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Showing papers from Peking University, Tsinghua University Show all papers

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NeurIPS 2026Shanghai JiaotongPeking University, TsinghuaByteDance Inc.SenseTimeBeijing University of AeronauticQuantization

BMAttn: Block-Aligned Mixed-Precision Attention Quantization for LLM Inference

Zining Wang, Haojie Duanmu, Zhihang Yuan, Fanliu Kong 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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medium 0/10
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67%Highly rated
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NeurIPS 2026PekingMichigan StateSoutheast University / Tongyi LaU Hong KongZero Gravity LabsAgent benchmarks & environments

SaaS-Bench: Can Computer-Use Agents Leverage Real-World SaaS to Solve Professional Workflows?

Kean Shi, Zihang Li, Tianyi Ma, Zengji Tu and 11 more

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

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lenient 2/5
medium 0/10
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45%Niche pick
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NeurIPS 2026Beijing University of Posts and Peking University, TsinghuaPekingAlibaba GroupMeituanKnowledge editing

DynEdit: Dynamic Entropy-Guided Sequential Editing for Large Language Models

Jinhu Fu, Yan Bai, Yihang Lou, Li 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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83%Must read
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NeurIPS 2026TsinghuaPeking University, TsinghuaPekingCentral SouthInstitute of automation, ChineseMultimodal reasoning

Unveiling Fine-Grained Visual Traces: Evaluating MultiModal Interleaved Reasoning Chains in Multimodal STEM Tasks

StepSTEM introduces 283 graduate-level STEM problems with strictly complementary visual-textual inputs to evaluate cross-modal reasoning via step-level alignment, revealing current MLLMs achieve only 38.29% accuracy due to heavy reliance on text.

Jing Jin, Hao Liu, Yan Bai, Yihang Lou and 8 more

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

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
83%Must read
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NeurIPS 2026Beijing University of AeronauticBeihangNorthwest Polytechnical UniversiU Chinese Academy of SciencesAlibaba GroupWeb & GUI agents

MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models

MIRAGE learns continuous latent reasoning for mobile agents, cutting decoded tokens 75% while matching explicit chain-of-thought accuracy and improving baselines up to 10.2 points via generative world modeling.

Zhichao Yang, Yuanze Hu, Haojie Hao, Longkun Hao 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: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 0/5