OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking

vanilla-retrieved information tends to lack depth, utility, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, repetitive, and unoriginal outputs. To address these issues, we propose OmniThink, a 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 progressively 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.

vanilla RAG relies on a fixed set of search strategies (Ram et al., 2023), which lack diversity in generation, preventing a thorough exploration of the topic and resulting in a fragmented and incomplete understanding of the subject (Spink et al., 1998).

Note that humans can naturally avoid such pitfalls in the writing process. This phenomenon can be explained through the theory of reflective practice, a concept rooted in cognitive science (Osterman, 1990). According to this theory, human writers continuously reflect on previously gathered information and personal experiences, allowing them to reorganize, filter, and refine their cognitive framework. This process prompts writers to iteratively adjust their writing direction and mental pathways, ultimately allowing human authors to generate more profound, nuanced and original content (Bruce, 1978).

Motivated by this, we propose OmniThink, a new machine writing framework that emulates the human-like cognitive process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they gradually deepen their understanding of complex topics to expand knowledge boundaries. By continuously reflecting on previously retrieved information, OmniThink can determine the optimal steps for further expansion. This expansion-reflection mechanism enables the dynamic adjustment of the retrieval strategies, fostering a more thorough and comprehensive exploration of relevant information. Once a diverse set of information has been gathered, OmniThink transitions to the stages of outline construction and article generation. This iterative thinking process leads to the production of articles of higher quality that contain a higher knowledge density of useful, insightful, and original content.

we introduce the Knowledge Density (KD) for the generated article, which is defined as the ratio of meaningful content to the overall volume of text (Xu and Reitter, 2017) as: KD = PN i=1 ki · U(ki) L (1) where N is the total number of atomic knowledge units identified within the document. The function U(ki) indicates whether the i-th unit information ki is unique. L represents the total length of the text. In this formula, the numerator represents the sum of unique units of atomic knowledge extracted from a long article. The denominator corresponds to the length of the article. Note that the value of the knowledge density metric lies in its ability to measure the reading cost of generated text from the perspective of information acquisition (Bovair and Kieras, 1991; Dos Santos and Mookerjee, 1993). Readers encountering low KD content often experience fatigue, frustration, or disengagement due to redundant or irrelevant details. In contrast, high-density content provides a streamlined experience, enabling efficient knowledge transfer.