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

Key points

The starting point is the insight that purely observational data cannot identify causal structure, so direct intervention through experiments is necessary to obtain answers.
The LLM acts as the hypothesis proposer, while Bayesian machinery, including SMC, SBI, and VoI, handles validation and experiment design.
When no correct hypothesis exists in the current hypothesis set, the system detects the gap through predictive checks and the LLM expands the search space by adding new models.
Discovery and experiment design reinforce each other, because the designed experiments confirm proposed mechanisms and the confirmed mechanisms improve prediction, which in turn reveals residual errors for the next discovery step.
The authors create a new single-neuron electrophysiology benchmark under partial observability called NEURONBENCH.

External research summaries. These are not HDATF publications or measured product results.