Good Papers

Showing papers from CUHK Show all papers

57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Southern University of Science aHKUSTCUHKWeChat Security AI TeamHong Kong University of Science Video understanding

FrameScout: Scouting Query-Relevant Frames for Long Video Understanding

Haonan Hu, Shuhao Chen, Weisen Jiang, Lizhao Gao and 2 more

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Department of Computer Science aCUHKOptimization

Multi-Nonsmooth-Nonconvex-Objective Optimization

Ru Wang, Chengchang Liu

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

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026CUHKHKUTsinghua universityThe University of Hong KongDepartment of Computer Science aRL for LLMs

Fast-RL: Accelerating Reinforcement Learning for LongCoT Reasoning Models

Sitong Wu, Haoru Tan, bin xia, Bei Yu and 2 more

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026CUHKHKUThe University of Hong KongDepartment of Computer Science aRL for LLMs

Historical Relative Policy Optimization for Bootstrapping LLM Reasoning

Sitong Wu, Haoru Tan, Bei Yu, Xiaojuan Qi and 1 more

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
72%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026The Chinese University of Hong KMax-Planck-Institute for IntelliWestlakeMax Planck Institute for IntelliCUHKLLM pretraining & scaling laws

Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation

Pion updates weight matrices via orthogonal equivalence transformations that preserve singular values during LLM training, offering a stable, competitive optimizer alternative.

Kexuan Shi, Hanxuan Li, Zeju Qiu, Yandong Wen and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 5 on Hugging Face · Code ★ 39

– ReadersNo votes yet
8/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 8 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 1/5
71%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026The Chinese University of Hong KCUHKTencent Quantum LabChinese University of Hong KongOptimization

Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise

Quantum estimators for heavy-tailed noise enable QNSGD and QPSGD to find ε-stationary or optimal solutions with poly(√d, ε) oracle queries, beating classical lower bounds in low dimensions.

Bin Luo, Chengchang Liu, Jonathan Allcock, Shengyu Zhang and 1 more

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

– ReadersNo votes yet
6/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 6 of 20 reviewers recommend it
lenient 2/5
medium 3/10
strict 1/5
74%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026The Chinese University of Hong KU British ColumbiaCUHKOptimization

Last-Iterate Convergence of Single-Loop Stochastic Methods for Constrained Convex-Concave Minimax Problems

Single-loop perturbed stochastic extragradient and optimistic gradient methods achieve last-iterate convergence for constrained convex-concave minimax problems with O(T^{-1/4}) and O(T^{-1/5}) rates.

Taoli Zheng, Jiajin Li, Anthony Man-Cho So

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

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 2/5