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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models

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TL;DR - Model Discovery Agent (MDA) pairs an LLM proposing candidate mechanistic model structures with Bayesian inference and value-of-information experiment design, aiming to learn causal world models from very few interventions. It matters because interventional "what if" prediction needs mechanism, not curve fitting, and experiments are expensive.

  • Architecture: LLM acts as a structure proposer; sequential Monte Carlo handles parameter/structure posteriors, simulation-based inference covers intractable likelihoods, and VoI selects the next experiment.
  • Operates in the M-open setting: a predictive check flags when truth lies outside the current hypothesis class, triggering the proposer to expand the hypothesis space, with new parameters identified by designed experiments.
  • Core claim is a discovery–design feedback loop: designed experiments identify proposed mechanisms, better-identified mechanisms improve predictions, and remaining unexplained residuals drive further discovery.
  • Evaluated on three benchmarks spanning physics, chemistry, and a new partially observed single-neuron electrophysiology benchmark (HH); authors report SOTA in data-efficient model learning and interventional forecasting.

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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models

arXiv cs.AI Kevin Murphy 2026-08-10 arXiv:2608.09696
Public signals Hugging Face upvotes 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-09 08:17:50.520903 UTC

TL;DR - Model Discovery Agent (MDA) pairs an LLM proposing candidate mechanistic model structures with Bayesian inference and value-of-information experiment design, aiming to learn causal world models from very few interventions. It matters because interventional "what if" prediction needs mechanism, not curve fitting, and experiments are expensive.

  • Architecture: LLM acts as a structure proposer; sequential Monte Carlo handles parameter/structure posteriors, simulation-based inference covers intractable likelihoods, and VoI selects the next experiment.
  • Operates in the M-open setting: a predictive check flags when truth lies outside the current hypothesis class, triggering the proposer to expand the hypothesis space, with new parameters identified by designed experiments.
  • Core claim is a discovery–design feedback loop: designed experiments identify proposed mechanisms, better-identified mechanisms improve predictions, and remaining unexplained residuals drive further discovery.
  • Evaluated on three benchmarks spanning physics, chemistry, and a new partially observed single-neuron electrophysiology benchmark (HH); authors report SOTA in data-efficient model learning and interventional forecasting.
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