Good Papers

Showing papers from Institute of Software Chinese Academy of Sciences Show all papers

45%Niche pick
?Niche pickVote to see the score

Adaptive Robust Estimator for Policy Optimization in Reinforcement Learning

Zhongyi Li, Wan Tian, Jingyu Chen, Kangyao Huang and 7 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · 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
78%Highly rated
?Highly ratedVote to see the score

PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models

PAPO-VLA improves vision-language-action reliability by identifying planning actions via action variation and trajectory outcomes, weighting them by causal importance in policy optimization, and boosting benchmark performance.

Peizheng Guo, Jingyao Wang, Changwen Zheng, Wenwen Qiang

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

– ReadersNo votes yet
11/20 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: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5
78%Highly rated
?Highly ratedVote to see the score

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting

Dirichlet-Guided Group Forecasting reduces time-series over-smoothing by modeling multi-modal predictive distributions with Dirichlet-guided sampling, improving accuracy, diversity, and dynamical consistency.

Xingyu Zhang, Jingyao Wang, Xin Yu, Zeen Song and 3 more

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

– ReadersNo votes yet
11/20 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: 11 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 0/5