Identity
- Type
- university
- Country label
- USA
- HQ country
- USA
- Founding country
- USA
- Parent
- not in the notes
- Parent country
- not in the notes
- Country basis
- the research notes
Where this comes from
Convention D1: a dual label, HQ first, when the HQ, founding or parent country differ.
Models by Class
Models
ModelClassOpennessParametersReleased
AsymFLUX.2 kleinimage generationMay 2026
OpenVLA-OFTrobot policy27 Feb 2025
MedVAEvision20 Feb 2025
s1Stanford University, University of Washington, Allen AI, Contextual AIreasoningJan 2025
WonderWorldworld model13 Jun 2024
OpenVLAStanford University, UC Berkeley, Toyota Research Institute (TRI)robot policyJun 2024
AnimateDiffShanghai AI Lab, The Chinese University of Hong Kong, Stanford Universityvideo generation10 Jul 2023
AnimateDiffShanghai AI Lab, The Chinese University of Hong Kong, Stanford Universityvideo generation10 Jul 2023
ACT (Action Chunking with Transformers)robot policy23 Apr 2023
ControlNet 1.1image generation12 Apr 2023
ControlNetimage generation10 Feb 2023
ControlNet 1.0image generation8 Feb 2023
StreetCLIPgeospatial1 Feb 2023
BioMedLMLLM14 Dec 2022
SatMAEgeospatial17 Jul 2022
Simple-BEVStanford University, Carnegie Mellon Universitydriving16 Jun 2022
Stanford De-identifier baselanguage encoder16 Jun 2022
ImVoteNetMeta, Stanford Universityvision29 Jan 2020
MinkUNetvision18 Apr 2019
GIN (Graph Isomorphism Network)Massachusetts Institute of Technology, Stanford Universitygraph1 Oct 2018
CheXNetvision14 Nov 2017
DCN (Deep and Cross Network)Google, Stanford Universityrecommendation17 Aug 2017
GraphSAGEgraph7 Jun 2017
PointNet++vision7 Jun 2017
PointNetvision2 Dec 2016
LFADSGoogle, Stanford Universityscientific22 Aug 2016
node2vecgraph3 Jul 2016
Fast Neural Style Transferimage generation27 Mar 2016
GloVeembeddingOct 2014
CheXagentmultimodal LLM–
OpenVLArobot policy–
PointNetvision–Links
Organisation
Stanford Universityuniversity, USA
Stanford Universityuniversity, USAModels 32
AnimateDiffShanghai AI Lab, The Chinese University of Hong Kong, Stanford University, video generationDeveloper
OpenVLAStanford University, UC Berkeley, Toyota Research Institute (TRI), robot policyDeveloper
s1Stanford University, University of Washington, Allen AI, Contextual AI, reasoningDeveloper
AnimateDiffShanghai AI Lab, The Chinese University of Hong Kong, Stanford University, video generationDeveloper
ControlNetStanford University, image generationDeveloper
CheXagentStanford University, multimodal LLMDeveloper
DCN (Deep and Cross Network)Google, Stanford University, recommendationDeveloper
ImVoteNetMeta, Stanford University, visionDeveloper
OpenVLAStanford University, robot policyDeveloper
OpenVLA-OFTStanford University, robot policyDeveloper
PointNetStanford University, visionDeveloper
ControlNet 1.0Stanford University, image generationDeveloper+20
ControlNet 1.1Stanford University, image generationDeveloper
GIN (Graph Isomorphism Network)Massachusetts Institute of Technology, Stanford University, graphDeveloper
LFADSGoogle, Stanford University, scientificDeveloper
MinkUNetStanford University, visionDeveloper
PointNetStanford University, visionDeveloper
PointNet++Stanford University, visionDeveloper
Simple-BEVStanford University, Carnegie Mellon University, drivingDeveloper
ACT (Action Chunking with Transformers)Stanford University, robot policyDeveloper
AsymFLUX.2 kleinStanford University, image generationDeveloper
BioMedLMStanford University, LLMDeveloper
CheXNetStanford University, visionDeveloper
Fast Neural Style TransferStanford University, image generationDeveloper
GloVeStanford University, embeddingDeveloper
GraphSAGEStanford University, graphDeveloper
MedVAEStanford University, visionDeveloper
node2vecStanford University, graphDeveloper
SatMAEStanford University, geospatialDeveloper
Stanford De-identifier baseStanford University, language encoderDeveloper
StreetCLIPStanford University, geospatialDeveloper
WonderWorldStanford University, world modelDeveloperScores
| Benchmark | Model | Score | Who ran it | Setting |
|---|---|---|---|---|
| AIME24 | s1 | 56.7 % | vendor-reported | after SFT on 1,000 examples (base 26.7) |
| GenEval | AsymFLUX.2 klein | 0.82 score | vendor-reported | paper-reported, labelled unverified by Codesota; year 2026 |
| LIBERO | OpenVLA | 76.5 % | vendor-reported | Average; baseline reported in OpenVLA-OFT paper |
| LIBERO | OpenVLA-OFT | 97.1 % | vendor-reported | Average |
| WorldScore | WonderWorld | 50.88 score | maintainer-run | Dynamic aggregate |
| WorldScore | WonderWorld | 72.69 score | maintainer-run | Static aggregate |
Where this comes from
Every recorded score of this organisation's models, with who ran it. Scores from different settings are not directly comparable.
Sources
- SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery arxiv.org https://arxiv.org/abs/2207.08051
- OpenVLA: An Open-Source Vision-Language-Action Model arxiv.org https://arxiv.org/abs/2406.09246
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success arxiv.org https://arxiv.org/abs/2502.19645
- huggingface.co/microsoft/maira-2 https://huggingface.co/microsoft/maira-2
- s1: Simple test-time scaling arxiv.org https://arxiv.org/html/2501.19393v1
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Stanford University", https://aurion.semai.id/organisations/stanford-university/, records of October 2026.
