56 citations · 56 across the 4 of their papers we have counts for
5 papers
ORSMT: Unleashing the Potential of Satisfiability Modulo Theories for Semantic Verification and Repair in LLM-Based Optimization
Rui Zhang, Chenyu Zhou, Jianghao Lin +3
Large language models increasingly automate optimization modeling from natural language problems, producing written mathematics, solver code and a returned report. Verifying one op…
DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation
Chenyu Zhou, Yaqi Wu, Xiaolei Guo +7
Multi-hop retrieval-augmented generation (RAG) acquires evidence sequentially, with each document contributing supporting facts, bridge entities, query refinements, or sufficient e…
Skill2Query: Exploiting Skill Structure to Generate Pseudo-Queries for Agent Skill Retrieval
Lihui Ding, Zihan Guo, Bingwei Lu +5
Pseudo-query generation can alleviate the supervision bottleneck for agent skill retrieval, but existing document-level approaches typically leave the rich internal relations among…
From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
Jianghao Lin, Zi Ling, Chenyu Zhou +4
Optimization modeling underpins real-world decision-making in logistics, manufacturing, energy, and public services, but reliably solving such problems from natural-language requir…
ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling
Chenyu Huang, Zhengyang Tang, Shixi Hu +5
Optimization modeling plays a critical role in the application of Operations Research (OR) tools to address real-world problems, yet they pose challenges and require extensive expe…