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The Experiment Planner Asked What Would Separate the Models
MDA combines LLM-proposed mechanisms with Bayesian inference and value-of-information experiment selection.
Summary
MDA combines LLM-proposed mechanisms with Bayesian inference and value-of-information experiment selection.
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.
Why it matters
MDA combines LLM-proposed mechanisms with Bayesian inference and value-of-information experiment selection.
Limits and context
- It remains a preprint framework: a plausible mechanism proposed efficiently is not the same as a confirmed causal explanation.
Key claims
MDA combines LLM-proposed mechanisms with Bayesian inference and value-of-information experiment selection.
Qualification: It remains a preprint framework: a plausible mechanism proposed efficiently is not the same as a confirmed causal explanation.
Evidence: source-2026-08-11-009
Sources
- arXiv preprint 2608.09696arXiv · primary research
Corrections
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