{"id":"arxiv:2408.08435","title":"Automated Design of Agentic Systems","date":"2024-08-15","section":"Agents, open-endedness, AGI","quality":0.129553,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/","source_url":"https://arxiv.org/abs/2408.08435","text_available":true,"authors":["Shengran Hu","Cong Lu","Jeff Clune"],"outgoing":[{"source":"arxiv:2408.08435","target":"arxiv:1905.10985","evidence":"id","id":"arxiv:1905.10985","title":"AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/089142f7297bed414d01b82c78dc888e4018fecff7efbe5cae66442e03f87c4b/"},{"source":"arxiv:2408.08435","target":"arxiv:2203.11171","evidence":"title","id":"arxiv:2203.11171","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/02a15b098d96a94d463bb470f59a34723abc34e59196f35c539f9796d8f3c3df/"},{"source":"arxiv:2408.08435","target":"arxiv:2303.17651","evidence":"title","id":"arxiv:2303.17651","title":"Self-Refine: Iterative Refinement with Self-Feedback","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/accef2157d87cf33be93cb3a9af246528b0a3742a9e2e700a194956fac8f5cf4/"},{"source":"arxiv:2408.08435","target":"arxiv:2309.16797","evidence":"title","id":"arxiv:2309.16797","title":"Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/daac3bcfcd7f39084d995905e9ef96ae01c5aab7bfe8e565bd7622b4e44088f7/"},{"source":"arxiv:2408.08435","target":"arxiv:2402.16823","evidence":"title","id":"arxiv:2402.16823","title":"Language Agents as Optimizable Graphs","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/750bd3807bf9c9a9b5cd2d60cc14387431aec3fe8a3f942916ca5d8922b2a5b0/"},{"source":"arxiv:2408.08435","target":"doi:10.1038/s41586-023-06924-6","evidence":"title","id":"doi:10.1038/s41586-023-06924-6","title":"Mathematical discoveries from program search with large language models","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/70a6b427d42fd28c9d538648e148a97db2fb2614c7764f3231e53f4bebdce887/"}],"incoming":[{"source":"arxiv:2410.04444","target":"arxiv:2408.08435","evidence":"id","id":"arxiv:2410.04444","title":"Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/d04ae5d37f10547be79c728efed48a629e631ab5384155564e52558e12ff4362/"},{"source":"arxiv:2505.22954","target":"arxiv:2408.08435","evidence":"title","id":"arxiv:2505.22954","title":"Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c29f716871f80314868b5647692de387f26e12166b93bd362915fd272b6e081c/"},{"source":"arxiv:2601.14525","target":"arxiv:2408.08435","evidence":"title","id":"arxiv:2601.14525","title":"Towards Execution-Grounded Automated AI Research","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/895006e818735c9a565dbe99ae31404366eefd6bdc2278612271c65a8493e4a5/"},{"source":"arxiv:2602.07755","target":"arxiv:2408.08435","evidence":"title","id":"arxiv:2602.07755","title":"Learning to Continually Learn via Meta-learning Agentic Memory Designs","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/4d0a0ea160495ea50583e0e24027b6085e2f430a44195e3053ba69cda8cc82ea/"},{"source":"arxiv:2603.19461","target":"arxiv:2408.08435","evidence":"title","id":"arxiv:2603.19461","title":"Hyperagents","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/17eec8a15debc212b5530f117dd8ba8bad339590729cbbc4f75a47755ebcb544/"},{"source":"arxiv:2603.28052","target":"arxiv:2408.08435","evidence":"title","id":"arxiv:2603.28052","title":"Meta-Harness: End-to-End Optimization of Model Harnesses","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/8bb0b87384dfdd42c419cf26cc7e52f1f0d89f63afa5d5968e246b73a44c99cb/"},{"source":"arxiv:2604.25850","target":"arxiv:2408.08435","evidence":"title","id":"arxiv:2604.25850","title":"Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c2358d2261b7478833ca59529278f0f4872896ca5ae4434e8db5d420532e10bd/"},{"source":"arxiv:2605.13821","target":"arxiv:2408.08435","evidence":"id","id":"arxiv:2605.13821","title":"Harnessing Agentic Evolution","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1f1060a507e2bf439d2934d610827d1b3177d85c90c1a2cab483608a56de5138/"},{"source":"arxiv:2607.13104","target":"arxiv:2408.08435","evidence":"title","id":"arxiv:2607.13104","title":"Self-Improvements in Modern Agentic Systems: A