Yi Wen

People tend to be more casual when asking AI questions than when asking another person. But what are the consequences? Can existing deep research agents (DRAs) infer what users leave unsaid through agentic reasoning and searching?

💡Introducing UNDERSPEC-DR, a framework for evaluating how deep research agents handle underspecified user queries.

📶Our results show that while DRAs can still generate answers grounded in cited evidence, their answers may fall short of satisfying users’ information needs substantially, even when one single detail is missing from the original query.

📄Paper: arxiv.org/abs/2610.39413…

While AI has the potential to automate semi-structured interviews, it falls short in perceiving nonverbal cues, building rapport, and strategically steering long-horizon conversations. At the same time, qualitative researchers often want to conduct interviews themselves, as doing so helps them better understand participants and may lead to deeper insights.

Instead of asking whether AI should replace interviewers, we asked: 𝗛𝗼𝘄 𝗰𝗮𝗻 𝗔𝗜 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝘄𝗮𝘆?

In our #CHI2026 paper, we introduce InterFlow, an unobtrusive form of AI assistance that supports interviewers in managing interview flow and facilitates real-time data sensemaking. InterFlow features ambient visualizations, mixed-initiative information capture, and process-oriented AI suggestions grounded in empirical knowledge of semi-structured interviewing.

Our user evaluation showed that InterFlow reduced cognitive load and helped interviewers keep track of both the big picture and important emerging details during interviews.

Huge thanks to my collaborators Yu Zhang, Sriram Suresh, and to my advisors @luzc08 , @canlhci , and @Iriskie_Xia!

Full paper: arxiv.org/pdf/2602.06396

Winning the best paper award on my first publication😮🤯Just feel so lucky to be on such an amazing team!