Literature review with Flowing
Synthesize across your references without losing the thread to your sources.
文献综述不是摘要列表——而是关于领域展示了什么、何处分歧、你填补什么空白的论证。当你需要的是下一段「证据段」而非「领域小综述」时，带本地论文库支持的续写往往更像顶刊综述：少量代表性研究、紧凑综合、无范围漂移。
Key facts
- 将核心参考 PDF 导入本地论文库——检索与 AI 围绕你选择的论文运转。
- 关键词索文在起草各节时浮现高度相关的片段。
- 带着上下文提问可在几次点击内综合你附加的文稿与论文库片段。
- 带着证据续写补充段落当下所需——而非模型见过的每一篇论文。
- 基于依据的润色修订话语功能（引入证据、对比、综合）——而非只改措辞。
- 富文本与 LaTeX 编辑配合实时 PDF 预览，综述稿在同一工作区完成。
A workflow built for synthesis
Start by importing the PDFs that matter for your review chapter—not necessarily your entire Zotero vault, but the papers you will actually cite in this section. Flowing indexes them locally so keyword recall can match terms in your draft to passages across those files.
As you write, use recall to see candidate evidence cards, attach the best passages as context chips, and ask focused questions: How do these three studies disagree? What limitation do they share? Flowing’s answers draw on the passages you attached—not the open internet.
续写 vs 知识堆砌
通用 AI 的常见失败：下一段学术上准确、语言成熟——但覆盖过多、引用过多、偏离你正在写的章节。审稿人称之为注释式书目，而非叙事。
Flowing 的续写为「插入」而优化：维护作者节奏，只补充当前论述动作所需的证据，再回到综合句。这种克制符合顶刊综述的阅读方式——而非把每篇相关论文都塞进一段。
Staying faithful while moving fast
Literature reviews fail when speed trades off against accuracy—unsupported claims, misread findings, or citations that look real but do not exist. Flowing reduces that risk by linking AI suggestions to library snippets you can open on the card before accepting text into your manuscript.
Basis-backed polish and evidence-backed continue writing extend the same principle: rewrites and new sentences should strengthen your argument with references you can check, not invent.
Who this is for
Thesis literature review chapters, survey articles, grant background sections, and any long-form synthesis where reviewers will check your sources. If you only need a quick unstructured summary of one paper, a general chat tool may suffice; if you need a citable chapter, Flowing is the better fit.