Apple is presenting new research at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026, taking place in person at the Colorado Convention Center in Denver from June 3 to Ju…
Researchers have introduced VSAS-Bench, a new benchmark for evaluating visual streaming assistant models that process continuous video frames in real time. The framework provides over 18,000 temporall…
Researchers at BalCapRL introduced a balanced reinforcement learning framework for multimodal large language model image captioning that jointly optimizes utility-aware correctness, reference coverage…
Apple hosted a two-day Workshop on Privacy-Preserving Machine Learning & AI in early 2026, bringing together its researchers and the broader academic community to discuss advances in private learning,…
Researchers at Duke University introduced Reward-Variance Policy Optimization (RVPO), a risk-sensitive alignment method that penalizes inter-reward variance to prevent language models from neglecting …
Researchers Chen Huang, Xianhang Li, Vimal Thilak, Etai Littwin, and Josh Susskind have developed Text-Conditional JEPA (TC-JEPA), a visual self-supervised learning model that uses image captions to r…
Researchers at Apple have developed a new learned image codec that achieves 2.3–3x bitrate savings against traditional codecs like AV1, AV2, VVC, ECM, and JPEG-AI, while also outperforming the best le…