57%Worth a look?Worth a lookVote to see the scoreNeurIPS 2026Caltech/GatechDelft University of TechnologyCaltechIndian Institute of Technology KWashington University in St. LouMedical imagingCoarse-to-Fine 3D MRI Reconstruction via Resolution-Agnostic Neural OperatorsJiayun (Peter) Wang, Ruibo Wang, Valentin Duruisseaux, Ram Daftari 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
80%Must read?Must readVote to see the scoreNeurIPS 2026U California, Santa BarbaraUCSB / AppleCaltech/GatechGoogleAccentureLLM agents & planningAres: Adaptive Reasoning Effort Selection for Efficient LLM AgentsAres uses a lightweight router to select per-step reasoning effort for LLM agents, cutting reasoning tokens by up to 52.7% with minimal accuracy loss.Jingbo Yang, Bairu Hou, Jiayun (Peter) Wang, Wei Wei and 2 moreAtlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 1 on Hugging Face– 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 5/5medium 7/10strict 0/5
74%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026Caltech/GatechQiyuan LabWashington University in St. LouAccentureGoogleControllable generationUniVL: Unified Vision-Language Embedding for Spatially Grounded Contextual Image GenerationUniVL embeds visual and textual instructions into spatial masks for contextual image generation, cutting FID to 11 and inference costs by 52% without a text encoder.Jiayun (Peter) Wang, Yu Wang, Weijie Gan, Zhenting Wang and 1 moreAtlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026– ReadersNo votes yet9/20 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: 9 of 20 reviewers recommend itlenient 4/5medium 5/10strict 0/5