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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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Featured news
The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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2026Multimodal Large Language Models (MLLMs) have recently demonstrated promising capabilities in multimodal coding tasks such as chart-to-code generation. However, existing methods primarily rely on supervised fine-tuning (SFT), which requires the model to learn code patterns through chart-code pairs but does not expose the model to a code execution environment. Moreover, while self-correction through execution
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VLDB 20262026Accurate optimizer statistics are fundamental to query and ML-prediction performance in modern database systems, yet maintaining them poses a significant challenge for large-scale data warehouses. Traditional statistics collection relies on full table scans, which become prohibitively expensive as tables grow to billions of rows and beyond. This creates a critical tension: statistics must be kept current
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CVPR 2026 Workshop on Personalization in Generative AI2026Makeup transfer models enable fun augmented reality (AR) experiences as well as virtual try-on (VTO) for online makeup shopping. While recent state-of-the-art diffusion-based solutions such as Stable-Makeup [45] dramatically improve the accuracy and realism of makeup transfer, they still face limitations in identity and skin color preservation, making production-level VTO for makeup shopping unrealistic
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2026Inferring rigid-body physical states and properties from monocular videos is a fundamental step toward physicsbased perception and simulation. Existing approaches assume specific underlying physical systems, object types, and camera poses, which are unable to generalize to complex real-world settings. We introduce ∆YNAMICS, a visionlanguage framework that uses language as a unified representation of rigid-body
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CVPR 2026 Workshop on Fine-Grained Visual Categorization2026Fine-grained visual recognition demands attention to subtle, localized differences that current multimodal large language models (MLLMs) often overlook when guided by generic prompts. We propose APO-Pair, a prompt-optimization framework that learns classification rules by contrasting image pairs. A multimodal agent views these pairs, judges whether they depict the same fine-grained class, and iteratively
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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Courtesy of Pai-Ling Yin
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