Customer-obsessed science

E-commerce stores face an evolving challenge in detecting fraudulent business registrations, as sophisticated actors continuously adapt their techniques to create deceptive accounts, rendering conventional post-registration measures increasingly inadequate. Realtime detection at the point of registration is further complicated by cold-start constraints and strict sub-second latency requirements. This paper

Understanding why marketplace metrics change is a central problem in two-sided marketplace optimization. We study root cause attribution for metric changes in complex e-commerce systems, focusing on trade-offs between interpretability, efficiency, and causal validity. As a starting point, we extend a metric-decomposition method into a recursive metric-tree framework for multi-level root cause analysis,

Optimizing the consolidation process in container-based fulfillment centers requires trading off com-peting objectives such as processing speed, re-source usage, and space utilization while adhering to a range of real-world operational constraints. This process involves moving items between con-tainers via a combination of human and robotic workstations to free up space for inbound inven-tory and increase

An AI agent that hallucinates a tool call typically shows no overt signs of failure: the call is syntactically correct, its arguments are well-formed, and the associated token probabilities appear routine. In these cases, the execution pipeline receives no indication that anything is amiss. The call executes, downstream components consume its output, and the erroneous transaction completes without human

Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long-horizon tasks due to distribution shift and error accumulation.While reinforcement learning (RL) can finetune these models, it cannot work well across diverse tasks without manual reward engineering. We propose VLLR, a dense reward