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AutoML-Zero evolutionary search over learning programs

Contribution grade

Automatic learning-algorithm discovery · Human-designed research method or resource

Reference score125,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Evolves Setup/Predict/Learn programs built from scalar/vector/matrix operations, rediscovering and extending small learning mechanisms on image-classification proxy tasks.

Scientific scope

Evolves Setup/Predict/Learn programs built from scalar/vector/matrix operations, rediscovering and extending small learning mechanisms on image-classification proxy tasks. Evolutionary search assembled learning programs from basic operations in a human-designed framework; results were demonstrated on bounded learning tasks. The memory model, instruction set, evaluator, task distribution and compute are designed by humans. From-scratch means no supplied neural-network/gradient program, not no inductive bias or general autonomous science. Projected/binary image tasks do not show automatic discovery of frontier-scale learners. First two authors contribute equally.

Evidence

Evolutionary search assembled learning programs from basic operations in a human-designed framework; results were demonstrated on bounded learning tasks. WRS has not independently rerun these experiments. Evolutionary search assembled learning programs from basic operations in a human-designed framework; results were demonstrated on bounded learning tasks. The memory model, instruction set, evaluator, task distribution and compute are designed by humans. From-scratch means no supplied neural-network/gradient program, not no inductive bias or general autonomous science. Projected/binary image tasks do not show automatic discovery of frontier-scale learners. First two authors contribute equally.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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