Enterprise AI Analysis
Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
Executive Summary: 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. This paper introduces 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. Evaluations on challenging coding benchmarks show GEA 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. Analysis reveals that GEA more effectively converts early-stage exploratory diversity into sustained, long-term progress, achieving stronger performance with 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.
Key Executive Impact
Group-Evolving Agents (GEA) deliver substantial improvements in AI agent performance, robustness, and efficiency through innovative group-level evolution and experience sharing.
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SWE-bench Verified Performance Increase
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Polyglot Pass@1 Performance Increase
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Framework-Level Bug Fixing
Deep Analysis & Enterprise Applications
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Group-Evolving Agents: A New Paradigm
Group-Evolving Agents (GEA) redefine open-ended self-improvement by shifting the fundamental unit of evolution from individual agents to groups. This allows for explicit experience sharing and reuse within the group, consolidating diverse exploratory discoveries into sustained, long-term progress. This approach contrasts sharply with traditional tree-structured evolution, which often leads to isolated, short-lived improvements.
Unlocking Superior Performance
GEA demonstrates significant performance gains on challenging coding benchmarks. On SWE-bench Verified, GEA achieves a success rate of 71.0%, substantially outperforming state-of-the-art self-evolving methods (56.7%). For Polyglot, GEA's success rate reaches 88.3%, far exceeding the baseline (68.3%). These gains highlight GEA's efficiency in converting exploratory diversity into concrete, measurable progress, leading to capabilities that match or even surpass human-designed state-of-the-art frameworks.
Enhanced Robustness & Cross-Model Transferability
A key advantage of GEA is its improved robustness. Through group-level experience sharing, GEA agents can learn from better-performing peers to fix framework-level bugs significantly faster, requiring only 1.4 iterations on average compared to 5 for self-evolving methods. Furthermore, GEA's improvements are primarily workflow and tool enhancements, making them model-agnostic and consistently transferable across different coding models (e.g., GPT-series, Claude-series).
Shifting from Isolated Branches to Collective Intelligence
Existing open-ended self-improving systems, inspired by biological evolution, rely on individual-centric, tree-structured evolution. While this generates diversity, strict isolation between branches prevents effective information and experience sharing. GEA explicitly addresses this by treating a group as the evolutionary unit, allowing exploratory discoveries from different agents to be consolidated and accumulated, driving long-term cumulative progress rather than temporary variants.
Enterprise Process Flow: Group-Evolving Agents
GEA treats a group of agents as the fundamental unit of evolution. At each iteration, a parent group jointly gives rise to an offspring group through explicit intra-group experience sharing and reuse.
Parent Group Selection
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Performance-Novelty Algorithm
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Experience Sharing
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Open-Ended Group Evolution
88.3%
Polyglot Pass@1 Success Rate Achieved by GEA
| Feature | Tree-structured Evolution (DGM) | Group-Evolving Agents (GEA) |
|---|---|---|
| Evolutionary Unit | Individual Agent | Group of Agents |
| Experience Sharing | Limited (within lineage) | Explicit (across group) |
| Diversity Utilization | Inefficient (isolated branches) | Consolidated for long-term progress |
| SWE-bench Verified Score | 56.7% | 71.0% |
| Polyglot Score | 68.3% | 88.3% |
| Avg. Iterations to Fix Bug | 5.0 | 1.4 |
Case Study: Group-Enhanced Robustness
GEA's group-level experience sharing significantly enhances its ability to identify and fix framework-level bugs, demonstrating superior robustness compared to individual-centric approaches.
"Group-evolving agents benefit from intra-group experience sharing, enabling successful framework-level experiences from better-performing agents to guide the repair of faulty ones, confirming the robustness of the group-evolving paradigm."
1.4
GEA Avg. Iterations to Fix Bug
5.0
DGM Avg. Iterations to Fix Bug
3.57x
Faster Bug Resolution (GEA vs. DGM)
This demonstrates GEA's unique capability to leverage collective intelligence for rapid problem-solving, a critical advantage for maintaining system integrity in open-ended AI development.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A strategic outline for integrating Group-Evolving Agents into your enterprise, maximizing self-improvement and innovation.
Phase 1: Foundation & Data Integration
Integrate GEA framework with existing LLM pipelines and relevant coding benchmarks (e.g., SWE-bench, Polyglot). Establish data collection for evolutionary traces. Timeline: 1-2 Weeks
Phase 2: Group Evolution & Experience Sharing Setup
Configure multi-agent evolutionary loops, implement Performance-Novelty selection, and establish robust mechanisms for intra-group experience aggregation and sharing. Timeline: 2-4 Weeks
Phase 3: Iterative Refinement & Performance Tuning
Execute initial evolutionary runs, monitor performance on target benchmarks, and refine evolution directives for optimal self-improvement and diversity consolidation. Timeline: 4-8 Weeks
Phase 4: Robustness & Transferability Validation
Conduct thorough testing for framework-level robustness and evaluate agent transferability across diverse coding models to ensure broad applicability. Timeline: 2-4 Weeks
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