autotrust/JEV-27B: fast, calibrated decisions and full reasoning from one open model autotrust • 12 days ago • 219
autotrust/JEV-27B-VL: a decision model that learned to see without a single image of training autotrust • 9 days ago • 167
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction nvidia • 10 days ago • 86
LightOnOCR-3: High-Performance OCR and Layout Extraction in One Model lightonai • about 19 hours ago • 21
Darwin-27B-ZTC: A Single-Pass Judge and a Quantitative Look at Its Calibration FINAL-Bench • 1 day ago • 12
Live Human Feedback in the Training Loop: Aligning Diffusion Models with Real People Rapidata • 7 days ago • 10
YODAS v3: A 1 Million Hour Dataset for the Next Generation of Open Voice AI Research espnet • 12 days ago • 35
Leading the System One Mosaic Benchmark: What Darwin-27B-ZTC-v2's #1 Means FINAL-Bench • about 2 hours ago • 6
ViDiHand: The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction Xingang-Pan • 3 days ago • 4
autotrust/JEV-27B: fast, calibrated decisions and full reasoning from one open model autotrust • 12 days ago • 219
autotrust/JEV-27B-VL: a decision model that learned to see without a single image of training autotrust • 9 days ago • 167
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction nvidia • 10 days ago • 86
LightOnOCR-3: High-Performance OCR and Layout Extraction in One Model lightonai • about 19 hours ago • 21
Darwin-27B-ZTC: A Single-Pass Judge and a Quantitative Look at Its Calibration FINAL-Bench • 1 day ago • 12
Live Human Feedback in the Training Loop: Aligning Diffusion Models with Real People Rapidata • 7 days ago • 10
YODAS v3: A 1 Million Hour Dataset for the Next Generation of Open Voice AI Research espnet • 12 days ago • 35
Leading the System One Mosaic Benchmark: What Darwin-27B-ZTC-v2's #1 Means FINAL-Bench • about 2 hours ago • 6
ViDiHand: The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction Xingang-Pan • 3 days ago • 4