Many scientific discovery problems require searching combinatorial hypothesis spaces under complex domain constraints. Reinforcement learning (RL) offers a promising approach, but existing methods rely on scalar rewards that provide limited information about why candidate solutions fail, leading agents to repeatedly ex...
Piyush Jha, Jake Rudolph, V. Knapp-Pérez et al.· 3 citations
Proof auto-formalization translates natural-language (NL) theorems and proofs into a formal language (FL) such as Lean, enabling mechanical verification. Despite rapid progress, research-level proofs often depend on concepts missing from leading proof assistant libraries (e.g., Lean's Mathlib), and successful compilati...
Prithwish Jana, Việt Bách Hoàng, Logan Luna et al.· 0 citations
CDES is introduced, which extends evolutionary search with enforceable restrictions derived from failed candidates, recorded as certificates of assumptions, checker evidence, and justified restrictions, to CPU-to-GPU translation of two particle-simulation functions from the Geant4 toolkit.
Evaluated across nine SMT logics, SMT-Select consistently outperforms existing selectors and SMT-COMP winning solvers and closes at least 30% of the performance gap between the competition winner and the virtual best solver (VBS).
Zhengyang Lu, Paul Sarnighausen-Cahn, Jiahao Chen et al.· International Conference on...· 0 citations
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