Federation of Agents (FoA)
✨步子哥 (steper) •
2025年12月22日 08:41
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<title>Federation of Agents (FoA)</title>
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<div class="poster-container">
<div class="bg-grid"></div>
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<header class="header">
<h1 class="title">Federation of Agents (FoA)</h1>
<h2 class="subtitle">CERN引领AI从被动工具向主动「代理人」演进的革命性框架</h2>
<div class="intro">
FoA是一个分布式编排框架，将静态多代理协调转变为动态、能力驱动的协作，挑战当前单大模型主导的范式
</div>
</header>
<div class="content">
<div class="left-column">
<section class="section">
<h3 class="section-title">
<i class="material-icons">architecture</i>核心机制
</h3>
<div class="mechanism">
<h4 class="mechanism-title">Versioned Capability Vectors (VCVs)</h4>
<ul class="mechanism-desc">
<li>机器可读的代理配置文件</li>
<li>六个组成部分：密集能力嵌入、离散技能表示、资源需求向量、策略合规标识、规范嵌入、版本计数器</li>
<li>通过语义嵌入实现可搜索</li>
</ul>
</div>
<div class="mechanism">
<h4 class="mechanism-title">Semantic Routing</h4>
<ul class="mechanism-desc">
<li>使用分片HNSW索引进行语义匹配</li>
<li>考虑成本、预算和政策约束</li>
<li>选择最合适的代理组合</li>
</ul>
</div>
<div class="mechanism">
<h4 class="mechanism-title">Collaborative Refinement</h4>
<ul class="mechanism-desc">
<li>代理分组到临时集群</li>
<li>进行多轮迭代精炼</li>
<li>提升解决方案质量</li>
</ul>
</div>
</section>
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<h3 class="section-title">
<i class="material-icons">compare_arrows</i>与联邦学习的区别
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<h4 class="comparison-title">FoA</h4>
<p>关注<span class="highlight">推理时</span>的代理编排，而非模型训练</p>
<p>强调动态协作和能力匹配</p>
</div>
<div class="comparison-item">
<h4 class="comparison-title">联邦学习</h4>
<p>关注<span class="highlight">训练时</span>的分布式模型训练</p>
<p>CERN CAFEIN平台专注于联邦学习，与FoA是不同项目</p>
</div>
</div>
</section>
</div>
<div class="right-column">
<section class="section">
<h3 class="section-title">
<i class="material-icons">settings</i>技术实现
</h3>
<ul class="tech-list">
<li>基于MQTT的发布-订阅语义实现可扩展的消息传递</li>
<li>分层HNSW索引支持高效的语义搜索</li>
<li>六阶段执行流程：
<ul>
<li>任务分解</li>
<li>代理匹配</li>
<li>初稿生成</li>
<li>协作精炼</li>
<li>结果综合</li>
<li>最终交付</li>
</ul>
</li>
</ul>
</section>
<div class="image-container">
<img src="https://sfile.chatglm.cn/moeSlide/image/a8/a8341afb.jpg" alt="AI代理编排过程">
</div>
<section class="section">
<h3 class="section-title">
<i class="material-icons">analytics</i>实验结果
</h3>
<ul class="result-list">
<li>在HealthBench Hard基准上：
<ul>
<li>比最佳单模型基线提升<span class="highlight">13倍</span></li>
<li>比无协调集成提升<span class="highlight">6.5倍</span></li>
</ul>
</li>
<li>系统可水平扩展，同时保持一致性能</li>
<li>聚类增强的协作在需要多视角的复杂推理任务中特别有效</li>
</ul>
</section>
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