AI agents aren't biological individuals. So why make them evolve like one? They can directly share experience and learned artifacts. Not bounded by reproduction, lineage, or genes. Meet 𝗚𝗘𝗔, accepted to 𝗖𝗢𝗟𝗠 𝟮𝟬𝟮𝟲 🎉 𝟳𝟭.𝟬% SWE-bench Verified / 𝟴𝟴.𝟯% Polyglot · zero human intervention 🧵👇 Most self-evolving agent systems follow a similar pattern: select a parent, refine it, produce an offspring, repeat. Evolution unfolds as a tree. It's great at generating diversity, but that diversity gets trapped. Agents explore independently, and instead of serving as stepping stones, their discoveries stay stuck in local branches. Most variants are short-lived. 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗵𝗮𝗽𝗽𝗲𝗻𝘀; 𝗿𝗲𝘂𝘀𝗲 𝗮𝗻𝗱 𝗮𝗰𝗰𝘂𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗿𝗮𝗿𝗲𝗹𝘆 𝗱𝗼. It's time to rethink the evolution of AI agents: why keep evolving them like biological individuals? AI agents aren't bound by reproduction, lineage, or genes. They can directly share trajectories, tools, workflows, and learned artifacts, aggregating complementary skills instantly. 𝗪𝗵𝘆 𝗻𝗼𝘁 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗮𝗿𝗼𝘂𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼? That's GEA. GEA makes a 𝗴𝗿𝗼𝘂𝗽 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘂𝗻𝗶𝘁 𝗼𝗳 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻. Each round, a parent group is selected under a Performance-Novelty criterion that balances competence with exploratory diversity. All members within the group then pool their experience, model patches, failure modes, eval logs, and solutions, into a shared pool, and the whole group jointly produces the next generation. Exploration is no longer wasted. It gets consolidated. The results show a significant improvement over prior state-of-the-art self-evolving methods, and GEA matches or surpasses top human-designed frameworks with 𝗻𝗼 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽. The analysis is the more interesting part. GEA's gains come from explicitly reusing diversity, not from lucky outliers: stronger performance under the same number of evolved agents, more robust to framework-level bugs (repaired in 𝟭.𝟰 iterations vs 𝟱), and improvements that target workflows and tools rather than overfitting to one model, so they transfer consistently across GPT- and Claude-series backbones. The key to open-ended evolution is not only generating enough diversity. What matters more is whether discoveries accumulate and get reused. 𝗚𝗘𝗔 𝗹𝗲𝘁𝘀 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝘀𝘁𝗮𝗿𝘁 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴. 📄 arXiv: arxiv.org/abs/2602.04837 Grateful to my advisor @xwang_lk and my wonderful coauthors @anton_iades @deepaknathani11 @zhenzhangzz @XiaoSophiaPu . Interning in Palo Alto this summer. See you at COLM 2026 👋

AI agents aren't biological individuals. So why make them evolve like one? They can directly share experience and learned artifacts. Not bounded by reproduction, lineage, or genes. Meet 𝗚𝗘𝗔, accepted to 𝗖𝗢𝗟𝗠 𝟮𝟬𝟮𝟲 🎉 𝟳𝟭.𝟬% SWE-bench Verified / 𝟴𝟴.𝟯% Polyglot · zero human intervention 🧵👇 Most self-evolving agent systems follow a similar pattern: select a parent, refine it, produce an offspring, repeat. Evolution unfolds as a tree. It's great at generating diversity, but that diversity gets trapped. Agents explore independently, and instead of serving as stepping stones, their discoveries stay stuck in local branches. Most variants are short-lived. 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗵𝗮𝗽𝗽𝗲𝗻𝘀; 𝗿𝗲𝘂𝘀𝗲 𝗮𝗻𝗱 𝗮𝗰𝗰𝘂𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗿𝗮𝗿𝗲𝗹𝘆 𝗱𝗼. It's time to rethink the evolution of AI agents: why keep evolving them like biological individuals? AI agents aren't bound by reproduction, lineage, or genes. They can directly share trajectories, tools, workflows, and learned artifacts, aggregating complementary skills instantly. 𝗪𝗵𝘆 𝗻𝗼𝘁 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗮𝗿𝗼𝘂𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼? That's GEA. GEA makes a 𝗴𝗿𝗼𝘂𝗽 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘂𝗻𝗶𝘁 𝗼𝗳 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻. Each round, a parent group is selected under a Performance-Novelty criterion that balances competence with exploratory diversity. All members within the group then pool their experience, model patches, failure modes, eval logs, and solutions, into a shared pool, and the whole group jointly produces the next generation. Exploration is no longer wasted. It gets consolidated. The results show a significant improvement over prior state-of-the-art self-evolving methods, and GEA matches or surpasses top human-designed frameworks with 𝗻𝗼 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽. The analysis is the more interesting part. GEA's gains come from explicitly reusing diversity, not from lucky outliers: stronger performance under the same number of evolved agents, more robust to framework-level bugs (repaired in 𝟭.𝟰 iterations vs 𝟱), and improvements that target workflows and tools rather than overfitting to one model, so they transfer consistently across GPT- and Claude-series backbones. The key to open-ended evolution is not only generating enough diversity. What matters more is whether discoveries accumulate and get reused. 𝗚𝗘𝗔 𝗹𝗲𝘁𝘀 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝘀𝘁𝗮𝗿𝘁 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴. 📄 arXiv: arxiv.org/abs/2602.04837 Grateful to my advisor @xwang_lk and my wonderful coauthors @anton_iades @deepaknathani11 @zhenzhangzz @XiaoSophiaPu . Interning in Palo Alto this summer. See you at COLM 2026 👋