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Showing papers from Zhejiang Lab Show all papers

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G-PAC: Constructing Cohesive Pseudo-Features for Generalizable Physical Adversarial Camouflage

tianrui lou, Haoqing Zhang, Jiawei Liang, Puning Zhao and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
45%Niche pick
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ALAM: Algebraically Consistent Latent Transitions for Vision-Language-Action Models

Zuojin Tang, Haoyun Liu, Xinyuan Chang, Changjie Wu and 10 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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Auto-Annotation with Expert-Crafted Guidelines: A Study through 3D LiDAR Detection Benchmark

Yechi Ma, Wei Hua, Shu Kong

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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No Free Alignment: Observability-Aware Alignment for Multimodal Heterogeneous Learning

Canran Xiao, Puning Zhao, Enneng Yang, XIAOCHUN CAO and 2 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
45%Niche pick
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From Structural Feedback to Prompt Policies: Learning Faithful Text-to-Image Prompt Editors

Wentao Ye, Yali Ye, Zhiqing Xiao, Ru Peng and 6 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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lenient 0/5
medium 0/10
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80%Must read
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Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement

A Riemannian ODE framework shows adaptive optimizers dampen flat directions too conservatively, and LITE accelerates Muon and SOAP by boosting flat-direction updates, cutting LLM pre-training time.

Shuchen Zhu, Rizhen Hu, Mingze Wang, Mou Sun and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5
91%Must read
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LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments

LITMUS benchmarks LLM agent behavioral jailbreaks in real OS environments, revealing agents execute 40.64% of high-risk operations despite refusals and suffer pervasive execution hallucination.

Chiyu Zhang, Huiqin Yang, Bendong Jiang, Xiaolei Zhang and 7 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 4/5