A two-system paradigm unifies disparate Monte Carlo approaches by having two particle subsystems interact symmetrically, yielding new overdamped and underdamped Langevin samplers that show higher ESS per gradient and wall-clock throughput than NUTS baselines.
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A new LLM-based system generates and validates runnable modular MCMC samplers directly from natural-language Bayesian model descriptions, reporting success on 120 of 132 benchmark models.
RefineStat improves small language model performance on probabilistic program synthesis by adding semantic constraint enforcement and diagnostic-aware refinement, producing syntactically and statistically reliable code that often matches larger models.
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Divide, Interact, Sample: The Two-System Paradigm
A two-system paradigm unifies disparate Monte Carlo approaches by having two particle subsystems interact symmetrically, yielding new overdamped and underdamped Langevin samplers that show higher ESS per gradient and wall-clock throughput than NUTS baselines.
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AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
A new LLM-based system generates and validates runnable modular MCMC samplers directly from natural-language Bayesian model descriptions, reporting success on 120 of 132 benchmark models.
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RefineStat: Efficient Exploration for Probabilistic Program Synthesis
RefineStat improves small language model performance on probabilistic program synthesis by adding semantic constraint enforcement and diagnostic-aware refinement, producing syntactically and statistically reliable code that often matches larger models.