As scientific literature has grown exponentially, researchers often rely on paper triaging strategies such as browsing abstracts before deciding to delve into a paper's full text. However, when an abstract is insufficient, researchers are required to navigate an informational chasm between 150-word abstracts and 10,000-word papers. To bridge that gap, we introduce the idea of recursively expandable summaries and present Qlarify, an interactive system that allows users to recursively expand an abstract by progressively incorporating additional information from a paper's full text. Starting from an abstract, users can brush over summary text to specify targeted information needs or select AI-suggested entities in the text. Responses are then generated on-demand by an LLM and appear in the form of a fluid, threaded expansion of the existing text. Each generated summary can be efficiently verified through attribution to a relevant source-passage in the paper. Through an interview study (n=9) and a field deployment (n=275) at a research conference, we use Qlarify as a technology probe to elaborate upon the expandable summaries design space, highlight how scholars benefit from Qlarify's expandable abstracts, and identify future opportunities to support low-effort and just-in-time exploration of scientific documents $\unicode{x2013}$ and other information spaces $\unicode{x2013}$ through LLM-powered interactions.

翻译：随着科学文献呈指数级增长，研究人员常依赖论文筛选策略，例如先浏览摘要再决定是否深入阅读全文。然而，当摘要信息不足时，研究者需跨越150词摘要与10000词全文之间的信息鸿沟。为填补这一空白，我们提出可递归扩展摘要的概念，并开发Qlarify交互式系统——该系统允许用户通过逐步整合论文全文的附加信息，对摘要进行递归扩展。用户可从摘要出发，通过刷选摘要文本指定特定信息需求，或选择文本中AI建议的实体。系统基于大语言模型（LLM）按需生成响应，形成流动式线程化扩展文本。每次生成的摘要均可通过关联论文中的相关源段落进行高效验证。通过研究会议中的访谈研究（n=9）与实地部署（n=275），我们将Qlarify作为技术探针，深入阐释可扩展摘要的设计空间，揭示学者如何受益于Qlarify的可扩展摘要，并指出未来通过LLM赋能的交互方式，支持对科学文档（及其他信息空间）进行低投入、即时性探索的机遇。