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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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IDETC-CIE 20252025Deformable packages are becoming increasingly prevalent in the logistics and warehouse industry, demanding robotic manipulation strategies that are robust and adaptive. Unlike rigid objects, these packages undergo significant shape changes under external forces, making their handling more complex. These deformable packages are often manipulated using suction cups and contain internal objects that shift
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Transportation Research Part E: Logistics and Transportation Review2025The 21st century workforce is increasingly characterized by more flexible labor models, particularly in e-commerce and supply chain operations. While previous research has focused mostly on last-mile, on-the-road settings, we focus on under-the-roof (UTR) environments, which present unique challenges due to their complex, varied tasks requiring training and experience. Our study addresses the need to better
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2025Large language models (LLMs) have exhibited extraordinary performance in a variety of tasks while it remains challenging for them to solve complex multi-step tasks as agents. In practice, agents are sensitive to the outcome of certain key steps which makes them likely to fail the task because of a subtle mistake in the planning trajectory. Recent approaches resort to calibrating the reasoning process through
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ICLR 2025 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM), ICML 2026 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM)2025Multi-model inference systems—whether based on routing, cascading, or unified strategies—often rely on confidence signals to decide when a small language model (SLM) output should be accepted or deferred. While such signals are commonly used in classification and short-form generation, their reliability in structured generation settings remains poorly understood. In this work, we study log-probability confidence
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NeurIPS 2025 Workshop on Multimodal Algorithmic Reasoning2025Large Language Models (LLMs) perform well on short-horizon tasks but struggle with long-horizon, multimodal scenarios that require multi-step reasoning, perception, and adaptive planning. We identify two key challenges in these settings: the difficulty of long-term coordination between planning and execution within single-agent architectures and the inefficiency of indiscriminate visual grounding. To address
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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.
