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LLMs can overcome knowledge gaps in evolutionary search by dynamically co-evolving solutions and targeted web queries, leading to substantial gains in scientific discovery.
AI-generated scientific papers exhibit a distinct "scientific slop" in global reasoning, detectable with 85.9% accuracy and impacting publication success, but can be effectively mitigated by grounding revisions in experimental evidence.
Recursive self-improvement in LLMs can lead to unprecedented performance, with Meta^n surpassing all prior agents on challenging benchmarks by leveraging fixed meta-operations.
EFT enables LLMs to evolve solutions across diverse optimization tasks, achieving over 10% performance gains and state-of-the-art results in challenging mathematical problems.
