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    "headline": "The Experiment Planner Asked What Would Separate the Models",
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    "dek": "MDA combines LLM-proposed mechanisms with Bayesian inference and value-of-information experiment selection.",
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    "body_text": "The Model Discovery Agent proposes candidate causal structures, estimates their parameters and posterior probabilities, and chooses interventions expected to discriminate among them. Sequential Monte Carlo and simulation-based inference provide the numerical machinery; the language model supplies structural hypotheses. The goal is to learn mechanistic world models from fewer experiments. It remains a preprint framework: a plausible mechanism proposed efficiently is not the same as a confirmed causal explanation.",
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      "title": "arXiv preprint 2608.09696",
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    "title": "The Experiment Planner Asked What Would Separate the Models",
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