6 citations · 6 across the 10 of their papers we have counts for
10 papers
Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models
Rui Zhu, Minglong Cao, Chenyu Zhou +2
Modern large language models (LLMs) can translate natural-language descriptions into operations research (OR) formulations. Post-training techniques including reinforcement learnin…
Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
Sihan Ge, Yichen Lin, Chenyu Zhou +3
Large language models (LLMs) are increasingly used to formulate optimization models from natural-language problem descriptions, yet realistic operations research (OR) requests are…
GraphBU: MILP Instance Generation with Graph-Native Block Units
Xiaolei Guo, Chenyu Zhou, Jianghao Lin +1
Mixed-integer linear programming (MILP) instances used for solver development are hard to obtain when models come from private or application-specific pipelines. A generator must k…
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
Chenyu Huang, Jianghao Lin, Zhengyang Tang +4
We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based e…
OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents
Chenyu Zhou, Xinyun Lu, Jiangyue Zhao +3
Large language model (LLM) agents are increasingly used to assist with operations research (OR) modeling, yet existing OR-oriented benchmarks often reduce evaluation to one-shot tr…
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…
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
Chenyu Zhou, Huacan Chai, Wenteng Chen +18
Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the…
MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
Shuhuai Li, Jianghao Lin, Dongdong Ge +1
Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to…
StepORLM: A Self-Evolving Framework With Generative Process Supervision For Operations Research Language Models
Chenyu Zhou, Tianyi Xu, Jianghao Lin +1
Large Language Models (LLMs) have shown promising capabilities for solving Operations Research (OR) problems. While reinforcement learning serves as a powerful paradigm for LLM tra…
FMIP: Joint Continuous-Integer Flow For Mixed-Integer Linear Programming
Hongpei Li, Hui Yuan, Han Zhang +4
Mixed-Integer Linear Programming (MILP) is a foundational tool for complex decision-making problems. However, the NP-hard nature of MILP presents a significant computational challe…