Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge. Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort. Here, we explore a new design space of mixed-initiative workflows. In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively. Synergi allows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.

翻译：高效综述学术文献并综合现有成果是科学进步的关键。然而，出版物数量的持续增长与知识负荷的加重，使得研究线索的综合工作比以往更具挑战性。尽管已有大量研究致力于帮助学者与单篇论文交互，但构建分散于多篇论文中的研究线索仍是一大难题。多数自上而下的综合方法（及大语言模型）难以实现个性化输出与迭代优化，而自下而上的综合方式则在时间与精力上成本高昂。本文探索了混合倡议工作流程的新设计空间，并据此开发了创新计算流程Synergi——该流程将用户提供的相关种子线索与引文图谱及大语言模型相结合，分别实现线索扩展与结构化。Synergi允许学者从与其兴趣相关的论文生成的完整线索与子线索结构出发，并按需进行迭代与定制。评估结果表明，Synergi有助于学者高效理解相关线索、拓展学术视野并激发求知欲。本文最后探讨了基于线索的混合倡议学术综合支持工具的未来设计启示。