57%Worth a look?Worth a lookVote to see the scoreNeurIPS 2026Texas A&MTexas A&MLambda, IncLambdaRepresentation & contrastive learningSpin-Weighted Spherical Harmonics Enable Complete and Scalable E(3)-Equivariant NetworksChenxing Liang, Yuchao Lin, Andrii Kryvenko, Wendi Yu and 4 moreAtlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026– ReadersNo votes yet1/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 1 of 20 reviewers recommend itlenient 1/5medium 0/10strict 0/5
57%Worth a look?Worth a lookVote to see the scoreNeurIPS 2026Texas A&M University - ColleTexas A&MTexas A&MActive learningCLUE: Correlated Latent Uncertainty for Single-Pass Deep Uncertainty EstimationYucheng Wang, Wenyuan Zhao, Weifeng Zhang, Chao Tian and 1 moreAtlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026– ReadersNo votes yet1/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 1 of 20 reviewers recommend itlenient 1/5medium 0/10strict 0/5
76%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026Texas A&MTexas A&MDiffusion modelsInference-Time Search Using Side Information for Diffusion-Based Image ReconstructionA training-free inference-time search framework incorporates side information into diffusion-based inverse problem solvers to consistently improve reconstruction quality across diverse tasks.Mahdi Farahbakhsh, Vishnu Teja Kunde, Dileep Kalathil, Krishna Narayanan and 1 moreAtlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026– ReadersNo votes yet10/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 10 of 20 reviewers recommend itlenient 5/5medium 4/10strict 1/5
80%Must read?Must readVote to see the scoreNeurIPS 2026Texas A&MTexas A&MRL for LLMsReinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise AdvantagesFormulating diffusion LLM generation as a finite-horizon MDP yields an exact stepwise policy gradient with entropy-guided step selection and one-step advantages, achieving state-of-the-art RL post-training results.Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh, Dileep Kalathil and 2 moreAtlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026– ReadersNo votes yet12/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 12 of 20 reviewers recommend itlenient 4/5medium 7/10strict 1/5