Survey","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1452caf7b16ffdb8c2efd5093f46dc4df0f89c3d45a5566cd5fdafd69385b2a7/"},{"source":"arxiv:2608.08311","target":"arxiv:2408.08435","evidence":"id","id":"arxiv:2608.08311","title":"Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/4b8f53ea59f810bf1717d78f1907b7596b91df2bec6751aba12accc8598c78e9/"},{"source":"arxiv:2608.23875","target":"arxiv:2408.08435","evidence":"id","id":"arxiv:2608.23875","title":"AI Finds A Way","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/"},{"source":"arxiv:2609.01437","target":"arxiv:2408.08435","evidence":"id","id":"arxiv:2609.01437","title":"HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/0e91a157b5aa6d15aa804a3849a31a55661a73d6c1470ff918d72163f5fe2076/"},{"source":"url:lilianweng.github.io/posts/2026-07-04-harness","target":"arxiv:2408.08435","evidence":"id","id":"url:lilianweng.github.io/posts/2026-07-04-harness","title":"Harnesses for models (Lilian Weng, 2026)","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c1ea249334ba128a9fff468e25fa4f8d13d4cccd13661ee130bec020ebb6463b/"}],"text_parts":[{"start":0,"end":15893,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-1.html"},{"start":15893,"end":30870,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-2.html"},{"start":30870,"end":46533,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html"},{"start":46533,"end":54385,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html"}],"contents":[{"title":"Automated Design of Agentic Systems","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-1.html#text"},{"title":"Abstract","offset":39,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-1.html#text"},{"title":"1 Introduction","offset":2002,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-1.html#text"},{"title":"2 Automated Design of Agentic Systems (ADAS)","offset":9242,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-1.html#text"},{"title":"3 Our Algorithm: Meta Agent Search","offset":13087,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-1.html#text"},{"title":"4 Experiments","offset":15875,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-1.html#text"},{"title":"4.1 Case Study: ARC Challenge","offset":16674,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-2.html#text"},{"title":"4.2 Reasoning and Problem-Solving Domains","offset":22083,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-2.html#text"},{"title":"4.3 Generalization and transferability","offset":25567,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-2.html#text"},{"title":"5 Related Work","offset":27672,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-2.html#text"},{"title":"6 Discussion and Conclusion","offset":33178,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Acknowledgments","offset":42372,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Supplementary Material","offset":42728,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Table of Contents","offset":42755,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Appendix A Generalization and Transferability","offset":42777,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Appendix B Prompts","offset":44236,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Appendix C Framework Code","offset":45460,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Appendix D Experiment Details for ARC Challenge","offset":46481,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-3.html#text"},{"title":"Appendix E Experiment Details for Reasoning and Problem-Solving Domains","offset":47499,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html#text"},{"title":"Appendix F Baselines","offset":48452,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html#text"},{"title":"Appendix G Example Agents","offset":49971,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html#text"},{"title":"Appendix H Pseudocode of the Meta Agent Search","offset":50421,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html#text"},{"title":"Appendix I Impact of Initialization","offset":50739,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html#text"},{"title":"Appendix J Cost of Experiments","offset":52552,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html#text"},{"title":"Instructions for reporting errors","offset":53421,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/part-4.html#text"}],"text_code_points":54385,"source_markdown":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/source.md"}