Customer-obsessed science

A/B testing remains the standard for rolling out new features in the technology industry. Each experiment, however, consumes real traffic, engineering effort, and weeks of wall-clock time. Can AI agents—conditioned on behavioral profiles and contextual descriptions of the intervention—simulate outcomes accurately enough to vet candidate treatments before committing live traffic? We formalize this question

Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying these models in production remains a challenge. Achieving the right balance between automation and human feedback is critical to maintain both scale and quality. While automation can handle large volumes, human oversight is still

In this paper we propose a novel optimization framework for store shelf space planning, specifically tailored for physical stores. The framework leverages machine learning and data-driven techniques to optimize shelf space allocation, aiming to maximize both short-term and long-term business metrics such as sales and profit. Our approach consists of three key components: a geospatial space elasticity model

Traditional online A/B experimentation limits the number of treatments that can be evaluated concurrently. Bandit-based adaptive experimentation algorithms address this by dynamically reallocating traffic across an order of magnitude more treatments. Yet existing methods involve a fundamental trade-off: single-metric Thompson Sampling-based methods are robust to novelty effects but cannot accommodate multiple

We introduce a vocabulary for automated research systems built from one or more agents to make their design choices easier to describe and compare. The vocabulary specifies who the agents are, which operations the harness exposes, who may invoke them, how agents communicate, what state is visible within and across runs, how the next action is chosen, how a run begins, and how outputs are scored. A trajectory