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The paper introduces MDA, a framework that uses LLMs for hypothesis generation and Bayesian inference for mechanism scoring, significantly reducing experiment needs while improving accuracy on scientific benchmarks like FORCEBENCH.
A detailed guide on designing effective ML experiments, emphasizing starting with a clear research question, developing research taste, and scaling results. Based on the author's experience running ~100 experiments weekly at Poolside.
OpenAI’s GPT-Rosalind plus a Life-Sciences Research Plugin turns a high-priority target into a ready-to-run 96-well wet-lab protocol in seconds, grounding every reagent choice in public data and feeding results back to shorten design cycles to hours.