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Showing papers from Tsinghua Shenzhen International Graduate School Show all papers

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NeurIPS 2026TsinghuaTsinghua University, TsinghuaGuangdong OceanTsinghua Shenzhen International New York University, ShanghaiAlignment & preference optimization

Grassmannian Geodesic Steering: Rank-Preserving Subspace Control for Inference-Time Alignment of Language Models

Longyi Liu, Zhitao Wang, Jianchao Yu, Mingrui Cai and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Chinese University of Hong KU SydneyTsinghua Shenzhen International Shanghai AI LabDiffusion models

VUM: Visual Unified Models for Image Generation and Perception

ZiDong Wang, Yiyuan Zhang, Xiaoyu Yue, Xiangyuan Xue and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tsinghua Shenzhen International TsinghuaNanyang TechnologicalGreat BayShenzhenSemi- & weakly supervised learning

View Confidence Perception-Driven Incremental Prediction for Incomplete Multi-view Multi-label Learning

Pingzhu Liu, Chunming He, Zunnan Xu, Zhirui Fang and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Chinese University of Hong KTsinghua Shenzhen International University College London, UniveUniversity College LondonNanjingUnified multimodal models

Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology

Meow-Omni 1 integrates video, audio, physiological time-series, and text to reach 71.16% feline intent recognition, outperforming baseline multimodal models.

Jucheng Hu, Zhangquan Chen, Yulin Chen, Chengjie Hong and 8 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face

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NeurIPS 2026ZhejiangChinese University of Hong KongFudan UnivrsityTsinghua Shenzhen International Tencent HunyuanVisual grounding

Evidence-RL: Towards Evidence-intensive Visual Reasoning

Counterfactual Evidence Disentanglement (CED) audits vision-language model grounding by comparing evidence-region and non-evidence-region support drops inside GRPO, improving visual reasoning across benchmarks without inference overhead or evidence annotations.

Haojie Huang, Xinlei Yu, Chengming Xu, Zhangquan Chen and 5 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 16 on Hugging Face · Code ★ 4

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AI panel: 15 of 20 reviewers recommend it
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