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SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution

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arXiv:2605. 15308v1 Announce Type: new Abstract: 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.

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Authors: Jiachen Jiang, Huminhao Zhu, Zhihui Zhu
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, under, control. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.15308

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