Author

Dongdong Ge

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Preprint Oct 2026

ORSMT: Unleashing the Potential of Satisfiability Modulo Theories for Semantic Verification and Repair in LLM-Based Optimization

Across 3,034 problems and six generator sizes, ORSMT improves the model-code match rate in all 72 benchmark and generator combinations before selection and raises the aggregate rate from 43.2% to 88.2% and the share that are both correct and matched from 25.2% to 40.2%.

Rui Zhang, Chenyu Zhou, Jiang-Hao Lin et al. · 0 citations

Preprint Aug 2026

Skill2Query: Exploiting Skill Structure to Generate Pseudo-Queries for Agent Skill Retrieval

Skill2Query is a framework that first parses a skill document into a Skill Knowledge Graph and then generates pseudo-queries through a three-stage process including style mimicking, query template generation, and parameter filling, which consistently improves sparse, dense, and skill-routing retrieval.

Lihui Ding, Zi-Han Guo, Bingwei Lu et al. · 2 citations