Customer-obsessed science
Research areas
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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world.
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July 9, 202610 min read
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Featured news
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2026Large 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
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KDD 2026 Workshop on AI for Fraud and Abuse2026E-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
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KDD 2026 Workshop on Two-Sided Market Orientation2026Understanding 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,
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ICML 2026 Workshop on Reinforcement Learning from World Feedback2026Optimizing 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
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ICLR 2026 Workshop on Agents in the Wild2026An 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
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