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ML-based data compression can be environmentally sustainable, but only if it surpasses a critical break-even point in carbon savings.
No single discovery harness reliably outperforms others, suggesting that harness selection should be treated as a hyperparameter tailored to specific tasks.
LLM-driven program evolution gets a smart upgrade: AdaEvolve dynamically allocates resources to promising solution candidates, leaving static schedules in the dust.
