  Navigating the vast scientific literature often starts with browsing a
paper's abstract. However, when a reader seeks additional information, not
present in the abstract, they face a costly cognitive chasm during their dive
into the full text. To bridge this gap, we introduce recursively expandable
abstracts, a novel interaction paradigm that dynamically expands abstracts by
progressively incorporating additional information from the papers' full text.
This lightweight interaction allows scholars to specify their information needs
by quickly brushing over the abstract or selecting AI-suggested expandable
entities. Relevant information is synthesized using a retrieval-augmented
generation approach, presented as a fluid, threaded expansion of the abstract,
and made efficiently verifiable via attribution to relevant source-passages in
the paper. Through a series of user studies, we demonstrate the utility of
recursively expandable abstracts and identify future opportunities to support
low-effort and just-in-time exploration of long-form information contexts
through LLM-powered interactions.
