Andrés Monroy-Hernández

Yuhan Liu, Varun Nagaraj Rao, Sohyeon Hwang et al.

In this vision paper, we outline a blueprint for a decentralized network for the delivery industry, powered by an open protocol. By presenting the network's key components and layers, alongside hypothetical scenarios, we illustrate how the network and the protocol may function in practice. Through this decentralized approach, we aim to address three major issues that mark the current platform-based delivery economy: power imbalances between the platform and workers, information asymmetries caused by opaque decision-making, and value misalignments. Our goal is to provoke dialogue and inspire future work toward more equitable, transparent, and worker-centered futures in the delivery industry, the broader gig economy, and related domains.

Yuhan Liu, Shuyao Zhou, Jakob Kaiser et al.

Policy researchers need scalable ways to surface public views, yet they often rely on interviews, listening sessions, and surveys-analyzed thematically-that are slow, expensive, and limited in scale and diversity. LLMs offer new possibilities for thematic analysis of unstructured text, yet we know little about how LLM-assisted workflows perform for policy research. Building on a workflow for LLM-assisted thematic analysis of online forums, we conduct a study with 11 policy researchers, who use an early prototype and see it as a quick, rough-and-ready input to their research. We then extend and scale the workflow to analyze millions of Reddit posts and 1,058 chatbot-led interview transcripts on a policy-relevant topic, treating these sources as rich and scalable data for policy discourse. We compare the synthesized themes to those from authoritative policy reports, identify points of alignment and divergence, and discuss what this implies for policy researchers adopting LLM-assisted workflows.

Diya Hundiwala, Andrés Monroy-Hernández

Sticky notes remain a durable collaborative medium because they support rapid idea externalization, rearrangement, and coordination of group attention through spatial organization while being low-friction and lightweight. Recent AR systems suggest new ways to externalize ideas in shared physical space, including spatial annotations and digital workspaces. We introduce AnchorNote, a co-located AR system that lets collaborators intentionally capture spoken ideas as spatially anchored sticky notes via live transcription and LLM summarization. We evaluated AnchorNote in a two-phase iterative study with 20 participants completing a brainstorming and thematic grouping task to examine how speech-driven, spatially persistent capture shapes idea externalization in collaboration. We found that AnchorNote reduced writing effort but reshaped collaboration by introducing new coordination costs and shifting how participants formulated, timed, and organized ideas. We use AnchorNote as an exploratory probe to study how speech-driven, spatial externalization in AR restructures collaborative cognition and coordination, and to derive design implications for future co-located AR collaboration tools.