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Showing papers from City University of Hong Kong (CityUHK) Show all papers

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NeurIPS 2026Beijing Institute of TechnologyCity University of Hong KongSun Yat-SenWuhanShanghaiMixture of experts

LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing

LightMoE replaces redundant MoE experts with parameter-efficient modules to cut memory use 30, 50% while matching or beating existing compression methods.

Jiawei Hao, Zhiwei Hao, Jianyuan Guo, Li Shen 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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lenient 5/5
medium 6/10
strict 0/5
86%Must read
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NeurIPS 2026City University of Hong KongU SydneySoutheastHuawei Noah's Ark LabCity University of Hong KongMultimodal hallucination

Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking

Causal-Invariant Masking decomposes MLLM uncertainty via semantic divergence to capture epistemic limitations, and Expected Embedding Drift accelerates quantification by nearly 50%.

Haoyang Luo, Linwei Tao, Jie Gui, Xinghao Chen and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · 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 5/5
medium 9/10
strict 0/5