Customer-obsessed science

In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing. Ignoring these exogenous signals can substantially degrade forecasting accuracy, particularly when they drive spikes, discontinuities

Despite their strong zero-shot forecasting capabilities, Time Series Foundation Models (TSFMs) lack mechanisms for incorporating the structured domain knowledge that practitioners need for interpretability and forecast control. Dynamic Factor Models (DFMs) provide this structure, decomposing series into interpretable, adjustable factors like trend and seasonality while capturing shared dynamics across related

Video-Language Models (VidLMs) achieve strong benchmark scores, yet these scores often hide whether models use the video at all. We show that VidLM failures follow two pathways: some visual signals are never reliably encoded, while others are encoded but overridden by model priors. We introduce REVEAL, a diagnostic stress-test benchmark for quantifying when and why VidLMs under-use visual evidence. REVEAL

Container image pulling accounts for the majority of pod startup time in Kubernetes environments. Standard pull down loads the entire image before the container can start, even when the application accesses only a fraction of the image content at startup. We present SOCI (Seekable OCI), a lazy-loading architecture that enables containers to start without downloading the full image. SOCI builds an external

Evaluating rule compliance in industry requires assessing products against complex regulatory standards using multimodal data sources—a task where both correctness and trustworthiness of automated judgments are critical. Existing approaches either rely on costly human audits, supervised classifiers that demand large-scale labeled training data, or monolithic multimodal models that apply uniform reasoning