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    Mixing Claude Code and Codex reportedly raised fully correct patches from 45.8% to 62.5% at matched spend

    A post describing a Meta paper says cross-review caught more silent bugs than giving one coding agent a bigger budget.

    Rohan PaulRP
    1 Source, 2h ago, first seen 2h ago

    TLDR

    A post describing a Meta paper says using Claude Code and Codex on the same task raised fully correct patches from 45.8% to 62.5% at matched spend. It attributes the gain to agents making different mistakes, giving cross-review more to catch, and says the effect repeated on an unrelated training codebase.

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    1 Source, first seen 2h ago

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    1 Source

    Rohan Paul@rohanpaul_aiNew 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"2h

    1 Source

    Rohan Paul@rohanpaul_aiNew 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"2h