activity
20242026
most citedPromoting Generalization for Exact Solvers via Adversarial Instance Augmentation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

11 papers

cs.CL2026

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…

cs.LG20261 cited

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…

cs.AR2025

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…

cs.RO2025

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…

cs.LG2025

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…

cs.AI2025

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…