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 generates validated modular stateful MCMC samplers from natural language Bayesian model descriptions via LLM translation, modular blocks, and recursive stateful composition.
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
AI4BayesCode generates validated modular stateful MCMC samplers from natural language Bayesian model descriptions via LLM translation, modular blocks, and recursive stateful composition.
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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.