---
title: "Evolving Deeper LLM Thinking — paper by Lee et al. (2025)"
description: "The authors propose Mind Evolution , a genetic-style search. 48 references, cited by 35 papers here."
authors: ["Kuang-Huei Lee", "Ian Fischer", "Yueh-Hua Wu", "Dave Marwood", "Shumeet Baluja", "Dale Schuurmans", "Xinyun Chen"]
year: 2025
venue: "arXiv:2501.09891"
source: "https://arxiv.org/abs/2501.09891"
doi: "10.48550/arxiv.2501.09891"
arxiv: "2501.09891"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lee-2025-evolving-deeper-llm-thinking/"
---
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# Evolving Deeper LLM Thinking

**Authors:** [Kuang-Huei Lee](https://md.research.elamur.ai/free/en/authors/kuang-huei-lee/), [Ian Fischer](https://md.research.elamur.ai/free/en/authors/ian-fischer/), Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, [Dale Schuurmans](https://md.research.elamur.ai/free/en/authors/dale-schuurmans/) and [Xinyun Chen](https://md.research.elamur.ai/free/en/authors/xinyun-chen/) · **Year:** 2025 · **Venue:** arXiv:2501.09891 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2501.09891) · [PDF](https://arxiv.org/pdf/2501.09891) · [DOI 10.48550/arxiv.2501.09891](https://doi.org/10.48550/arxiv.2501.09891) · [arXiv 2501.09891](https://arxiv.org/abs/2501.09891) · [OpenAlex](https://openalex.org/W4406603682)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [35](https://md.research.elamur.ai/free/en/papers/lee-2025-evolving-deeper-llm-thinking/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

The paper asks how extra inference-time compute can make an LLM solve hard planning problems stated purely in natural language, without first converting them into a formal representation. The authors propose **Mind Evolution**, a genetic-style search. The LLM writes an initial population of complete candidate answers. A programmatic evaluator scores each candidate and returns textual critiques. Prompts then drive the LLM to perform crossover, mutation and refinement. An island model with migration and resets keeps the population diverse. Because only whole solutions are scored, no step-level reward model is needed, and the process parallelizes easily.

Experiments use off-the-shelf Gemini 1.5 Flash and Pro, with no finetuning, on TravelPlanner and Natural Plan. At matched compute, Mind Evolution clearly beats Best-of-N and sequential revision. It approaches perfect accuracy without a formal solver. The authors also release a new steganography benchmark, StegPoet.

The work matters because it extends search-based scaling to problems that are hard to formalize. The only requirement is that answers can be checked automatically.

## Key points

- **Baselines are weak on these tasks.** On TravelPlanner, Gemini 1.5 Flash solves 5.6% of problems and o1-preview solves 11.7%. Best-of-N with 800 samples lifts Flash to only 55.6% on TravelPlanner and 69.4% on Meeting Planning (Natural Plan).
- **Mind Evolution with Flash alone** reaches 95.6% on TravelPlanner and 85.0% on Meeting Planning.
- **A two-stage setup adds Gemini 1.5 Pro** for the instances Flash leaves unsolved. This yields 100% on TravelPlanner and 98.4% on Meeting Planning.
- **StegPoet** asks the model to hide a number sequence in a poem, story or essay via a number-to-word cipher. It has 101 validation and 245 test instances. The two-stage method scores 87.1% on validation and 79.2% on test, while Best-of-N solves 1% of validation.
- **Ablations:** the island model gives a significant gain, and deeper search helps as long as each generation still explores broadly. The main limitation is that a programmatic evaluator is required; LLM-based evaluators are left for future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of California San Diego](https://md.research.elamur.ai/free/en/universities/university-of-california-san-diego/) — University, San Diego, United States · 101 works · 54,571 citations
- [University of Alberta](https://md.research.elamur.ai/free/en/universities/university-of-alberta/) — University, Edmonton, Canada · 54 works · 5,242 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 4,145 works

## Cite

Cite as: Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans and Xinyun Chen. 2025. Evolving Deeper LLM Thinking. arXiv:2501.09891.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lee2025evolving,
  title = {{Evolving Deeper LLM Thinking}},
  author = {Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans and Xinyun Chen},
  year = {2025},
  doi = {10.48550/arxiv.2501.09891},
  eprint = {2501.09891},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.09891}
}
```

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*Updated 3 November 2026*