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However, existing LLM-based approaches face two significant challenges: the high computational cost of LLM inference, which limits real-time decision making, and a domain gap between the LLMs\u2019 general-purpose knowledge and the specific demands of navigation scenarios. To overcome these challenges, we propose a Knowledge-Enhanced navigation framework with an Intuitive-Deliberate mechanism (KEID). KEID employs an Intuitive-Deliberate mechanism that mimics human cognition, using a lightweight intuition module to strategically invoke the LLM, which reduces computational overhead. Meanwhile, KEID enhances the LLM with two specialized knowledge bases: a Scene Description Tree that describes the complex spatial and semantic relationships of indoor environments within a hierarchical framework and a Navigation Example database for in-context learning adaptation. Evaluations on the HM3D dataset within the Habitat simulator validate our method\u2019s efficacy, demonstrating that KEID achieves a 47.1% success rate and a competitive 18.8% success weighted by path length, significantly outperforming existing baselines. Our work not only improves navigation performance but also enhances decision-making efficiency, establishing an effective framework for developing practical, real-time LLM-based robotic agents.<\/jats:p>","DOI":"10.1007\/s44230-025-00122-5","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T15:36:36Z","timestamp":1764948996000},"page":"482-496","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge Enhanced Efficient Robotic Decision Making with Intuitive-Deliberate LLMs"],"prefix":"10.1007","volume":"5","author":[{"given":"Long","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixuan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"122_CR1","doi-asserted-by":"publisher","first-page":"2292","DOI":"10.1109\/TASE.2024.3378010","volume":"22","author":"J Sun","year":"2024","unstructured":"Sun J, Wu J, Ji Z, Lai Y-K. 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The paper was handled by another Editor and has undergone a rigorous peer review process. Tianrui Li was not involved in the journal\u2019s peer review of, or decisions related to, this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}