- Популярные видео
- Авто
- Видео-блоги
- ДТП, аварии
- Для маленьких
- Еда, напитки
- Животные
- Закон и право
- Знаменитости
- Игры
- Искусство
- Комедии
- Красота, мода
- Кулинария, рецепты
- Люди
- Мото
- Музыка
- Мультфильмы
- Наука, технологии
- Новости
- Образование
- Политика
- Праздники
- Приколы
- Природа
- Происшествия
- Путешествия
- Развлечения
- Ржач
- Семья
- Сериалы
- Спорт
- Стиль жизни
- ТВ передачи
- Танцы
- Технологии
- Товары
- Ужасы
- Фильмы
- Шоу-бизнес
- Юмор
Self-Evolving Multi-Agent Swarms: 74 AI Agents on a Mac Mini
Research paper on autonomous AI agent systems. 74 agents, single Mac Mini, 960MB, 35+ self-improvement cycles.
Abstract
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. Compliance scores improved from approximately 75% on day one to 92% by day five, while hallucination incidents dropped from four in the first two days to zero in the final two. These results demonstrate that Harness Engineering principles---constraints, feedback, and verification---can be fully automated at swarm scale, producing a system that does not merely execute tasks but continuously improves its own quality, safety, and capability.
Keywords: multi-agent systems, self-evolution, autonomous improvement, swarm intelligence, quality assurance, harness engineering
这篇文章介绍了一种名为 LocalKin 的自进化多智能体集群架构,该系统通过“瘦灵魂、胖技能”的设计,实现了在单台消费级电脑上高效运行 78 个低内存占用的 AI 智能体。其核心贡献在于建立了一个完全自主的质量审计与修复闭环,使系统能够无需人类干预,便能通过审计、反馈、修理和验证四个阶段持续优化自身的配置与合规性。在为期五天的实测中,该集群展现了惊人的自主学习能力,不仅显著提升了任务合规率并消除了幻觉,还通过抓取学术论文自主集成新技术,将“马具工程”的可靠性原则从理论转化为大规模自动化实践。这种架构标志着 AI 开发重心的转移:相比于精雕细琢单个提示词,构建一个能够自我迭代和自我修复的基础设施对于生产级 AI 系统而言更具价值。
Paper: https://localkin.dev/papers/self-evolving-swarms
GitHub: https://github.com/LocalKinAI/localkin
#LocalKin #AIAgents #MultiAgent #SelfEvolving
Видео Self-Evolving Multi-Agent Swarms: 74 AI Agents on a Mac Mini канала LocalKinAI
Abstract
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. Compliance scores improved from approximately 75% on day one to 92% by day five, while hallucination incidents dropped from four in the first two days to zero in the final two. These results demonstrate that Harness Engineering principles---constraints, feedback, and verification---can be fully automated at swarm scale, producing a system that does not merely execute tasks but continuously improves its own quality, safety, and capability.
Keywords: multi-agent systems, self-evolution, autonomous improvement, swarm intelligence, quality assurance, harness engineering
这篇文章介绍了一种名为 LocalKin 的自进化多智能体集群架构,该系统通过“瘦灵魂、胖技能”的设计,实现了在单台消费级电脑上高效运行 78 个低内存占用的 AI 智能体。其核心贡献在于建立了一个完全自主的质量审计与修复闭环,使系统能够无需人类干预,便能通过审计、反馈、修理和验证四个阶段持续优化自身的配置与合规性。在为期五天的实测中,该集群展现了惊人的自主学习能力,不仅显著提升了任务合规率并消除了幻觉,还通过抓取学术论文自主集成新技术,将“马具工程”的可靠性原则从理论转化为大规模自动化实践。这种架构标志着 AI 开发重心的转移:相比于精雕细琢单个提示词,构建一个能够自我迭代和自我修复的基础设施对于生产级 AI 系统而言更具价值。
Paper: https://localkin.dev/papers/self-evolving-swarms
GitHub: https://github.com/LocalKinAI/localkin
#LocalKin #AIAgents #MultiAgent #SelfEvolving
Видео Self-Evolving Multi-Agent Swarms: 74 AI Agents on a Mac Mini канала LocalKinAI
Комментарии отсутствуют
Информация о видео
4 апреля 2026 г. 12:46:14
00:07:04
Другие видео канала
















