2026· International Conference on Principles and Practice of Constraint Programming· pp. 29:1-29:20· 2 citations· 52 references
Computer Science
TL;DR
This paper adapts an existing MaxSAT-based explanation framework designed for TEs to function with general REs, and devise a dedicated logic encoding for REs combining SAT solving with pseudo-Boolean (PB) constraints for determining the winning class.
A fine-tuning-based Stratified Consistency Distillation approach that shows significant and consistent improvements in both Pass@K and the novel Equivalent Logical Similarity metrics, demonstrating the potential of advancing logical translation through consistency distillation.
Zhi-Chao Hou, Ferhat Erata, Joseph Lilien et al.· 1 citation
The Boolean satisfiability (SAT) problem is fundamental in applications such as verification and scheduling, where fast solving is often required. However, the performance of SAT solvers varies significantly across instances, making solver selection an important challenge. Previous studies have commonly employed random...
Takeru Nagahama, Tomohisa Kawakami, Tomoyasu Shimada et al.· IEEE International Conferenc...· 0 citations
This work proposes an end-to-end approach for handcrafted Feature-Free SAT Solver Selection, called F2S3, which effectively captures the structural complexity of graph data, eliminates the need for handcrafted features, and improves feature representation in the low-dimensional space.
Yitao Zhang, Xiao Yang, Yong Lai et al.· Proceedings of the 32nd ACM...· 0 citations
This work formalizes explanations as Halpern-Pearl actual causes, modeling input dependencies using Boolean Structural Causal Models (SCMs), and compute HP causes by applying bound propagation and branch-and-bound techniques, while providing formal guarantees of completeness and minimality.
J. Strobel, Muqsit Azeem, Stefan Leue· 0 citations
The widespread adoption of artificial intelligence (AI) within real-world applications has raised a lot of concerns regarding their trustworthiness, especially in critical applications. The field of eXplainable AI (XAI) has emerged with the objective of providing explanations to the users about the decisions made by AI...
Arthur Ledaguenel, Florent Capelli, Jean-Marie Lagniez· 0 citations
Large language models (LLMs) have recently improved their problem-solving abilities and can solve complex mathematical problems with an increasing accuracy, necessitating the development of more challenging benchmarks. Over the years, the performance of LLMs on several benchmark datasets has also improved, motivating t...
Anurag Dutta, S. Priya, A. Ramamoorthy et al.· AppliedMath· 0 citations
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