The paper introduces a new Monte Carlo algorithm for approximate counting of Disjunctive Normal Form (DNF) formulas, featuring an adaptive stopping rule and short‑circuit evaluation. It achieves PAC learning bounds and is asymptotically more efficient than existing methods, including classical Monte Carlo, hashing‑based, and neural‑network approaches. Experiments demonstrate that the algorithm outperforms prior techniques by orders of magnitude and scales to problems with millions of variables.
arXiv:2607. 11492v1 Announce Type: new Abstract: In this paper, we investigate preprocessing techniques aimed at improving the efficiency of accessing models of propositional formulas represented in conjunctive normal form (CNF).
arXiv:2608. 16565v1 Announce Type: new Abstract: This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertainty in artificial intelligence (AI).
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Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific structures such as JSON schema-formatted function calls. Existing systems are designed for autoregressive models and assume left-to-right generation, masking out invalid next tokens at each step.