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Showing papers from Macquarie University Show all papers

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NeurIPS 2026The University of SydneyU Southern QueenslandMacquarieU OsloMacquarie University, AustraliaRL for LLMs

PRISM:Disentangling Preference Distributions for Generative Ranking

zhangkai wu, Kaize Shi, Xu Zhang, Zhihong Cui and 1 more

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

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NeurIPS 2026U Science and Technology BeijingU New South WalesHarbin Institute of TechnologyUniversity Of New South WalesMacquarieTest-time compute

Test-Time Prompt-Agnostic Decomposition

Junze Wang, Lei Fan, Dezheng Zhang, Donglin Di and 3 more

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

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NeurIPS 2026U MacquaireMacquarieMachine unlearning

Hyperspherical Local Margin Retraction for Zero-Shot Instance-Wise Machine Unlearning

Hongyi Lyu, Xuyun Zhang, Xiaoxiao Chi, Guanfeng Liu and 1 more

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

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NeurIPS 2026Chengdu University of InformatioMacquarieCentral SouthCentral South University, ChinaShanghai JiaotongKnowledge distillation

Temporal Prototype Alignment for Frozen-Feature Dataset Distillation

Rui Ding, Jing Hu, Mei Chen, Xi Wu and 4 more

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

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NeurIPS 2026MacquarieMacquarie University, AustraliaThe University of SydneySpeech synthesis

DUET: Unified Dual-Space Emotion Control for Diffusion and Flow-Matching Driven Text-to-Speech

DUET enables plug-and-play emotion control for pretrained diffusion and flow-matching TTS by steering hidden states and guiding mel-spectra via a differentiable vocoder, surpassing supervised emotional baselines.

Xu Zhang, Longbing Cao, zhangkai wu

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

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AI panel: 18 of 20 reviewers recommend it
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78%Highly rated
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NeurIPS 2026WuhanShanghai Jiao TongMacquarieHong Kong University of Science Huazhong University of Science aEfficient inference & serving

Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management

PBKV predicts future agent invocations in dynamic LLM workflows to manage KV-cache reuse, achieving up to 1.85x speedup over LRU.

Haoyu Zheng, Fangcheng Fu, Jia Wu, Binhang Yuan and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 11 of 20 reviewers recommend it
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