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…
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 …
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 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…