3 papers
cs.AI2026
Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection
Hai Xia, Carlos Ansótegui, Stefan Szeider
Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing feature extractors demands deep domain expe…
cs.AI2026
LLM-Guided Graph Generation for Structure-Based Local Improvement Methods
Hai Xia, Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider
Large neighborhood search normally selects a random subset of decision variables for iterative optimization. To efficiently solve various problems, researchers tend to design varia…
cs.AI2025
Smart Cubing for Graph Search: A Comparative Study
Markus Kirchweger, Hai Xia, Tomáš Peitl +1
Parallel solving via cube-and-conquer is a key method for scaling SAT solvers to hard instances. While cube-and-conquer has proven successful for pure SAT problems, notably the Pyt…