Customer-obsessed science

Novel View Synthesis (NVS) enables the generation of unseen views of a scene from a single or multiple images, allowing users to freely explore an object from any viewpoint. Despite the recent impressive qualitative improvements of generative models for this task, existing methods struggle to provide global and intuitive control of target viewpoints because they either use input-relative camera poses or

MLOps has emerged as a critical challenge in the field of artificial intelligence, due to the necessity for continual model updates. This requirement arises from common occurrences such as model degradation, shifts in input data distribution and changes in incidence rates. A significant bottleneck in these automated updates is the drift in the output score distribution that requires incremental effort from

Production AI agents fail when their context sources—system prompts, knowledge bases, tool descriptions, and procedural skills—contain errors or gaps. Current maintenance approaches rely on manual log review and ad-hoc debugging, creating a scalability bottleneck as interaction volume grows. We present TRace (TRajectory Attribution for Automated Context Engineering), an automated feedback loop that mines

Allocating the one-time carbon cost of training a large AI model across the inference requests it serves is an open methodological problem with no standardized solution. The choices made in boundary definition, functional unit selection, and lifetime forecasting can alter reported per-request emissions by an order of magnitude, undermining any comparison across models. We decompose this problem into three

Multivariate time series contain two kinds of cross-variable relationships: persistent ones that reflect underlying structure (geographic proximity, shared infrastructure, physical coupling) and dynamic ones that arise from transient conditions in each observation window. Current transformer architectures conflate the two—channel-independent models ignore cross-variable relationships entirely, while cross-variable