To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training.How do we find knowledge that is (1) salient to ingroup users, but (2) unknown to LLMs?The most common solutions are single-initiative: either researchers define challenging questions that users passively answer (traditional annotation), or users actively produce data that researchers structure as benchmarks (knowledge extraction).The process would benefit from mixed-initiative collaboration, where users guide the process to meaningfully reflect their cultures, and LLMs steer the process to meet the researcher's goals.We propose CULTURE CARTOGRAPHY as a methodology that operationalizes this mixed-initiative vision.Here, an LLM initializes annotation with questions for which it has low-confidence answers, making explicit both its prior knowledge and the gaps therein.This allows a human respondent to fill these gaps and steer the model towards salient topics through direct edits.We implement CULTURE CARTOGRAPHY as a tool called CULTURE EXPLORER.Compared to a baseline where humans answer LLMproposed questions, we find that CULTURE EX-PLORER more effectively produces knowledge that strong models like DeepSeek R1, Llama-4 and GPT-4o are missing, even with web search.Fine-tuning on this data boosts the accuracy of Llama models by up to 19.2% on related culture benchmarks.

Mobility restrictions have been a primary intervention for controlling the spread of COVID-19, but they also place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an interactive dashboard that communicates our model's predictions for thousands of potential policies.