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Showing papers from SIGS, Tsinghua University Show all papers

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NeurIPS 2026TsinghuaHarbin Institute of TechnologyShenzhenShenzhen TechnologySIGS, TsinghuaVision-language models

What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective

Jiazhen Huang, Xiao Chen, Zhiming Liu, Yaru Sun and 2 more

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

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45%Niche pick
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NeurIPS 2026TsinghuaXiamenShanghai Jiao TongShenzhen TechnologySIGS, Tsinghua3D vision & reconstruction

EIHMR: Collaborative Human-Camera Estimation for Global Human Mesh Recovery

Junchen Ge, Zhengqi Zhang, Hanglei Jin, Shuzhao Xie and 4 more

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

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NeurIPS 2026TsinghuaETH ZurichZhejiangU ArizonaSIGS, TsinghuaLLM agents & planning

Treat Domain-Specific Languages as Design Variables in LLM Agents

Letian Li, Wenyuan Jiang, Xin Yang, Shuzhao Xie and 4 more

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

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NeurIPS 2026Tsinghua University, TsinghuaTsinghuaSouthern University of Science aSIGS, TsinghuaRobot manipulation

GeoMIND: A Benchmark for Spatial Understanding in Robotic Manipulation

Jinghe Wang, Xinrui Cao, Duo Wu, Chenghao Gu 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 2026Nanyang TechnologicalSouthern University of Science aTsinghuaSIGS, TsinghuaAumovio SingaporeTest-time compute

DAWN: Dependency-Aware Fast Inference for Diffusion LLMs

DAWN extracts token dependency graphs to select reliable unmasking positions, accelerating diffusion LLM inference by 1.80-8.06x with negligible quality loss.

Lizhuo Luo, Zhuoran Shi, Jiajun Luo, Zhi Wang and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 12

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 10/10
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89%Must read
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NeurIPS 2026Tsinghua University, TsinghuaTsinghuaXidianU Illinois Urbana-ChampaignYuanXingGuangNianVision-language-action models

ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models

ElegantVLA accelerates vision-language-action models via adaptive compute scheduling, achieving up to 3.77x speedup and doubling control frequency without retraining.

Ye Li, Huanan Liu, Kangye Ji, Yuan Meng and 6 more

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

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 1/5