title: '[2501.09891] Evolving Deeper LLM Thinking'
id: 250109891-evolving-deeper-llm-thinking
tags:
- deepread
created: '2026-06-10T00:24:58.674469Z'
source: https://arxiv.org/abs/2501.09891
source_domain: arxiv.org
fetched_at: '2026-06-10T00:24:58.674344Z'
fetch_provider: builtin
status: draft
type: note
tier: institutional
content_type: paper
deprecated: false
[2501.09891] Evolving Deeper LLM Thinking Computer Science > Artificial Intelligence arXiv:2501.09891 (cs) [Submitted on 17 Jan 2025] Title: Evolving Deeper LLM Thinking Authors: Kuang-Huei Lee , Ian Fischer , Yueh-Hua Wu , Dave Marwood , Shumeet Baluja , Dale Schuurmans , Xinyun Chen View a PDF of the paper titled Evolving Deeper LLM Thinking, by Kuang-Huei Lee and 6 other authors View PDF HTML (experimental) Abstract: We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses. The proposed approach avoids the need to formalize the underlying inference problem whenever a solution evaluator is available. Controlling for inference cost, we find that Mind Evolution significantly outperforms other inference strategies such as Best-of-N and Sequential Revision in natural language planning tasks. In the TravelPlanner and Natural Plan benchmarks, Mind Evolution solves more than 98% of the problem instances using Gemini 1.5 Pro without the use of a formal solver. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2501.09891 [cs.AI] (or arXiv:2501.09891v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2501.09891 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dale Schuurmans [ view email ] [v1] Fri, 17 Jan 2025 00:41:44 UTC (3,183 KB) Full-text links: Access Paper: View a PDF of the paper titled Evolving Deeper LLM Thinking, by Kuang-Huei Lee and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2025-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )