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@IntuitMachine Seems like good strategy even now in late 2026
Post says cross-review by two coding agents catches more silent bugs in training codeMachine translation
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A post describing a Meta paper says having different coding agents review each other’s patches catches more silent bugs than giving one agent a larger budget. At matched spend, pairing Claude Code and Codex raised the share of fully correct patches from 45.8% to 62.5%; the post says the effect also appeared in an unrelated training codebase.
Tip: Use different AI vendors to critique your work.
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New Meta paper finds that having 2 different coding agents review each other's patches catches far more silent bugs than giving 1 agent a bigger budget.
Mixing Claude Code and Codex on the same task raised fully correct patches from 45.8% to 62.5% at matched spend.
Agents editing real training code can leak test data, break a gradient, or miswire a flag.
The code still runs, so you burn GPU hours and get numbers that look valid.
The reason is uncorrelated mistakes: agents from the same product fail the same way, so there is little for review to catch. The effect repeated on an unrelated training codebase.
– arxiv. org/abs/2609.39551
Title: "RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking Models"
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