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AI can now design better AI: ASI-Evolve discovers SOTA architectures, curates pretraining data, and designs RL algorithms, outperforming human-designed baselines by significant margins.
Pretraining isn't just about scaling data volume; daVinci-LLM's ablations reveal that data processing depth, domain-specific strategies, and compositional balance are equally critical for unlocking LLM capabilities.
Frontier LLMs can unlock substantial performance gains in scientific domains by refining and completing raw scientific text, leading to a +8.40 point improvement on domain-aligned tasks.
