11 citations · 14 across the 15 of their papers we have counts for
16 papers
Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
Haoyang Liu, Jie Wang, Boxuan Niu +8
Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements have utilized large language m…
Short Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimization
Runquan Gui, Jie Wang, Zhihai Wang +3
While Large Reasoning Models (LRMs) have demonstrated impressive capabilities in solving complex tasks through the generation of long reasoning chains, this reliance on verbose gen…
Why Attention Patterns Exist: A Unifying Temporal Perspective Analysis
Qingyue Yang, Jie Wang, Xing Li +6
Attention patterns play a crucial role in both training and inference of large language models (LLMs). Prior works have identified individual patterns such as retrieval heads, sink…
OptiTree: Hierarchical Thoughts Generation with Tree Search for LLM Optimization Modeling
Haoyang Liu, Jie Wang, Yuyang Cai +3
Optimization modeling is one of the most crucial but technical parts of operations research (OR). To automate the modeling process, existing works have leveraged large language mod…
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking
Runquan Gui, Zhihai Wang, Jie Wang +7
Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathe…
Accelerating Large Language Model Reasoning via Speculative Search
Zhihai Wang, Jie Wang, Jilai Pan +5
Tree-search-based reasoning methods have significantly enhanced the reasoning capability of large language models (LLMs) by facilitating the exploration of multiple intermediate re…
Advancing Symbolic Discovery on Unsupervised Data: A Pre-training Framework for Non-degenerate Implicit Equation Discovery
Kuang Yufei, Wang Jie, Huang Haotong +5
Symbolic regression (SR) -- which learns symbolic equations to describe the underlying relation from input-output pairs -- is widely used for scientific discovery. However, a rich…
Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming
Haoyang Liu, Jie Wang, Zijie Geng +5
Leveraging machine learning (ML) to predict an initial solution for mixed-integer linear programming (MILP) has gained considerable popularity in recent years. These methods predic…
AttentionPredictor: Temporal Patterns Matter for KV Cache Compression
Qingyue Yang, Jie Wang, Xing Li +8
With the development of large language models (LLMs), efficient inference through Key-Value (KV) cache compression has attracted considerable attention, especially for long-context…
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation
Xinyi Zhou, Xing Li, Yingzhao Lian +6
We introduce SeaDAG, a semi-autoregressive diffusion model for conditional generation of Directed Acyclic Graphs (DAGs). Considering their inherent layer-wise structure, we simulat…
Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms
Zhihai Wang, Zijie Geng, Zhaojie Tu +12
The increasing complexity of modern very-large-scale integration (VLSI) design highlights the significance of Electronic Design Automation (EDA) technologies. Chip placement is a c…
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu +7
Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. Th…
PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling
Ruizhe Zhong, Junjie Ye, Zhentao Tang +4
Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design. It involves directly estimating the timing metrics…
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
Xijun Li, Fangzhou Zhu, Hui-Ling Zhen +23
In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the inte…
LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation
Ruizhe Zhong, Xingbo Du, Shixiong Kai +7
Driven by Moore's Law, the complexity and scale of modern chip design are increasing rapidly. Electronic Design Automation (EDA) has been widely applied to address the challenges e…
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
Zhihai Wang, Lei Chen, Jie Wang +7
Logic Synthesis (LS) plays a vital role in chip design -- a cornerstone of the semiconductor industry. A key task in LS is to transform circuits -- modeled by directed acyclic grap…