3 citations · 15 across the 15 of their papers we have counts for
18 papers
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…
Piano: A Multi-Constraint Pin Assignment-Aware Floorplanner
Zhexuan Xu, Kexin Zhou, Jie Wang +5
Floorplanning is a critical step in VLSI physical design, increasingly complicated by modern constraints such as fixed-outline requirements, whitespace removal, and the presence of…
One Step Beyond: Feedthrough & Placement-Aware Rectilinear Floorplanner
Zhexuan Xu, Jie Wang, Siyuan Xu +3
Floorplanning determines the shapes and locations of modules on a chip canvas and plays a critical role in optimizing the chip's Power, Performance, and Area (PPA) metrics. However…
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…
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…
MILP-StuDio: MILP Instance Generation via Block Structure Decomposition
Haoyang Liu, Jie Wang, Wanbo Zhang +6
Mixed-integer linear programming (MILP) is one of the most popular mathematical formulations with numerous applications. In practice, improving the performance of MILP solvers ofte…
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
Qitan Lv, Jie Wang, Hanzhu Chen +3
Generation of plausible but incorrect factual information, often termed hallucination, has attracted significant research interest. Retrieval-augmented language model (RALM) -- whi…
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…
ROPO: Robust Preference Optimization for Large Language Models
Xize Liang, Chao Chen, Shuang Qiu +6
Preference alignment is pivotal for empowering large language models (LLMs) to generate helpful and harmless responses. However, the performance of preference alignment is highly s…
Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Jie Wang, Zhihai Wang, Xijun Li +7
Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends…
State Sequences Prediction via Fourier Transform for Representation Learning
Mingxuan Ye, Yufei Kuang, Jie Wang +4
While deep reinforcement learning (RL) has been demonstrated effective in solving complex control tasks, sample efficiency remains a key challenge due to the large amounts of data…
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning
Yufei Kuang, Xijun Li, Jie Wang +7
Large-scale LP problems from industry usually contain much redundancy that severely hurts the efficiency and reliability of solving LPs, making presolve (i.e., the problem simplifi…
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation
Haoyang Liu, Yufei Kuang, Jie Wang +3
Machine learning has been successfully applied to improve the efficiency of Mixed-Integer Linear Programming (MILP) solvers. However, the learning-based solvers often suffer from s…
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
Zijie Geng, Xijun Li, Jie Wang +3
In the past few years, there has been an explosive surge in the use of machine learning (ML) techniques to address combinatorial optimization (CO) problems, especially mixed-intege…
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias
Zhihao Shi, Jie Wang, Fanghua Lu +5
Node representation learning on attributed graphs -- whose nodes are associated with rich attributes (e.g., texts and protein sequences) -- plays a crucial role in many important d…
Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction
Jie Wang, Hanzhu Chen, Qitan Lv +7
Inductive link prediction -- where entities during training and inference stages can be different -- has shown great potential for completing evolving knowledge graphs in an entity…
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…