Good Papers

Showing papers from Jiangnan University Show all papers

57%Worth a look
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NeurIPS 2026SUN YAT-SEN UNIVERSITYTencent AI for Life Sciences LabTencentTsinghua University, TsinghuaThe Hong Kong University of ScieGenomics & single-cell

WaveSem: Frequency-Adaptive Tokenization for Disentangling Semantics and Noise in Genomics

Shou Z Chen, Bing He, Zhenchao Tang, Jun Zhu and 8 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
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NeurIPS 2026Lenovo ResearchJiangnanLenovo Group LimitedTianjinTsinghua University, TsinghuaLLM agents & planning

Lemon: Evidence-Risk-Aware Adaptive Organization for Long-Horizon LLM Agents

Zimo Yin, Haipeng Jiang, Kailong Ren, Zhetao Sun and 7 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
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NeurIPS 2026JiangnanU Science and Technology of ChinJIANGNAN UNIVERSITYAdversarial robustness

Prompt Ensemble Image Purification for Test-time Adversarial Robustness of CLIP

Yuxuan Zhang, Shuchang Wang, Zhenbo Shi, Xiaoman Liu 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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AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
67%Highly rated
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NeurIPS 2026Nanjing University of Posts and JiangnanShandongVision-language models

Test-Time Adaptation via Self-Reinforced Optimal Transport for Zero-Shot OOD Detection with Vision–Language Models

MingCai Chen, Heng-yang Lu, Yuntao Du, Baoming Zhang and 1 more

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

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AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
89%Must read
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NeurIPS 2026TencentHarbin Institute of TechnologyHong Kong PolytechnicWestlakeJiangnanAlignment & preference optimization

Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning

Balanced Fine-Tuning uses dual-scale token and sequence reweighting targeting dense epistemic uncertainty to align LLMs with biomedical knowledge, improving reasoning and sparse-reward RL over standard fine-tuning.

Zhenchao Tang, Fang Wang, Haohuai He, Jiale Zhou and 12 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 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 9/10
strict 2/5