Customer-obsessed science

Reward hacking arises when a model improves a proxy reward by exploiting shortcuts rather than solving the intended task. We study this failure mode through the geometry of reinforcement learning updates in language models and argue that hacking emerges when optimization drifts away from a stable low-dimensional learning trajectory. We analyze this drift through dominant singular directions of parameter

Large Vision–Language Models (LVLMs) increasingly rely on retrieval to answer knowledge-intensive multimodal questions. Existing benchmarks overlook conflicts between visual and textual evidence and the importance of generating deflections (e.g., 'Sorry, I cannot answer...') when retrieved knowledge is incomplete. These benchmarks also suffer from rapid obsolescence, as growing LVLM training sets allow

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