4 papers · 1 filter
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…
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…
Agentic Neurosymbolic Collaboration for Mathematical Discovery: A Case Study in Combinatorial Design
Hai Xia, Carla P. Gomes, Bart Selman +1
We study mathematical discovery through the lens of neurosymbolic reasoning, where an AI agent powered by a large language model (LLM), coupled with symbolic computation tools, and…
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…