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. -
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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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ICML 2025 Workshop on Foundation Models for Structured Data2026Tabular and relational foundation models have demonstrated strong in-context learning on academic benchmarks, but their behavior on enterprise-scale structured data—marked by multi-relational schemas, extreme sparsity, and cold-start inference requirements—remains understudied. We evaluate two foundation model paradigms on global supply chain compliance risk prediction, a setting that stresses all three
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ICML 2026 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM)2026We establish a formal equivalence between the Quantized Johnson–Lindenstrauss (QJL) transform of the TurboQuant KV cache compression scheme and the classical 1-bit compressive sensing (1-bit CS) model of Boufounos and Baraniuk (2008), which lets us import 1-bit CS theory into QJL analysis. From it we derive three new consequences. First, reconstruction guarantees for QJL side-channel estimates in terms
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Interspeech 20262026Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings. The system is built on a speech large language model operating in chunk-wise streaming
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2026Fine-tuning large language models (LLMs) for downstream tasks typically exhibits a fundamental safety-capability trade-off, where improving task performance degrades safety alignment even on benign datasets. This degradation persists across standard approaches including supervised fine-tuning (SFT) and Reinforcement learning from human feedback (RLHF). While reinforcement learning with verifiable rewards
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IEEE CAI 20262026Climate data science faces persistent barriers stemming from the fragmented nature of data sources, heterogeneous formats, and the steep technical expertise required to identify, acquire, and process datasets. These challenges limit participation, slow discovery, and reduce the reproducibility of scientific workflows. In this paper, we present a proof of concept for addressing these barriers through the
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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.
