4 citations · 10 across the 7 of their papers we have counts for
7 papers
Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling
Shaofeng Zhang, Hongyuan Su, Qingwen Peng +4
Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations. While recent works have leveraged LLMs to automat…
AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery
Yu Li, Chenyang Shao, Xinyang Liu +13
Artificial intelligence research increasingly depends on prolonged cycles of reproduction, debugging, and iterative refinement to achieve State-Of-The-Art (SOTA) performance, creat…
ContextEvolve: Multi-Agent Context Compression for Systems Code Optimization
Hongyuan Su, Yu Zheng, Yong Li
Large language models are transforming systems research by automating the discovery of performance-critical algorithms for computer systems. Despite plausible codes generated by LL…
Understanding World or Predicting Future? A Comprehensive Survey of World Models
Jingtao Ding, Yunke Zhang, Yu Shang +12
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which…
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper,…
Rumor Mitigation in Social Media Platforms with Deep Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread i…
MetroGNN: Metro Network Expansion with Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansi…