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Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

Paper · 2025 · Jenny Zhang, Shengran Hu, Cong Lu et al.

Summary

The paper asks whether an AI system can keep improving itself without human redesign. Schmidhuber's Gödel machine required formal proofs that each self-rewrite helps, which is impractical. The authors propose the Darwin Gödel Machine (DGM), which replaces proof with experiment. It is a coding agent, built on frozen pretrained foundation models, that edits its own Python codebase. Each edited version is scored on coding benchmarks. Borrowing from Darwinian evolution and open-endedness research, the DGM does not keep only the best agent. It stores every variant in a growing archive and samples parents from it, so weaker but unusual agents can later serve as stepping stones. Because the agent that solves tasks is also the agent that rewrites itself, gains in coding skill can also improve its future self-edits. Across iterations, the system found its own upgrades, such as better file-editing tools and context management. Performance roughly doubled on SWE-bench and Polyglot, beating ablations without self-improvement or without the archive. This is a practical, empirically grounded route toward recursively self-improving agents.

Key points

  • Results: SWE-bench rose from 20.0% to 50.0%, and Polyglot from 14.2% to 30.7%.
  • Two ablation baselines did worse than the full DGM:
    • one where a fixed base agent generates all new agents, with no self-improvement;
    • one where each agent builds only on its latest version, with no archive or open-ended exploration.
  • Discovered self-modifications include improved code-editing tools, long-context window management, and peer-review-style checking of solutions.
  • Experiments used sandboxing, human oversight, and traceable changes. The authors also observed objective hacking, where agents gamed the benchmark metric rather than truly solving the task.
  • The code is open-sourced at github.com/jennyzzt/dgm. The open-ended exploration loop itself stays fixed, and letting the agent evolve this loop is left as 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 British ColumbiaVancouver · Canada107 works · 37,489 citations🔬Vector InstituteToronto · Canada41 works · 5,820 citations

Also on the byline: Sakana AI.

 Fields, topics and concepts

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksInformation Systems · 1,888 works

Cite

Cite as Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange and Jeff Clune. 2025. Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents. arXiv:2505.22954.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2505.22954 · arXiv 2505.22954

@misc{zhang2025darwin,
  title = {{Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents}},
  author = {Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange and Jeff Clune},
  year = {2025},
  doi = {10.48550/arxiv.2505.22954},
  eprint = {2505.22954},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.22954}
}

References

184 references, 54 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Fuma Aki, Riku Ikeda, Takumi Saito, Ciaran Regan, and Mizuki Oka. Llm-poet: Evolving complex environments using large language models. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 243–246, 2024. find
  2. Rajeev Alur, Rishabh Singh, Dana Fisman, and Armando Solar-Lezama. Search-based program synthesis. Communications of the ACM, 61(12):84–93, 2018. find
  3. S-I Amari. Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Transactions on computers, 100(11):1197–1206, 1972. find
  4. Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba. Hindsight experience replay. Advances in neural information processing systems, 30, 2017.
  5. Anthropic. Claude 3.5 Sonnet. https://www.anthropic.com/news/ claude-3-5-sonnet, June 2024a. [Accessed 17 April 2025]. ↗
  6. Anthropic. Claude can now use tools, May 2024b. URL https://www.anthropic.com/ news/tool-use-ga. Accessed: 2025-05-03. ↗
  7. Anthropic. Claude 3.7 sonnet and claude code, February 2025. URL https://www.anthropic.com/news/claude-3-7-sonnet. Accessed: 2025-05-06. ↗
  8. Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, et al. Foundational challenges in assuring alignment and safety of large language models. arXiv preprint arXiv:2404.09932, 2024.
  9. Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations, 2015.
  10. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073, 2022.
  11. Adrien Baranes and Pierre-Yves Oudeyer. Active learning of inverse models with intrinsically motivated goal exploration in robots. Robotics and Autonomous Systems, 61(1):49–73, 2013.
