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Showing papers from University of Queensland Show all papers

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NeurIPS 2026U QueenslandU MelbourneU California, BerkeleyUC BerkeleyDeep learning theory

Why Heavy-Tailed Weights Predict Model Quality

Joseph Wilson, Chris van der Heide, Liam Hodgkinson, Zhichao Wang 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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NeurIPS 2026TongjiIEIT SYSTEMS (Beijing) Co., Ltd.U QueenslandIEIT SYSTEMS CO.,LTDNeusoft CorporationRobot manipulation

UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception

Linfei Li, Ruining Hu, Lin Zhang, Zhong Wang and 4 more

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

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57%Worth a look
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NeurIPS 2026U QueenslandPohang University of Science andThe University of QueenslandChung-AngMultimodal reasoning

SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark

Taeyoung Yu, Seonbin Jo, Jiwon Kim, Junyoung Byun

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

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AI panel: 1 of 20 reviewers recommend it
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71%Highly rated
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NeurIPS 2026The University of QueenslandU QueenslandRL for LLMs

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models

Block-R1 reveals domain-level block-size conflicts in diffusion LLM reinforcement learning and proposes sample-level block sizing for cross-domain post-training.

Yan Jiang, Ruihong Qiu, Zi Huang

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

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AI panel: 7 of 20 reviewers recommend it
lenient 1/5
medium 6/10
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76%Highly rated
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NeurIPS 2026The University of QueenslandSimon FraserTaobao (China) Software Co., LtdTaobao & Tmall Group of AlibAlibaba GroupVision-language models

AdaM-Rec: Adaptive Modality Routing for Multimodal Recommendation

AdaM-Rec adaptively routes between textual and visual modalities via LLM-based proxy recall tasks to improve multimodal recommendation accuracy.

Honghao Fu, Jiacheng Chen, Manxi Lin, Junjun Zheng and 6 more

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

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AI panel: 10 of 20 reviewers recommend it
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91%Must read
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NeurIPS 2026The University of QueenslandMonashU QueenslandMachine unlearning

Open Vocabulary Domain Unlearning

Existing domain unlearning overfits to seen classes; this paper proposes open-vocabulary domain unlearning via Fisher-masked parameter editing and targeted manifold scattering to erase domains across unseen classes with few shots.

Sumanth V Udupa, Mehrtash Harandi, Yadan Luo, Mahsa Baktashmotlagh

Sydney Poster Session 6, Thu, Dec 10, 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 10/10
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78%Highly rated
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NeurIPS 2026The University of QueenslandProfenso Pty LtdQIMR Berghofer Medical Research U QueenslandSparse autoencoders

The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws

Sparse autoencoder scaling varies by layer because curved activation manifolds with varying intrinsic dimensions impose geometry-dependent reconstruction walls rather than universal linear scaling laws.

Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 3/5
medium 6/10
strict 2/5