Customer-obsessed science

Ranking systems in online platforms across domains must handle temporal drift where artifact distributions and contextual relevance evolve over time. Existing approaches address temporal drift using recency weighting, sliding windows, or forecasting techniques, however, they do not focus on integrating drift signals with ranking systems or handle the data variability introduced by the drift. We propose

Predicting 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

Health 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.

We 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

E-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