Good Papers

Showing papers from Eastern Institute of Technology, Ningbo Show all papers

45%Niche pick
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DASS: A Solver-Agnostic Dynamic Auxiliary Search Strategy for Symbolic Regression

Hu, Qian Li, Ding Wang, Yekun Zheng and 2 more

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

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0/20 AI panelreviewers recommend it

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
67%Highly rated
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Backdoor Attacks under Lossy Compression: From Failure to Reactivation and Adaptation

Qian Li, Yunuo Chen, Yuntian Chen

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

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2/20 AI panelreviewers recommend it

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AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 1/5
71%Highly rated
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DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning

DeformGen uses dynamics-based topological augmentation to generate diverse deformable object states and warp trajectories for improved manipulation policy learning.

Zili Lin, Wenyao Zhang, Yuyang Zhang, Zekun Qi 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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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 1/5
86%Must read
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Adaptive Mass-Segmented KV Compression for Long-Context Reasoning

AMS replaces global token eviction with adaptive region-aware KV quotas to prevent reasoning block wipe-out, boosting long-context performance without extra attention overhead.

Junzhe Yang, Xiaoyu Shen

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · 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 9/10
strict 0/5
83%Must read
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Generation Navigator: A State-Aware Agentic Framework for Image Generation

Generation Navigator is a state-aware multi-turn text-to-image agent that learns to steer generation via trajectory-level reinforcement learning, achieving a 0.90 WISE score and 79.06% reasoning accuracy.

Jinming Liu, Ruoyu Feng, Yuqi Wang, Wenjun Zeng 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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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
78%Highly rated
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Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment

PILA injects physics-structured latent guidance into frozen video generators via mixture-of-experts alignment, achieving state-of-the-art physical plausibility and visual quality.

Cong Wang, Hanxin Zhu, Jiayi Luo, Yonglin Tian and 5 more

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

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 0/5