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Exposure Without Mastery: Why seeing more of your code doesn't make LLMs better at maintaining it
Get the latest insights drawn from 57 LLMs and 196,137 evaluations, showing greater exposure to code was linked to lower refactoring success, despite leaving detectable similarities in model behavior.
Key takeaways:
- More exposure didn’t improve refactoring performance.
- Models showed similar success and failure patterns on exposed code.
- Different LLMs could share the same blind spots.
- Public benchmark performance may not predict results on proprietary code.
Make clearer judgements about which AI models to trust on code maintenance, and how much weight to give public benchmark scores when choosing or scaling them.
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Last updated:
September 9, 2026
Published:
September 9, 2026
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