AI 中文总结
研究探索性搜索中LLMs的问题，提出TrailLM系统，通过与用户意义建构工作流程对齐，帮助用户重构和重温探索路径，在保留基于LLM搜索优势的同时，增加对搜索过程批判性反思机会。
AI 中文摘要
大语言模型（LLMs）可提高探索性搜索效率，但可能破坏陌生领域所需的反思和迭代意义建构。现有LLM工具常优先快速作答而非支持用户追踪理解演变及策略与目标的契合度。我们提出TrailLM系统，助用户重构和重温探索路径，在信息寻求中支持反思和元认知参与。通过使LLM辅助与用户意义建构工作流程对齐，TrailLM旨在保留基于LLM搜索的优势，同时增加对自身搜索过程进行批判性反思的机会。
英文摘要
Large Language Models (LLMs) can make exploratory search more efficient but may undermine the reflection and iterative sensemaking needed in unfamiliar domains. Existing LLM tools often prioritize rapid answers over supporting users in tracking how their understanding evolves and how well their strategies align with their goals. We present TrailLM, a system that helps users reconstruct and revisit their exploration paths to support reflection and metacognitive engagement during information seeking. By aligning LLM assistance with users' sensemaking workflows, TrailLM aims to preserve the benefits of LLM-based search while enhancing opportunities for critical reflection on one's own search process.
