Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model's predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, novelty, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, unoriginal, and repetitive outputs. To address these issues, we propose OmniThink, a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they slowly deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles.

翻译：基于大语言模型的机器写作通常依赖于检索增强生成。然而，这些方法仍局限于模型预定义的知识边界内，限制了生成内容的信息丰富性。具体而言，传统检索到的信息往往缺乏深度和新颖性，且存在冗余问题，这对生成文章的质量产生了负面影响，导致输出内容肤浅、缺乏原创性且重复。为解决这些问题，我们提出了OmniThink，一种模拟人类迭代扩展与反思过程的慢思考机器写作框架。OmniThink的核心思想在于模拟学习者在逐步深化对主题认知过程中的思维行为。实验结果表明，OmniThink在不损害连贯性与深度等指标的前提下，提高了生成文章的知识密度。人工评估与专家反馈进一步凸显了OmniThink在解决生成长篇文本的实际挑战方面的潜力。