  12. Shraddha Barke, Emmanuel Anaya Gonzalez, Saketh Ram Kasibatla, Taylor Berg-Kirkpatrick, and Nadia Polikarpova. Hysynth: Context-free llm approximation for guiding program synthesis. Advances in Neural Information Processing Systems, 37:15612–15645, 2024. find
  13. Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, et al. Managing extreme AI risks amid rapid progress. Science, 384(6698):842–845, 2024. find
  14. N Bostrom. Existential Risks: analyzing human extinction scenarios and related hazards. Journal of Evolution and Technology, 9, 2002. find
  15. Nick Bostrom. Ethical issues in advanced artificial intelligence. Machine Ethics and Robot Ethics, pp. 69–75, 2020. find
  16. Herbie Bradley, Andrew Dai, Hannah Benita Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth Stanley, Gregory Schott, and Joel Lehman. Quality-diversity through ai feedback. In The Twelfth International Conference on Learning Representations, 2024.
  17. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  18. Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al. Genie: Generative interactive environments. In Forty-first International Conference on Machine Learning, 2024. find
  19. J Richard Buchi and Lawrence H Landweber. Solving sequential conditions by finite-state strategies. In The collected works of J. Richard Büchi, pp. 525–541. Springer, 1990. find
  20. Ruisheng Cao, Fangyu Lei, Haoyuan Wu, Jixuan Chen, Yeqiao Fu, Hongcheng Gao, Xinzhuang Xiong, Hanchong Zhang, Wenjing Hu, Yuchen Mao, et al. Spider2-v: How far are multimodal agents from automating data science and engineering workflows? Advances in Neural Information Processing Systems, 37:107703–1… find
  21. Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades, and Jean-Baptiste Mouret. Quality-diversity optimization: a novel branch of stochastic optimization. In Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, pp. 109–135. Springer, 2021. find
  22. Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, and Shengxin Zhu. Unleashing the potential of prompt engineering in large language models: a comprehensive review. arXiv preprint arXiv:2310.14735, 2023.
  23. Ching-An Cheng, Allen Nie, and Adith Swaminathan. Trace is the next autodiff: Generative optimization with rich feedback, execution traces, and llms. Advances in Neural Information Processing Systems, 37:71596–71642, 2024. find
  24. Jeff Clune. AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence. arXiv preprint arXiv:1905.10985, 2019. — [arXiv:1905.10985](https://arxiv.org/abs/1905.10985) find
  25. Cédric Colas, Pierre Fournier, Mohamed Chetouani, Olivier Sigaud, and Pierre-Yves Oudeyer. Curious: intrinsically motivated modular multi-goal reinforcement learning. In International conference on machine learning, pp. 1331–1340. PMLR, 2019. find
  26. Cédric Colas, Tristan Karch, Clément Moulin-Frier, and Pierre-Yves Oudeyer. Language and culture internalization for human-like autotelic AI. Nature Machine Intelligence, 4(12):1068–1076, 2022a. find
  27. Cédric Colas, Tristan Karch, Olivier Sigaud, and Pierre-Yves Oudeyer. Autotelic agents with intrinsically motivated goal-conditioned reinforcement learning: a short survey. Journal of Artificial Intelligence Research, 74:1159–1199, 2022b. find
  28. Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, and Marc-Alexandre Côté. Augmenting autotelic agents with large language models. In Conference on Lifelong Learning Agents, pp. 205–226. PMLR, 2023.
  29. Rémi Coulom. Efficient selectivity and backup operators in monte-carlo tree search. In International conference on computers and games, pp. 72–83. Springer, 2006. find
  30. Charles Darwin. Origin of the species. In British Politics and the environment in the long nineteenth century, pp. 47–55. Routledge, 2023. find
  31. Richard Dawkins. The evolution of evolvability. In Artificial life, pp. 201–220. Routledge, 2019. find
  32. Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine. Emergent complexity and zero-shot transfer via unsupervised environment design. Advances in neural information processing systems, 33:13049–13061, 2020. find
  33. Aaron Dharna, Cong Lu, and Jeff Clune. Quality-Diversity Self-Play: Open-Ended Strategy Innovation via Foundation Models. In NeurIPS 2024 Workshop on Open-World Agents, 2024. find
  34. Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, and Joel Lehman. Quality diversity through human feedback: towards open-ended diversity-driven optimization. In Proceedings of the 41st International Conference on Machine Learning, pp. 11072–11090, 2024. find
  35. Theodosius Dobzhansky. Genetics of the evolutionary process, volume 139. Columbia University Press, 1970. find
  36. Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune. Go-explore: a new approach for hard-exploration problems. arXiv preprint arXiv:1901.10995, 2019. find
  37. Adrien Ecoffet, Jeff Clune, and Joel Lehman. Open questions in creating safe open-ended AI: tensions between control and creativity. In Artificial Life Conference Proceedings 32, pp. 27–35. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info . . . , 2020. find
  38. Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune. First return, then explore. Nature, 590(7847):580–586, 2021. find
  39. Anthony W Fisher Edwards. The genetical theory of natural selection. Genetics, 154(4):1419–1426, 2000. find
  40. Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, and Joshua B Tenenbaum. Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning. In Proceedings of the 42nd acm sigplan international conference o… find
  41. Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. Diversity is all you need: Learning skills without a reward function. arXiv preprint arXiv:1802.06070, 2018.
  42. Meta Fundamental AI Research Diplomacy Team (FAIR)†, Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, et al. Human-level play in the game of Diplomacy by combining language models with strategic reasoning. Science, 378(66… find
  43. Maxence Faldor, Jenny Zhang, Antoine Cully, and Jeff Clune. OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=Y1XkzMJpPd. ↗
  44. Chrisantha Fernando, Dylan Sunil Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution. In Forty-first International Conference on Machine Learning, 2024.
  45. Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858, 2022.
  46. Hongcheng Gao, Yue Liu, Yufei He, Longxu Dou, Chao Du, Zhijie Deng, Bryan Hooi, Min Lin, and Tianyu Pang. Flowreasoner: Reinforcing query-level meta-agents. arXiv preprint arXiv:2504.15257, 2025. — [arXiv:2504.15257](https://arxiv.org/abs/2504.15257) find
  47. Paul Gauthier. Aider: Ai pair programming in your terminal. Aider-AI/aider, 2024. Accessed: 2025-05-14. https://github.com/ ↗
  48. Loris Gaven, Thomas Carta, Clément Romac, Cédric Colas, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces. arXiv preprint arXiv:2502.07709, 2025. find
  49. John Gerhart and Marc Kirschner. The theory of facilitated variation. Proceedings of the National Academy of Sciences, 104(suppl_1):8582–8589, 2007. find
  50. Irving John Good. Speculations concerning the first ultraintelligent machine. In Advances in computers, volume 6, pp. 31–88. Elsevier, 1966. find
  51. Google DeepMind. Gemini model “thinking” updates — march 2025. https://blog.google/technology/google-deepmind/ gemini-model-thinking-updates-march-2025/\#gemini-2-5-thinking, March 2025. Accessed: 2025-05-11. ↗
  52. Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, et al. Alignment faking in large language models. arXiv preprint arXiv:2412.14093, 2024. find
  53. Sumit Gulwani. Automating string processing in spreadsheets using input-output examples. ACM Sigplan Notices, 46(1):317–330, 2011.
  54. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025.
  55. John Storrs Hall. Self-improving AI: An analysis. Minds and Machines, 17(3):249–259, 2007. find
  56. Alex Havrilla, Andrew Dai, Laura O’Mahony, Koen Oostermeijer, Vera Zisler, Alon Albalak, Fabrizio Milo, Sharath Chandra Raparthy, Kanishk Gandhi, Baber Abbasi, et al. Surveying the effects of quality, diversity, and complexity in synthetic data from large language models. arXiv preprint arXiv:2412.0…
  57. Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, and Roberta Raileanu. Glore: When, where, and how to improve llm reasoning via global and local refinements. arXiv preprint arXiv:2402.10963, 2024b.
  58. Jesse Love Hendrikse, Trish Elizabeth Parsons, and Benedikt Hallgrímsson. Evolvability as the proper focus of evolutionary developmental biology. Evolution & development, 9(4):393–401, 2007. find
  59. Nathan Herr, Tim Rocktäschel, and Roberta Raileanu. Llm-first search: Self-guided exploration of the solution space. arXiv preprint arXiv:2506.05213, 2025. — [arXiv:2506.05213](https://arxiv.org/abs/2506.05213) find
  60. Marius Hobbhahn. Swe-bench verified mini. https://github.com/mariushobbhahn/ SWEBench-verified-mini, April 2025. Accessed: 2025-04-16. ↗
  61. John J Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8):2554–2558, 1982. find
  62. Shengran Hu and Jeff Clune. Thought Cloning: Learning to think while acting by imitating human thinking. Advances in Neural Information Processing Systems, 36, 2024. find
  63. Shengran Hu, Cong Lu, and Jeff Clune. Automated Design of Agentic Systems. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=t9U3LW7JVX. ↗
  64. Yue Hu, Yuzhu Cai, Yaxin Du, Xinyu Zhu, Xiangrui Liu, Zijie Yu, Yuchen Hou, Shuo Tang, and Siheng Chen. Self-evolving multi-agent collaboration networks for software development. arXiv preprint arXiv:2410.16946, 2024. — [arXiv:2410.16946](https://arxiv.org/abs/2410.16946) find
  65. Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. Large language models can self-improve. arXiv preprint arXiv:2210.11610, 2022.
  66. Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, and Tim Rocktaschel. Open-endedness is essential for artificial superhuman intelligence. arXiv preprint arXiv:2406.04268, 2024.
  67. Kazuki Irie, Imanol Schlag, Róbert Csordás, and Jürgen Schmidhuber. A modern self-referential weight matrix that learns to modify itself. In International Conference on Machine Learning, pp. 9660–9677. PMLR, 2022. find
  68. Kazuki Irie, Róbert Csordás, and Jürgen Schmidhuber. Metalearning continual learning algorithms. Transactions on Machine Learning Research, 2025. find
  69. Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al. Population based training of neural networks. arXiv preprint arXiv:1711.09846, 2017. find
  70. Minqi Jiang, Edward Grefenstette, and Tim Rocktäschel. Prioritized level replay. In International Conference on Machine Learning, pp. 4940–4950. PMLR, 2021. find
  71. Minqi Jiang, Tim Rocktäschel, and Edward Grefenstette. General intelligence requires rethinking exploration. Royal Society Open Science, 10(6):230539, 2023. find
  72. Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan. SWE-bench: Can Language Models Resolve Real-world Github Issues? In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=VTF8yNQM66. ↗
  73. Ingmar Kanitscheider, Joost Huizinga, David Farhi, William Hebgen Guss, Brandon Houghton, Raul Sampedro, Peter Zhokhov, Bowen Baker, Adrien Ecoffet, Jie Tang, Oleg Klimov, and Jeff Clune. Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft. arXiv preprint arXiv:21… — [arXiv:2106.14876](https://arxiv.org/abs/2106.14876) find
  74. Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R Bowman, Tim Rocktäschel, and Ethan Perez. Debating with more persuasive llms leads to more truthful answers. arXiv preprint arXiv:2402.06782, 2024.
  75. Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vard-hamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. Dspy: Compiling declarative language model calls into self-improving pipelines. arXiv preprint arXiv:2310.03714, 2023.
  76. Yoon Kim, Carl Denton, Luong Hoang, and Alexander M Rush. Structured Attention Networks. In International Conference on Learning Representations, 2017. find
  77. Motoo Kimura. The neutral theory of molecular evolution. Scientific American, 241(5):98–129, 1979. find
  78. Louis Kirsch and Jürgen Schmidhuber. Self-referential meta learning. In First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022. find
  79. Martin Klissarov, Pierluca D’Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, and Mikael Henaff. Motif: Intrinsic motivation from artificial intelligence feedback. arXiv preprint arXiv:2310.00166, 2023. — [arXiv:2310.00166](https://arxiv.org/abs/2310.00166) find
  80. Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C Machado, and Pierluca D’Oro. MaestroMotif: Skill Design from Artificial Intelligence Feedback. arXiv preprint arXiv:2412.08542, 2024. — [arXiv:2412.08542](https://arxiv.org/abs/2412.08542) find
  81. Varun Raj Kompella, Marijn Stollenga, Matthew Luciw, and Juergen Schmidhuber. Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots. Artificial Intelligence, 247:313–335, 2017. find
  82. Robert Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, and Sebastian Flennerhag. Discovering evolution strategies via meta-black-box optimization. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 29–30, 2023. find
  83. Robert Lange, Yingtao Tian, and Yujin Tang. Large language models as evolution strategies. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 579–582, 2024.
  84. Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter. Learning quadrupedal locomotion over challenging terrain. Science robotics, 5(47):eabc5986, 2020. find
  85. Joel Lehman. Machine love. arXiv preprint arXiv:2302.09248, 2023. — [arXiv:2302.09248](https://arxiv.org/abs/2302.09248) find
  86. Joel Lehman and Kenneth O Stanley. Novelty search and the problem with objectives. Genetic programming theory and practice IX, pp. 37–56, 2011. find
  87. Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O Stanley. Evolution through large models. In Handbook of Evolutionary Machine Learning, pp. 331–366. Springer, 2023. find
  88. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33: 9459–9474, 2020.
  89. J. Li, Storie J., and J. Clune. Encouraging creative thinking in robots improves their ability to solve challenging problems. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 193–200, 2014. find
  90. Yixuan Li, Julian Parsert, and Elizabeth Polgreen. Guiding enumerative program synthesis with large language models. In International Conference on Computer Aided Verification, pp. 280–301. Springer, 2024. find
  91. Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu. Encouraging divergent thinking in large language models through multi-agent debate. arXiv preprint arXiv:2305.19118, 2023.
  92. Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step. In The Twelfth International Conference on Learning Representations, 2023. — Let's Verify Step by Step
  93. Bryan Lim, Manon Flageat, and Antoine Cully. Large language models as in-context ai generators for quality-diversity. In ALIFE 2024: Proceedings of the 2024 Artificial Life Conference. find
  94. Seppo Linnainmaa. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. PhD thesis, Master’s Thesis (in Finnish), Univ. Helsinki, 1970. find
  95. Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang. Evolution of heuristics: Towards efficient automatic algorithm design using large language model. arXiv preprint arXiv:2401.02051, 2024. — [arXiv:2401.02051](https://arxiv.org/abs/2401.02051) find
  96. Lei Liu, Xiaoyan Yang, Yue Shen, Binbin Hu, Zhiqiang Zhang, Jinjie Gu, and Guannan Zhang. Think-in-memory: Recalling and post-thinking enable llms with long-term memory. arXiv preprint arXiv:2311.08719, 2023. — [arXiv:2311.08719](https://arxiv.org/abs/2311.08719) find
  97. Chris Lu, Sebastian Towers, and Jakob Foerster. Arbitrary order meta-learning with simple population-based evolution. In Artificial Life Conference Proceedings 35, volume 2023, pp. 67. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info . . . , 2023. find
  98. Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292, 2024a.
  99. Cong Lu, Shengran Hu, and Jeff Clune. Intelligent go-explore: Standing on the shoulders of giant foundation models. arXiv preprint arXiv:2405.15143, 2024b.
  100. Cong Lu, Shengran Hu, and Jeff Clune. Automated capability discovery via model self-exploration. arXiv preprint arXiv:2502.0757, 2025. — [arXiv:2502.0757](https://arxiv.org/abs/2502.0757) find
  101. Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Eureka: Human-level reward design via coding large language models. arXiv preprint arXiv:2310.12931, 2023.
  102. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback, 2023. URL https://arxiv.org/abs/2303.17651, 2023. ↗
  103. John Markoff. Machines of loving grace: The quest for common ground between humans and robots. HarperCollins Publishers, 2016. find
  104. Ernst Mayr. The growth of biological thought: Diversity, evolution, and inheritance. Harvard University Press, 1982. find
  105. Luke Metz, C Daniel Freeman, Niru Maheswaranathan, and Jascha Sohl-Dickstein. Training learned optimizers with randomly initialized learned optimizers. arXiv preprint arXiv:2101.07367, 2021. — [arXiv:2101.07367](https://arxiv.org/abs/2101.07367) find
  106. Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, and Hinrich Schütze. Ret-llm: Towards a general read-write memory for large language models. arXiv preprint arXiv:2305.14322, 2023. — [arXiv:2305.14322](https://arxiv.org/abs/2305.14322) find
  107. Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites. arXiv preprint arXiv:1504.04909, 2015. — [arXiv:1504.04909](https://arxiv.org/abs/1504.04909) find
  108. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. s1: Simple test-time scaling. arXiv preprint arXiv:2501.19393, 2025.
  109. Muhammad U Nasir and Julian Togelius. Practical PCG through large language models. In 2023 IEEE Conference on Games (CoG), pp. 1–4. find
  110. Muhammad U Nasir, Steven James, and Julian Togelius. Word2world: Generating stories and worlds through large language models. arXiv preprint arXiv:2405.06686, 2024. — [arXiv:2405.06686](https://arxiv.org/abs/2405.06686) find
  111. Anh Mai Nguyen, Jason Yosinski, and Jeff Clune. Innovation engines: Automated creativity and improved stochastic optimization via deep learning. In Proceedings of the 2015 annual conference on genetic and evolutionary computation, pp. 959–966, 2015. find
  112. Fan Nie, Lan Feng, Haotian Ye, Weixin Liang, Pan Lu, Huaxiu Yao, Alexandre Alahi, and James Zou. Weak-for-strong: Training weak meta-agent to harness strong executors. arXiv preprint arXiv:2504.04785, 2025. find
  113. Boye Niu, Yiliao Song, Kai Lian, Yifan Shen, Yu Yao, Kun Zhang, and Tongliang Liu. Flow: Modularized agentic workflow automation. In The Thirteenth International Conference on Learning Representations, 2025. find
  114. Alexander Novikov, Ngân Vũ, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco J. R. Ruiz, Abbas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushmeet Kohli, and Matej Ba… — AlphaEvolve: A coding agent for scientific and algorithmic discovery
  115. OpenAI. Introducing swe-bench verified. https://openai.com/index/ introducing-swe-bench-verified/, August 2024. Accessed: 2025-04-16. ↗
  116. OpenAI. OpenAI o3-mini. https://openai.com/index/openai-o3-mini/, January 2025. Accessed: 2025-05-01. ↗
  117. Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner. Intrinsic motivation systems for autonomous mental development. IEEE transactions on evolutionary computation, 11(2):265–286, 2007. find
  118. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730– 27744, 2022.
  119. Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. A Decomposable Attention Model for Natural Language Inference. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2249–2255, 2016.
  120. Jack Parker-Holder, Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi, Stephen Spencer, Jessica Yung, Michael Dennis, Sultan Kenjeyev, Shangbang Long, Vlad Mnih, Harris Chan, Maxime Gazeau, Bonnie Li, Fabio Pardo… find
  121. Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning, pp. 2778–2787. PMLR, 2017. find
  122. Paul Gauthier. o1 tops aider’s new polyglot leaderboard. https://aider.chat/2024/12/ 21/polyglot.html, December 2024. Accessed: 2025-04-16. ↗
  123. Oleksandr Polozov and Sumit Gulwani. Flashmeta: A framework for inductive program synthesis. In Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications, pp. 107–126, 2015. find
  124. Justin K Pugh, Lisa B Soros, and Kenneth O Stanley. Quality diversity: A new frontier for evolutionary computation. Frontiers in Robotics and AI, 3:40, 2016. find
  125. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. find
  126. Maxime Robeyns, Martin Szummer, and Laurence Aitchison. A Self-Improving Coding Agent. arXiv preprint arXiv:2504.15228, 2025. — [arXiv:2504.15228](https://arxiv.org/abs/2504.15228) find
  127. Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al. Mathematical discoveries from program search with large language models. Nature, 625(7995):468–475, 2024.
  128. J Rosser and Jakob Nicolaus Foerster. Agentbreeder: Mitigating the AI safety impact of multi-agent scaffolds via self-improvement. In Scaling Self-Improving Foundation Models without Human Supervision, 2025. URL https://openreview.net/forum?id=j0n3BJJTcT. ↗
  129. David E Rumelhart, Geoffrey E Hinton, Ronald J Williams, et al. Learning internal representations by error propagation, 1985. find
  130. Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, et al. Rainbow teaming: Open-ended generation of diverse adversarial prompts. Advances in Neural Information Processing Systems, 37:69747–… find
  131. Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk, Pavel Kolev, and Georg Martius. SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models. arXiv preprint arXiv:2503.01584, 2025. — [arXiv:2503.01584](https://arxiv.org/abs/2503.01584) find
  132. Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver. Universal value function approximators. In International conference on machine learning, pp. 1312–1320. PMLR, 2015. find
  133. Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36:68539–68551, 2023.
  134. Jürgen Schmidhuber. Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. PhD thesis, Technische Universität München, 1987. find
  135. Jürgen Schmidhuber. A ‘self-referential’weight matrix. In International conference on artificial neural networks, pp. 446–450. Springer, 1993. find
  136. Jürgen Schmidhuber. Gödel machines: Fully self-referential optimal universal self-improvers. In Artificial general intelligence, pp. 199–226. Springer, 2007. find
  137. Jürgen Schmidhuber. Driven by compression progress: A simple principle explains essential aspects of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. In Workshop on anticipatory behavior in adaptive learning systems, pp. 48–76. Spri… find
  138. Jürgen Schmidhuber. Powerplay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem. Frontiers in psychology, 4:313, 2013. find
  139. Jürgen Schmidhuber and Rudolf Huber. Learning to generate focus trajectories for attentive vision. Institut für Informatik, 1990. find
  140. Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, et al. The prompt report: A systematic survey of prompting techniques. arXiv preprint arXiv:2406.06608, 2024.
  141. Ivaxi Sheth, Jan Wehner, Sahar Abdelnabi, Ruta Binkyte, and Mario Fritz. Safety is Essential for Responsible Open-Ended Systems. arXiv preprint arXiv:2502.04512, 2025. — [arXiv:2502.04512](https://arxiv.org/abs/2502.04512) find
  142. Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, and Charles Sutton. Lambdabeam: Neural program search with higher-order functions and lambdas. Advances in Neural Information Processing Systems, 36:51327–51346, 2023. find
  143. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36:8634–8652, 2023.
  144. David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484–489, 2016. find
  145. David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815, 2017.
  146. Avi Singh, John D Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J Liu, James Harrison, Jaehoon Lee, Kelvin Xu, et al. Beyond human data: Scaling self-training for problem-solving with language models. arXiv preprint arXiv:2312.06585, 2023.
  147. Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger. Defining and characterizing reward gaming. Advances in Neural Information Processing Systems, 35:9460–9471, 2022. find
  148. Kenneth O Stanley and Joel Lehman. Why greatness cannot be planned: The myth of the objective. Springer, 2015. find
  149. Kenneth O Stanley, Joel Lehman, and Lisa Soros. Open-endedness: The last grand challenge you’ve never heard of. While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself, 2017. find
  150. Marilyn Strathern. ‘Improving ratings’: audit in the British University system. European review, 5 (3):305–321, 1997. find
  151. Jinwei Su, Yinghui Xia, Ronghua Shi, Jianhui Wang, Jianuo Huang, Yijin Wang, Tianyu Shi, Yang Jingsong, and Lewei He. Debflow: Automating agent creation via agent debate. arXiv preprint arXiv:2503.23781, 2025. — [arXiv:2503.23781](https://arxiv.org/abs/2503.23781) find
  152. Shyam Sudhakaran, Miguel González-Duque, Matthias Freiberger, Claire Glanois, Elias Najarro, and Sebastian Risi. Mariogpt: Open-ended text2level generation through large language models. Advances in Neural Information Processing Systems, 36:54213–54227, 2023. find
  153. OpenAI Team, Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai o1 system card. arXiv preprint arXiv:2412.16720, 2024.
  154. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  155. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023a. find
  156. Ren-Jian Wang, Ke Xue, Yutong Wang, Peng Yang, Haobo Fu, Qiang Fu, and Chao Qian. Diversity from human feedback. arXiv preprint arXiv:2310.06648, 2023b. — [arXiv:2310.06648](https://arxiv.org/abs/2310.06648) find
  157. Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O Stanley. Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions. arXiv preprint arXiv:1901.01753, 2019.
  158. Xingyao Wang, Boxuan Li, Yufan Song, Frank F Xu, Xiangru Tang, Mingchen Zhuge, Jiayi Pan, Yueqi Song, Bowen Li, Jaskirat Singh, et al. Openhands: An open platform for ai software developers as generalist agents. In The Thirteenth International Conference on Learning Representations, 2024.
  159. Yufei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Katerina Fragkiadaki, Zackory Erickson, David Held, and Chuang Gan. Robogen: Towards unleashing infinite data for automated robot learning via generative simulation. arXiv preprint arXiv:2311.01455, 2023c. — [arXiv:2311.01455](https://arxiv.org/abs/2311.01455) find
  160. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837, 2022.
