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Kai Han

ACL 2026KDD 2026NeurIPS 2026SIGIR 2026效率与系统多模态评测与基准代码可解释性
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0 一作 · 4 篇 · ACL 2026 / KDD 2026 / NeurIPS 2026 / SIGIR 2026

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2026 顶会论文

4
作者位次 #5/6ACL 2026Findings· Findings of ACL

Mitigating Coordinate Prediction Bias from Positional Encoding Failures

Xingjian Tao · Yiwei Wang · Yujun Cai · Yihong Luo · Kai Han · Jing Tang

摘要

While Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, precise coordinate prediction remains a significant challenge, particularly as high-resolution inputs cause visual positional encodings (VPEs) to degrade. We demonstrate that these encoding failures do not resu...

多模态评测与基准效率与系统OpenReview 项目页
作者位次 #6/7SIGIR 2026Oral· Information Retrieval (Full)

Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation

Yifan Song · Fenglin Yu · Yihong Luo · Xingjian Tao · Siya Qiu · Kai Han · Jing Tang

摘要

Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learning systems (e.g., GNNs). Existing solutions typically rely on diffusion-based structural smoothing (e.g., feature pr...

效率与系统代码可解释性OpenReview

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