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Showing papers from Universiti Malaya Show all papers

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Geometry-Aware Subspace Perturbation for Heterogeneous Federated Learning

Xiangtao Zhang, Hailong Yan, Obed Irihose, Joey Tianyi Zhou 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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45%Niche pick
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RECAP: Looking Once Is Not Enough for Vision-Language Reasoning

Zhaolu Kang, Tailong Luo, Chenxin Li, Zhenyu Yu and 12 more

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

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67%Highly rated
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Task-Aware KV Cache Compression for LLM Agents via Utility-Driven Step Pruning

Yusen Wu, Yefan Wang, Jia Yee Tan, Guangyuan Dong and 5 more

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

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2/20 AI panelreviewers recommend it

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AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
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91%Must read
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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission

ChemGuard exposes that chemistry-aware admission invalidates many molecular graph backdoors, but ChemBack achieves high attack success with fully admitted poisons via chemically feasible motif-anchor attachments.

Thinh Nguyen, Sze Jue Yang, Khoa D Doan, Chee Seng Chan 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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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 3/5
83%Must read
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GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning

GeoSym Engine automates symbolically-verifiable geometric reasoning data synthesis, and models trained on GeoSym127K achieve large gains on diagram-dependent geometry benchmarks.

Jinhao Jing, Zheng Ma, Jinwei Liang, Qiannian Zhao and 8 more

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

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 0/5
74%Highly rated
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Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model

Evo-Depth is a lightweight 0.9-billion-parameter vision-language-action model using implicit depth encoding from RGB to improve spatial manipulation without extra sensors. It achieves top benchmark performance with minimal GPU memory and highest inference speed among compared methods.

Tao Lin, Yuxin Du, Jiting Liu, Nuobei Zhu and 13 more

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

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5