5 papers
AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library
Minwei Kong, Ao Qu, Xiaotong Guo +12
Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise…
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Minwei Kong, Chonghe Jiang, Ao Qu +24
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…
Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization
Xiaoyuan Cheng, Wenxuan Yuan, Zhancun Mu +5
Model-based reinforcement learning (RL) can be effectively supported at scale through the use of world models. However, in practice, scaling such approaches remains fundamentally l…
MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training
Taicheng Guo, Hai Wang, ChaoChun Liu +4
Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to the target schema. However, mos…
Knowledge Graph Enhanced Language Agents for Recommendation
Taicheng Guo, Chaochun Liu, Hai Wang +5
Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not underst…