activity
20202026
most citedOn the Convergence of Decentralized Adaptive Gradient Methods

5 citations · 8 across the 15 of their papers we have counts for

collaborators

20 papers

cs.IR2026

A Versioned Unified Graph Index for Dynamic Timestamp-Aware Nearest Neighbor Search

Jun Woo Chung, Weijie Zhao

We present TiGER (Time-Integrated Graph for Efficient Retrieval), a novel approach for performing fast time-aware approximate nearest neighbor searches on dynamic vector datasets w…

cs.LG2026

Nexusformer: Nonlinear Attention Expansion for Stable and Inheritable Transformer Scaling

Weijie Zhao, Mingquan Liu, Bolun Wang +4

Scaling Transformers typically necessitates training larger models from scratch, as standard architectures struggle to expand without discarding learned representations. We identif…

cs.LG2026

Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning

Ziwen Liu, Huawei Lin, Yide Ran +5

Large language models (LLMs) sometimes memorize undesirable knowledge, which must be removed after deployment. Prior work on machine unlearning has focused largely on optimization…

cs.AI2025

Robust Watermarking on Gradient Boosting Decision Trees

Jun Woo Chung, Yingjie Lao, Weijie Zhao

Gradient Boosting Decision Trees (GBDTs) are widely used in industry and academia for their high accuracy and efficiency, particularly on structured data. However, watermarking GBD…

cs.AI2025

Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything

Huawei Lin, Yunzhi Shi, Tong Geng +3

Multimodal large language models (MLLMs) have shown strong capabilities but remain limited to fixed modality pairs and require costly fine-tuning with large aligned datasets. Build…

cs.IR2025

Automating Financial Statement Audits with Large Language Models

Rushi Wang, Jiateng Liu, Weijie Zhao +2

Financial statement auditing is essential for stakeholders to understand a company's financial health, yet current manual processes are inefficient and error-prone. Even with exten…