Chatbots are increasingly used to analyze temporal event sequences of which website clickstreams are among the most common. Explanations are essential for understanding claims about event-sequence data. However, existing systems often require programming expertise to interpret these explanations and fail to explain the data processes underlying the data claims. We introduce a technique for linking a data claim to its analytic provenance (i.e., the analytic steps and results that produced it). It has two components: a reference table that records analytic steps and results in addressable cells, and a citation format that binds each claim to one or more cells. To operationalize this technique for clickstream analysis, we curated a set of analytic operations for clickstream analysis, organized them through a pivot-table abstraction, and developed tools that an AI agent can invoke to create a reference table and assemble a natural language response with cited data claims. To evaluate how citations affect users' assessment of data claims, we conducted two crowdsourced experiments with 90 participants each. The experiments showed that participants perceived correct data claims to be more accurate with citations when compared with no explanations, and that they perceived incorrect data claims to be less accurate with citations when compared with code and no explanations.
Machine learning models for omics-based prediction of cancer outcomes are commonly built on aggregated data without distinguishing ancestry groups, or under an ancestry-discrete design, which partitions samples into ancestry groups and fits a separate model within each group. Recent evidence shows that this approach is inherently limited: predictive accuracy declines with genetic distance from the training samples, even within a single ancestry group, because the data distribution shifts continuously rather than only between predefined categories. In this paper, we propose distribution-aware in-context learning (DA-ICL) for ancestry-continuum modeling. Our method extends conventional ancestry-discrete modeling to ancestry-continuum modeling, capturing the nonstationary omics-endpoint relationship across the genetic continuum while enabling efficient modeling through an in-context learning foundation model. We provide theoretical analysis demonstrating the superiority of the ancestry-continuum over ancestry-discrete modeling, along with empirical results supporting the proposed framework. Source code is available at https://github.com/ai4pm/DA-ICL-Continuum.