As AI agents take on increasingly long-running tasks involving sophisticated planning and execution, there is a corresponding need for novel interaction designs that enable deeper human-agent collaboration. However, most prior works leverage human interaction to fix “autonomous” workflows that have yet to become fully autonomous or rigidly treat planning and execution as separate stages. Based on a formative study with 9 researchers using AI to support their work, we propose a design that affords greater flexibility in collaboration, so that users can 1) delegate agency to the user or agent via a collaborative plan where individual steps can be assigned; and 2) interleave planning and execution so that plans can adjust after partial execution. We introduce Cocoa, a system that takes design inspiration from computational notebooks to support complex research tasks. A lab study (n = 16) found that Cocoa enabled steerability without sacrificing ease-of-use, and a week-long field deployment (n = 7) showed how researchers collaborated with Cocoa to accomplish real-world tasks.

With the rapid growth of scholarly archives, researchers subscribe to “paper alert’’ systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users’ research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers.

In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and hide the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask “newbie” questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal.

In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches have shown the usefulness of highlighting relevant authors while scholars engage in paper discovery. However, these systems do not capture and utilize users’ evolving knowledge of authors. We reflect on the design space and introduce ComLittee, a literature discovery system that supports author-centric exploration. In contrast to paper-centric interaction in prior systems, ComLittee’s author-centric interaction supports curating research threads from individual authors, finding new authors and papers using combined signals from a paper recommender and the curated authors’ authorship graphs, and understanding them in the context of those signals. In a within-subjects experiment that compares to a paper-centric discovery system with author-highlighting, we demonstrate how ComLittee improves author and paper discovery.

Research consumption has been traditionally limited to the reading of academic papers—a static, dense, and formally written format. Alternatively, pre-recorded conference presentation videos, which are more dynamic, concise, and colloquial, have recently become more widely available but potentially under-utilized. In this work, we explore the design space and benefits for combining academic papers and talk videos to leverage their complementary nature to provide a rich and fluid research consumption experience. Based on formative and co-design studies, we present Papeos, a novel reading and authoring interface that allow authors to augment their papers by segmenting and localizing talk videos alongside relevant paper passages with automatically generated suggestions. With Papeos, readers can visually skim a paper through clip thumbnails, and fluidly switch between consuming dense text in the paper or visual summaries in the video. In a comparative lab study (n=16), Papeos reduced mental load, scaffolded navigation, and facilitated more comprehensive reading of papers.

The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user’s publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement—and future engagement when mediated by authors—without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.

We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML. SciA11y uses machine learning models to extract and understand the content of scientific PDFs, and reorganizes the resulting paper components into a form that better supports skimming and scanning for blind and low vision (BLV) readers. SciA11y adds navigation features such as tagged headings, a table of contents, and bidirectional links between inline citations and references, which allow readers to resolve citations without losing their context. A set of 1.5 million open access papers are processed and available at https://scia11y.org/. This system is a first step in addressing scientific PDF accessibility, and may significantly improve the experience of paper reading for BLV users.