Customer-obsessed science
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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. -
August 21, 20269 min read -
July 30, 20268 min read
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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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2026Visual grounding aims to associate free-form textual queries with specific regions in an image. While recent Multimodal Large Language Models (MLLMs) have demonstrated promising capabilities in this domain, they primarily excel at object-level grounding and often struggle with part-level grounding—an essential requirement for fine-grained tasks such as robotic manipulation. In this work, we introduce a
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2026LLM-based agents struggle to execute complex, multi-step Standard Operating Procedures (SOPs) that are fundamental to industrial automation. Existing benchmarks fail to capture the procedural complexity and tool orchestration demands of real-world workflows. We introduce SOP-Bench, a benchmark of 2,000+ tasks from human expert-authored SOPs across 12 business domains (healthcare, logistics, finance, content
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KDD 2026 Workshop on Two-sided Marketplace Optimization2026Optimizing online pricing strategy for Amazon Device dependent products is a uniquely challenging topic in dynamic pricing of two-sided marketplaces when balancing the supply side economics and demand responsiveness. To address this challenge, we propose a scalable framework that integrates a hierarchical segmentation model with a sequential learning layer for high-velocity event (HVE) adjustment, to simulate
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ICTIR 20262026Effectively understanding and modeling the temporal aspects of user queries is crucial for Information Retrieval (IR) and Question Answering (QA), particularly in contexts that demand freshness, historical accuracy, or temporal reasoning. In this paper, we present a formal and comprehensive taxonomy for classifying natural language queries along four dimensions: (i) temporal understanding, (ii) reasoning
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IMAGE 20262026Energy companies hold millions of legacy seismic files in SEG-Y format with inconsistent, fragmented metadata that prevents automated processing and AI integration. We present a multi-agent AI system that automates end-to-end metadata reconstruction for large-scale SEG-Y migration to MDIO v1.0, a modern self-describing seismic format. Our system addresses three coupled challenges: (1) seismic product type
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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.
