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Showing papers from South China Normal University Show all papers

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SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection

SAHG detects LLM-driven social bots by applying direction-dependent hyperbolic curvature and dual-channel feature fusion, achieving top accuracy and F1 across three benchmarks.

Hanning Lu, Yingguang Yang, Jinwei Su, Yang; Liu and 7 more

Published May 28, 2026 · 0 citations

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
45%Niche pick
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SynMQG: Disentanglement and Mutual-Information Optimization for Synergistic Multi-modal Question Generation

Junjie Zhang, ShengYong Ding, Shuangyin Li

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

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medium 0/10
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45%Niche pick
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AVID: A 5T fMRI Dataset for Benchmarking Auditory-induced Visual Mental Imagery Decoding

Shiqi Shen, Shurui Li, Yuanning Li, Xilin Zhang

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

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57%Worth a look
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BayesJudge: Uncertainty-Aware Bayesian Meta-Evaluation of Human and LLM Judgments

Jiahao Zhang, Pengbin Feng, Chunlei Meng, Hang He and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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
45%Niche pick
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MUST: Stage-Adaptive Stability Control for Test-Time Scaling in Multimodal Reasoning

YunFeng Deng

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

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57%Worth a look
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Revisiting Autoregressive GCNs for Vehicle Routing Problems

Zhipeng Zhong, Junquan Huang, Yu Huang, Boyuan Zheng and 5 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
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DetectViT: Test-time Backdoor Detection for Vision Transformers via Inter-Head Attention Discrepancy

Siquan Huang, Yijiang Li, Xingfu Yan, Ningzhi Gao 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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AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
57%Worth a look
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CoTrek: Toward Scalable On-Policy Distillation for Long Chain-of-Thought Reasoning

Heng Zhang, Chengyu Zhou, Jiajun Wu, Estella Liu and 7 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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PolyMind: Exploring Width Scaling for Reflective Reasoning in Language Agents

Heng Zhang, Chengyu Zhou, Jiajun Wu, Estella Liu and 7 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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LongBanana: An Expert-Verified Benchmark for Long-Context Multi-Reference Image Synthesis

Haoxiang Cao, Yuxuan Zhang, Penghui Du, Bo Li and 15 more

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

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lenient 1/5
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86%Must read
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Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback

Structured Defect Grounding models text-to-image failures as structured tuples for diagnosis and alignment, outperforming proprietary vision-language models and improving generation via importance-weighted rewards.

Huaisong Zhang, Hao Yu, Yuxuan Zhang, Jiahe Wang and 6 more

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

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5
83%Must read
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StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training

StableVQ decouples encoder-decoder and codebook training via Dynamic STE, Region VQ Loss, and independent schedules to stabilize VQ tokenizers and boost utilization and reconstruction.

Bao Tang, Jiahao Guo, Haoxiang Cao, Wenyu Liu and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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
76%Highly rated
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The Spectral Amplitude Principle for Dynamics of Quantum Neural Networks

Quantum neural networks follow spectral amplitude priority rather than frequency bias, enabling efficient high-frequency learning via large-amplitude components and outperforming classical networks.

Yihang Xu, Dan-Bo Zhang, Junchi Yan

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

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5