Self-Evolving Multi-Agent Swarms: Autonomous Quality Audit, Repair, and Verification Loops for Production AI Agent Systems
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2026
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| _version_ | 1866902294365208576 |
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| author | The LocalKin Team |
| author_facet | The LocalKin Team |
| contents | We present LocalKin, a self-evolving multi-agent swarm architecture capable of autonomously auditing, repairing, and verifying its own constituent agents without human intervention. The system runs 78 specialized agents on a single consumer machine (16GB Mac Mini) with a total memory footprint of 960MB - approximately 12.5MB per agent - compared to 200MB or more per agent in Python-based frameworks such as AutoGen and CrewAI. The core contribution is a fully autonomous improvement loop consisting of four stages: quality audit, feedback synthesis, targeted repair, and verification. Over a continuous 5-day autonomous deployment, the system completed more than 30 improvement cycles, autonomously modified 68 agent configuration files, and discovered, evaluated, and integrated techniques from 6 research papers found on arXiv and HuggingFace - all with zero human intervention.<br><br><strong>Note (2026-05-09):</strong> This version bundles English + 中文 in a single PDF (English first, then Chinese), generated directly from the canonical Markdown source files. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20098019 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Self-Evolving Multi-Agent Swarms: Autonomous Quality Audit, Repair, and Verification Loops for Production AI Agent Systems The LocalKin Team multi-agent systems self-evolution autonomous improvement swarm intelligence quality assurance harness engineering We present LocalKin, a self-evolving multi-agent swarm architecture capable of autonomously auditing, repairing, and verifying its own constituent agents without human intervention. The system runs 78 specialized agents on a single consumer machine (16GB Mac Mini) with a total memory footprint of 960MB - approximately 12.5MB per agent - compared to 200MB or more per agent in Python-based frameworks such as AutoGen and CrewAI. The core contribution is a fully autonomous improvement loop consisting of four stages: quality audit, feedback synthesis, targeted repair, and verification. Over a continuous 5-day autonomous deployment, the system completed more than 30 improvement cycles, autonomously modified 68 agent configuration files, and discovered, evaluated, and integrated techniques from 6 research papers found on arXiv and HuggingFace - all with zero human intervention.<br><br><strong>Note (2026-05-09):</strong> This version bundles English + 中文 in a single PDF (English first, then Chinese), generated directly from the canonical Markdown source files. |
| title | Self-Evolving Multi-Agent Swarms: Autonomous Quality Audit, Repair, and Verification Loops for Production AI Agent Systems |
| topic | multi-agent systems self-evolution autonomous improvement swarm intelligence quality assurance harness engineering |
| url | https://doi.org/10.5281/zenodo.20098019 |