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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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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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KDD 2025 Workshop on AI Agent for Information Retrieval2025In this paper, we present CACHE-ED, a novel framework for document entity extraction that combines the power of large language models (LLMs) with graph-based document representations, caching mechanisms, and an actor-critic multi-agent architecture. Our approach addresses the inefficiencies and inaccuracies that are common in extracting structured information from documents, particularly in templated formats
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2025In this paper, we introduce CoRet, a dense retrieval model designed for code-editing tasks that integrates code semantics, repository structure, and call graph dependencies. The model focuses on retrieving relevant portions of a code repository based on natural language queries such as requests to implement new features or fix bugs. These retrieved code chunks can then be presented to a user or to a second
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CVPR 2025 Workshop on Visual Concepts2025State-of-the-art performance has been achieved in recent years on tasks such as search, recommendation and classification using Visuo-Lingual Multi-Modal models. While the pretrained Vision-Language models like Contrastive Language-Image Pre-training (CLIP) have achieved promising zero-shot performance on several generalized tasks by learning vision-language concepts in a common space, the natural hierarchical
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IEEE CogMI 2025 Workshop on Agentic Intelligence: Risks, Ethics, and Trust2025Creating authentic digital twins of mobile users through realistic user behavior simulation is critical to truly understand and anticipate customer needs at scale. Toward this, we introduce Digital TwIns of MObile User (TIMO), an end-to-end framework for high-fidelity mobile user simulation that addresses four key limitations in existing approaches: subjective decision-making diversity, scalable experience
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2025As the scale of training large language models (LLMs) increases, one emergent failure is silent data corruption (SDC), where hardware produces incorrect computations without explicit failure signals. In this work, we are the first to investigate the impact of real-world SDCs on LLM training by comparing model training between healthy production nodes and unhealthy nodes exhibiting SDCs. With the help from
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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
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
