Jakob Macke reposted
ModelSMC: we frame LLM-based scientific model discovery as Bayesian inference. Sequential Monte Carlo over executable model structures, with the LLM as a probabilistic proposal mechanism. 📍 Wed Jul 8, at 2:30–4:15 PM KST in Hall A #3512 📄 arxiv.org/abs/2602.18266 🧵 bsky.app/pr…
AI Weekly's analysis
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- The authors introduce ModelSMC, an algorithm based on Sequential Monte Carlo sampling that treats candidate scientific models as weighted particles.
- They recast LLM-based model discovery as sampling from an unknown distribution over mechanistic models capable of explaining observational data.
- Reported experiments on unnamed real-world scientific systems claim interpretable mechanisms and improved posterior predictive checks.
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