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  "text": "Stronger models do not always need lighter harnesses.\n\nEveryone believes more structured harnesses universally improve reliability, and that higher-capability models need proportionally less structural guidance. Together, that implies a clean inverse relationship between model tier and optimal harness complexity.\n\nThis new research tests it with a controlled 432-run experiment, six models across four capability tiers crossed with three harness conditions, on a 24-task benchmark with git-based workspace verification.\n\nFor a frontier chat model, increasing harness verbosity dropped success by 29 to 38 percentage points. They call it the harness-complexity paradox.\n\nPaper: https://t.co/3D1FUdQ3Y8\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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