SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution
Quick Take
SMCEvolve enhances automated scientific discovery through principled Sequential Monte Carlo program evolution.
Key Points
- Introduces adaptive parent resampling and mutation mechanisms.
- Provides automatic convergence control for better results.
- Outperforms existing systems with fewer LLM calls.
📖 Reader Mode
~2 min readAbstract:LLM-driven program evolution has emerged as a powerful tool for automated scientific discovery, yet existing frameworks offer no principled guide for designing their individual components and provide no guarantee that the search converges. We introduce SMCEvolve, which recasts program search as sampling from a reward-tilted target distribution and approximates it with a Sequential Monte Carlo (SMC) sampler. From this view, three core mechanisms emerge as principled components: adaptive parent resampling, mixture of mutation with acceptance, and automatic convergence control. We further provide a finite-sample complexity analysis that bounds the LLM-call budget required to reach a target approximation error. Across math, algorithm efficiency, symbolic regression, and end-to-end ML research benchmarks, SMCEvolve surpasses state-of-the-art evolving systems while using fewer LLM calls under self-determined termination. The code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2605.15308 [cs.AI] |
| (or arXiv:2605.15308v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15308 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiachen Jiang [view email]
[v1]
Thu, 14 May 2026 18:21:08 UTC (4,977 KB)
— Originally published at arxiv.org
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