v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.30328
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Salma Roshdy Aly [view email] [v1] Wed, 23 Sep 2026 23:19:59 UTC (8 KB)
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