Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
Open-ended self-improving agents can autonomously modify their own structural designs to advance their capabilities and overcome the limits of pre-defined architectures, thus reducing reliance on human intervention. We introduce Group-Evolving Agents (GEA), a new paradigm for open-ended self-improvements, which treats a group of agents as the fundamental evolutionary unit, enabling explicit experience sharing and reuse within the group throughout evolution. Unlike existing open-ended self-evolving paradigms that adopt tree-structured evolution, GEA overcomes the limitation of inefficient utilization of exploratory diversity caused by isolated evolutionary branches. We evaluate GEA on challenging coding benchmarks, where it significantly outperforms state-of-the-art self-evolving methods (71.0% vs. 56.7% on SWE-bench Verified, 88.3% vs. 68.3% on Polyglot) and matches or exceeds top human-designed agent frameworks (71.8% and 52.0% on two benchmarks, respectively). Analysis reveals that GEA more effectively converts early-stage exploratory diversity into sustained, long-term progress, achieving stronger performance under the same number of evolved agents. Furthermore, GEA exhibits consistent transferability across different coding models and greater robustness, fixing framework-level bugs in 1.4 iterations on average, versus 5 for self-evolving methods.
6985980f4ad556f294b7ecb6
Zhaotian Weng
6985980f4ad556f294b7ecb7
Antonis Antoniades
6985980f4ad556f294b7ecb8
Deepak Nathani
claimed_verified
2026-03-23T11:01:24.135Z
6114c9fae7a2566ae7d1a1a7
/avatars/c71ab1850322fcf5ef239cb8d31cb137.svg
Deepak Nathani
dnathani
user
dnathani
6985980f4ad556f294b7ecb9
Zhen Zhang
6985980f4ad556f294b7ecba
Xiao Pu
6985980f4ad556f294b7ecbb
Xin Eric Wang
claimed_verified
2026-02-06T18:50:40.073Z
64679a226192d39142245e5e
/avatars/05abee0b6317f100923936ca2099e9eb.svg
Xin Eric Wang
xw-eric
user
xw-eric
Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
Open-ended self-improving agents can autonomously modify their own structural designs to advance their capabilities and overcome the limits of pre-defined architectures, thus reducing reliance on human intervention. We introduce Group-Evolving Agents (GEA), a new paradigm for open-ended self-improvements, which treats a group of agents as the fundamental evolutionary unit, enabling explicit experience sharing and reuse within the group throughout evolution. Unlike existing open-ended self-evolving paradigms that adopt tree-structured evolution, GEA overcomes the limitation of inefficient utilization of exploratory diversity caused by isolated evolutionary branches. We evaluate GEA on challenging coding benchmarks, where it significantly outperforms state-of-the-art self-evolving methods (71.0% vs. 56.7% on SWE-bench Verified, 88.3% vs. 68.3% on Polyglot) and matches or exceeds top human-designed agent frameworks (71.8% and 52.0% on two benchmarks, respectively). Analysis reveals that GEA more effectively converts early-stage exploratory diversity into sustained, long-term progress, achieving stronger performance under the same number of evolved agents. Furthermore, GEA exhibits consistent transferability across different coding models and greater robustness, fixing framework-level bugs in 1.4 iterations on average, versus 5 for self-evolving methods.
Group-Evolving Agents enable open-ended self-improvement by treating groups of agents as evolutionary units, allowing efficient experience sharing and reuse to enhance coding performance and robustness.
open-ended self-improving agents structural design modification evolutionary unit experience sharing exploratory diversity self-evolving methods coding benchmarks SWE-bench Verified Polyglot agent frameworks framework-level bugs