Google's Titans & MIRAS 突破AI长期记忆瓶颈，迈向AGI新纪元
✨步子哥 (steper) •
2025年12月07日 04:52
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<title>Google's Titans & MIRAS: 突破AI长期记忆瓶颈，迈向AGI新纪元</title>
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<div class="header">
<h1 class="title">Google's Titans & MIRAS</h1>
<h2 class="subtitle">突破AI长期记忆瓶颈，迈向AGI新纪元</h2>
</div>
<div class="image-container">
<img src="https://sfile.chatglm.cn/moeSlide/image/4c/4c029f64.jpg" alt="AI Memory Architecture" class="main-image">
<p class="image-caption">Titans & MIRAS：AI记忆系统的革命性突破</p>
</div>
<div class="section">
<h3 class="section-title">
<i class="material-icons">memory</i>
当前AI的内存限制
</h3>
<div class="section-content">
<ul>
<li>Transformer技术随文本量增加<span class="highlight">速度变慢、成本升高</span>，计算复杂度呈二次增长</li>
<li>现有AI（ChatGPT、Claude、Gemini）在长对话或处理长文本时易遗忘</li>
<li>这是长期存在的基础限制，阻碍AGI发展</li>
</ul>
</div>
</div>
<div class="section">
<h3 class="section-title">
<i class="material-icons">psychology</i>
Titans架构：类人脑的三层记忆系统
</h3>
<div class="section-content">
<div class="two-column">
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<div class="card">
<div class="card-title">短期记忆（核心）</div>
处理当前任务，类似人脑短期记忆，基于注意力机制
</div>
<div class="card">
<div class="card-title">长期记忆</div>
存储历史信息，能在运行时学习和更新，使用"惊喜指标"决定存储内容
</div>
<div class="card">
<div class="card-title">持久记忆</div>
训练时固化的知识，类似人脑元记忆，保持稳定不变
</div>
</div>
<div class="column">
<img src="https://sfile.chatglm.cn/moeSlide/image/44/4403e124.jpg" alt="Brain Circuit Board" class="brain-image">
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</div>
<p><span class="highlight">MAC架构</span>：将记忆作为上下文，实现超过200万token的上下文窗口</p>
</div>
</div>
<div class="section">
<h3 class="section-title">
<i class="material-icons">schema</i>
MIRAS：统一序列模型的理论框架
</h3>
<div class="section-content">
<p>揭示所有主要AI架构（Transformer、RNN、Mamba等）本质相同，为设计更好的内存系统打开大门</p>
<div class="two-column">
<div class="column">
<div class="card">
<div class="card-title">内存架构</div>
信息存储方式（向量、矩阵、MLP）
</div>
<div class="card">
<div class="card-title">注意力偏差</div>
模型优化的内部学习目标，决定优先关注的内容
</div>
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<div class="column">
<div class="card">
<div class="card-title">保留门控</div>
平衡"学习新知识"与"保留旧记忆"的遗忘机制
</div>
<div class="card">
<div class="card-title">记忆算法</div>
更新记忆状态的优化算法
</div>
</div>
</div>
<p>创新点：引入<span class="highlight">非欧几里得目标函数</span>，允许更复杂的数学惩罚机制</p>
</div>
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<div class="section">
<h3 class="section-title">
<i class="material-icons">analytics</i>
性能验证：超越现有模型
</h3>
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<ul>
<li>在长序列上<span class="highlight">困惑度低</span>（性能好），对比Mamba，深度记忆架构在不同参数规模下均保持优势</li>
<li>在Babai Long任务（超长文档事实问答）中表现优异</li>
<li>即使模型更小（参数少、成本低），在长序列上仍保持高准确率</li>
<li>对比GPT-4、Mamba等模型有显著优势，特别是在处理超长上下文时</li>
</ul>
</div>
</div>
<div class="section">
<h3 class="section-title">
<i class="material-icons">apps</i>
实际应用场景与AGI意义
</h3>
<div class="section-content">
<div class="two-column">
<div class="column">
<ul>
<li><span class="highlight">法律文档分析</span>：处理超长法律文件</li>
<li><span class="highlight">医疗记录管理</span>：整合长期患者数据</li>
<li><span class="highlight">科研论文理解</span>：分析跨论文概念联系</li>
</ul>
</div>
<div class="column">
<ul>
<li><span class="highlight">代码库维护</span>：理解大型代码项目</li>
<li><span class="highlight">个人助理</span>：记住用户长期偏好和历史</li>
<li><span class="highlight">AGI意义</span>：实现类人脑记忆系统，开启之前不可能的应用</li>
</ul>
</div>
</div>
</div>
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<div class="section">
<h3 class="section-title">
<i class="material-icons">lightbulb</i>
结论：迈向AGI的重要一步
</h3>
<div class="section-content">
<ul>
<li>Titans和MIRAS解决了AI长期记忆的根本问题</li>
<li>实现了<span class="highlight">测试时学习</span>，模型能在使用过程中不断进化</li>
<li>为AGI发展提供了新的技术路径</li>
<li>Google再次引领AI架构创新，超越自身创造的Transformer</li>
</ul>
</div>
</div>
<div class="footer">
<p>基于Google Research论文"Titans: Learning to Memorize at Test Time"和"It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization"</p>
<p>2025年12月</p>
</div>
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