18 citations · 27 across the 4 of their papers we have counts for
6 papers
LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization
Shihao Zhang, Weiting Liu, Siyu Shao +4
Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are predominantly evaluated on s…
OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling
Yitian Chen, Cheng Cheng, Yinan Sun +2
We investigate the capabilities and scalability of Large Language Models (LLMs) in optimization modeling, a domain requiring structured reasoning and precise formulation. To this e…
Auto-Formulating Dynamic Programming Problems with Large Language Models
Chenyu Zhou, Jingyuan Yang, Linwei Xin +3
Dynamic programming (DP) is a fundamental method in operations research, but formulating DP models has traditionally required expert knowledge of both the problem context and DP te…
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling
Yitian Chen, Jingfan Xia, Siyu Shao +2
Optimization modeling is fundamental to decision-making across diverse domains. Despite progress in automating optimization formulation from natural language descriptions, Large La…
GP-NAS-ensemble: a model for NAS Performance Prediction
Kunlong Chen, Liu Yang, Yitian Chen +3
It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot o…
Probabilistic Forecasting with Temporal Convolutional Neural Network
Yitian Chen, Yanfei Kang, Yixiong Chen +1
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probab…