Customer-obsessed science
Research areas
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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
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iScience2026Predicting antigen–antibody binding is essential to drug discovery and protein engineering. For de novo antibody design, generalizable binding prediction models are crucial for efficient in silico screening. However, existing affinity predictors lack generalization, with performance deteriorating for antibodies targeting antigens absent from training data or datasets lacking non-binders. To address this
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arXiv2026Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf. Primary care guards against diagnostic errors and unsafe care; agents assisting in this domain warrant evaluation against the same risks. Current benchmarks focus on medical knowledge, assessed through isolated question-answering or clinician-facing tasks.
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DigiPro 20262026We describe a series of experiments reconstructing dynamic VFX scenes from multi-camera and moving-camera footage using Gaussian Splatting, built from open-source components. Through this process we report what worked, what broke, and what we learned, spanning an extension of Shape of Motion for moving cameras, an evaluation harness for 4D Gaussian Splatting, a baseline study, and algorithmic contributions
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SIGIR 2025 Workshop on E-commerce2026E-commerce stores rely on product catalog data, which can be enriched by automated mechanisms like visual attribute extraction, for features like search and filtering. Extracting visual attributes from product images in e-commerce is challenging due to the wide diversity in products and the high cost of manual labeling, making traditional methods that rely on human-annotated data often impractical. In this
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UAI 20262026We study stochastic bandits in which observing a reward is optional but incurs an action-dependent cost. This setting captures applications where feedback acquisition (e.g., human evaluation or randomized testing) is expensive, and the learner must trade off exploration, exploitation, and observation cost. We formulate regret to include both reward loss and the cumulative cost of requested observations.
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