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Current AI agents can match human performance in some tasks, but they largely recycle existing human-designed algorithms rather than creating novel solutions.
Performance discrepancies in material property prediction can be drastically reduced with AutoMatBench, which saves over half the computational cost while achieving similar results to established benchmarks.