  161. Marco Wiering and Jürgen Schmidhuber. Hq-learning. Adaptive behavior, 6(2):219–246, 1997. find
  162. S Wright. The roles of mutation, inbreeding, crossbreeding and selection in evolution, proceedings of the sixth international congress of genetics. proc sixth int congr genet [internet]. New York356366, 1932. find
  163. Chunqiu Steven Xia, Yinlin Deng, Soren Dunn, and Lingming Zhang. Agentless: Demystifying llm-based software engineering agents. arXiv preprint arXiv:2407.01489, 2024.
  164. John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik R Narasimhan, and Ofir Press. SWE-agent: Agent-computer interfaces enable automated software engineering. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https://arxiv.org/abs… ↗
  165. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR), 2023.
  166. Rui Ye, Shuo Tang, Rui Ge, Yaxin Du, Zhenfei Yin, Siheng Chen, and Jing Shao. Mas-gpt: Training llms to build llm-based multi-agent systems. arXiv preprint arXiv:2503.03686, 2025. — [arXiv:2503.03686](https://arxiv.org/abs/2503.03686) find
  167. Xunjian Yin, Xinyi Wang, Liangming Pan, Xiaojun Wan, and William Yang Wang. G\\" odel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement. arXiv preprint arXiv:2410.04444, 2024. — [arXiv:2410.04444](https://arxiv.org/abs/2410.04444) find
  168. Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, and Deqing Yang. Evoagent: Towards automatic multi-agent generation via evolutionary algorithms. arXiv preprint arXiv:2406.14228, 2024. find
  169. Eliezer Yudkowsky et al. Artificial Intelligence as a positive and negative factor in global risk. Global catastrophic risks, 1(303):184, 2008. find
  170. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. Textgrad: Automatic" differentiation" via text. arXiv preprint arXiv:2406.07496, 2024.
  171. Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, and Noah D Goodman. Quiet-star: Language models can teach themselves to think before speaking. arXiv preprint arXiv:2403.09629, 2024a.
  172. Eric Zelikman, Eliana Lorch, Lester Mackey, and Adam Tauman Kalai. Self-taught optimizer (stop): Recursively self-improving code generation. In First Conference on Language Modeling, 2024b. find
  173. Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, and Jie Tang. Rest-mcts*: Llm self-training via process reward guided tree search. Advances in Neural Information Processing Systems, 37:64735–64772, 2024a.
  174. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. Multi-agent architecture search via agentic supernet. arXiv preprint arXiv:2502.04180, 2025a. — [arXiv:2502.04180](https://arxiv.org/abs/2502.04180) find
  175. Jenny Zhang, Joel Lehman, Kenneth Stanley, and Jeff Clune. OMNI: Open-endedness via Models of human Notions of Interestingness. In The Twelfth International Conference on Learning Representations, 2024b. URL https://openreview.net/forum?id=AgM3MzT99c. ↗
  176. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, et al. Aflow: Automating agentic workflow generation. arXiv preprint arXiv:2410.10762, 2024c.
  177. Yuanshuo Zhang, Yuchen Hou, Bohan Tang, Shuo Chen, Muhan Zhang, Xiaowen Dong, and Siheng Chen. Gnns as predictors of agentic workflow performances. arXiv preprint arXiv:2503.11301, 2025b. — [arXiv:2503.11301](https://arxiv.org/abs/2503.11301) find
  178. Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. Autocoderover: Autonomous program improvement. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 1592–1604, 2024d. find
  179. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19724–19731, 2024. find
  180. Andy Zhou, Kevin Wu, Francesco Pinto, Zhaorun Chen, Yi Zeng, Yu Yang, Shuang Yang, Sanmi Koyejo, James Zou, and Bo Li. AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration. arXiv preprint arXiv:2503.15754, 2025. — [arXiv:2503.15754](https://arxiv.org/abs/2503.15754) find
  181. Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, et al. Symbolic learning enables self-evolving agents. arXiv preprint arXiv:2406.18532, 2024.
  182. Yuqi Zhu, Jia Li, Ge Li, YunFei Zhao, Zhi Jin, and Hong Mei. Hot or cold? adaptive temperature sampling for code generation with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 437–445, 2024. find
  183. Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piékos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksand… — [arXiv:2305.17066](https://arxiv.org/abs/2305.17066) find
  184. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber. Gptswarm: Language agents as optimizable graphs. In Forty-first International Conference on Machine Learning, 2024.

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