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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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2026Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) fine-tuning algorithms such as GRPO have improved long-form reasoning in text-only language models, particularly for coding and mathematics. Reliable tool use in multimodal agents, however, remains challenging because models must interpret text and images while integrating noisy
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ICRA 20262026We present the Generalized Action Model (GAM), a production-grade foundation model that unifies robotic action generation across diverse tasks and embodiments through a vision-language-action (VLA) pipeline. GAM addresses two fundamental barriers in scaling robotic manipulation: the lack of a unified representation for diverse robot end-effectors and the prohibitive cost of acquiring high-quality interaction
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When rubrics fail: Error enumeration as reward in reference-free RL post-training for virtual try-on2026Reinforcement learning with verifiable rewards (RLVR) and Rubrics as Rewards (RaR) have driven strong gains in domains with clear correctness signals and even in subjective domains by synthesizing evaluation criteria from ideal reference answers. But many real-world tasks admit multiple valid outputs and lack the single ideal answer that rubric generation depends on. We identify this reference-free setting
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QRS 20262026Test automation is moving from fixed script replay to agent driven execution and judgement. An agent may read a goal, inspect product state, call tools, recover from small changes, and collect evidence before reporting a result. Existing test case formats do not give enough structure for this style of execution. Plain prompts mix setup, purpose, rules, and steps. Traditional scripts are repeatable, but
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ICML 2026 Workshop on Combining Theory and Benchmark2026Generative text classifiers, which assign labels by modeling the joint distribution over inputs and labels, have recently regained attention due to strong low-sample performance and a growing perception that they are less prone to shortcut learning than discriminative classifiers. However, existing evidence for shortcut avoidance is often indirect, frequently conflates classifier formulation with architectural
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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.
