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Showing papers from University of North Carolina at Chapel Hill Show all papers

57%Worth a look
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UMAS: System-Level Uncertainty Quantification for Multi-Agent LLM Systems

Hanwen Li, Jinhao Duan, Xiaoshuang Shi, Yue Zhang and 3 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
69%Highly rated
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Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness

Songyuan Sui, Zhen Tan, Mohan Zhang, Rana M Khan and 2 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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3/20 AI panelreviewers recommend it

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AI panel: 3 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 1/5
69%Highly rated
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Guarding the Life Code: Preserving Membership Privacy in Genomic Foundation Models

Xinyu Zhao, Jinhao Duan, Zhen Tan, Tianlong Chen

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 3 of 20 reviewers recommend it
lenient 2/5
medium 1/10
strict 0/5
45%Niche pick
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Words Before Pixels: Selective Modality Routing for Vision-Language Model Unlearning

Laura Yao, Haochen Zhang, Jinhao Duan, Sijia Liu and 1 more

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

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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Rethinking Geometric Depth in Monocular 3D Object Detection: A Projection-Consistent Reformulation

Zhihao Zhang, Abhinav Kumar, Huaizhi Qu, Tianlong Chen and 1 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
86%Must read
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RefineAny3D: Depth Refinement as Semantic Alignment for Monocular 3D Detection

RefineAny3D treats monocular 3D depth refinement as visual alignment via categorical vision-language action tokens, boosting detectors without numerical regression.

Zhihao Zhang, Gengwei Zhang, Tianlong Chen, Xiaoming Liu

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 2/5