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Indirect data poisoning can enable scientific fraud at an unprecedented scale, with a staggering 49.56% success rate in corrupting AI-driven research outputs.
LLM agents can identify reproducibility problems in 90% of analyzed machine learning papers, leveraging GitHub issues as a novel supervision source.
