autotrust/JEV-27B-VL: a decision model that learned to see without a single image of training autotrust • 10 days ago • 122
autotrust/JEV-27B: fast, calibrated decisions and full reasoning from one open model autotrust • 13 days ago • 47
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction nvidia • 11 days ago • 88
Darwin-27B-ZTC: A Single-Pass Judge and a Quantitative Look at Its Calibration FINAL-Bench • 2 days ago • 12
Leading the System One Mosaic Benchmark: What Darwin-27B-ZTC-v2's #1 Means FINAL-Bench • 1 day ago • 6
ViDiHand: The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction Xingang-Pan • 4 days ago • 4
FiftyOne Now Reads LeRobot: 50 Embodiments, 497 Episodes, One Indexed Dataset harpreetsahota • 5 days ago • 4
Live Human Feedback in the Training Loop: Aligning Diffusion Models with Real People Rapidata • 8 days ago • 10
autotrust/JEV-27B-VL: a decision model that learned to see without a single image of training autotrust • 10 days ago • 122
autotrust/JEV-27B: fast, calibrated decisions and full reasoning from one open model autotrust • 13 days ago • 47
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction nvidia • 11 days ago • 88
Darwin-27B-ZTC: A Single-Pass Judge and a Quantitative Look at Its Calibration FINAL-Bench • 2 days ago • 12
Leading the System One Mosaic Benchmark: What Darwin-27B-ZTC-v2's #1 Means FINAL-Bench • 1 day ago • 6
ViDiHand: The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction Xingang-Pan • 4 days ago • 4
FiftyOne Now Reads LeRobot: 50 Embodiments, 497 Episodes, One Indexed Dataset harpreetsahota • 5 days ago • 4
Live Human Feedback in the Training Loop: Aligning Diffusion Models with Real People Rapidata • 8 days ago • 10