45%Niche pick?Niche pickVote to see the scoreNeurIPS 2026NortheasternUniversite de MontrealMechanistic interpretabilityWhat Transformer FFNs Never See: Theory, Diagnosis, and Lightweight RemediationTinghe Zhang, Yucheng Xiao, Alex LambSydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026– ReadersNo votes yet0/20 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: 0 of 20 reviewers recommend itlenient 0/5medium 0/10strict 0/5
86%Must read?Must readVote to see the scoreNeurIPS 2026Beijing Institute of TechnologyRenmin University of ChinaHeriot-WattShenzhen Institutes of Advanced Microsoft ResearchModel-based RLSLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement LearningSLOPE constructs optimistic potential landscapes via distributional regression to amplify sparse success signals and guide planning, outperforming baselines across sparse reward benchmarks.Yao-Hui Li, Zeyu Wang, Xin Li, Wei Pang and 6 moreSydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026– ReadersNo votes yet14/20 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: 14 of 20 reviewers recommend itlenient 5/5medium 7/10strict 2/5
89%Must read?Must readVote to see the scoreNeurIPS 2026Massachusetts Institute of TechnU AlbertaMicrosoft ResearchU PennsylvaniaNew YorkLLM pretraining & scaling lawsNext-Latent Prediction Transformers Learn Compact World ModelsNextLat adds latent self-prediction to transformers, theoretically converging to belief states and empirically improving world modeling, reasoning, and inference speed.Jayden Teoh, Manan Tomar, Kwangjun Ahn, Edward Hu and 6 moreSydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 7 on Hugging Face · Code ★ 196– ReadersNo votes yet16/20 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: 16 of 20 reviewers recommend itlenient 4/5medium 10/10strict 2/5