285 lines
16 KiB
HTML
285 lines
16 KiB
HTML
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<title>记忆与 KV 的分工 — Natural Memory v2</title>
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<meta name="description" content="为什么不是简单加长上下文：哪些东西该留在 Qwen 的热 KV 里，哪些该写入带地址的记忆记录，各自的边界在哪里。">
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<meta property="og:description" content="为什么不是简单加长上下文：哪些东西该留在 Qwen 的热 KV 里，哪些该写入带地址的记忆记录，各自的边界在哪里。">
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<a class="skip" href="#doc">跳到正文</a>
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<header class="site" id="site">
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<div class="topbar">
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<a class="brand" href="./" aria-label="Natural Memory v2 发布页">
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<span class="brand__name">Natural Memory</span>
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<span class="pill pill--acc">v2</span>
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</a>
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<span class="crumb">
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<span class="crumb__n">01</span>
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<span class="crumb__t">记忆与 KV 的分工</span>
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<span class="topbar__spacer"></span>
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<nav class="topnav" aria-label="快速导航">
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<a class="topnav__a" href="./">发布页</a> <a class="topnav__a" href="./mechanism">机制</a> <a class="topnav__a" href="./results">测量</a> <a class="topnav__a" href="./nm21">NM2.1</a> <a class="topnav__a" href="./ledger">台账</a> <a class="topnav__a" href="./limits">边界</a> <a class="topnav__a" href="./reproduce">复现</a>
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<button class="palbtn" id="palBtn" type="button" aria-label="检索全站内容（快捷键 /）">
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<span class="palbtn__t">检索</span><span class="kbd">/</span>
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</button>
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<span class="pill" id="serverChip" title="本站服务端运行时读数">
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<i class="dot"></i><span id="serverChipText">读取中</span>
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<div class="progress" id="progress"></div>
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<nav class="side__in" aria-label="章节导航">
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<a class="side__back" href="./">← 返回发布页</a>
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<p class="side__grp">原理</p>
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<ul class="side__list">
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<li><a class="side__a is-on" href="./division" aria-current="page"><span class="side__n">01</span><span class="side__t">记忆与 KV 的分工</span></a></li>
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<li><a class="side__a" href="./mechanism"><span class="side__n">02</span><span class="side__t">一次读取与一次写入</span></a></li>
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</ul>
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<p class="side__grp">证据</p>
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<ul class="side__list">
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<li><a class="side__a" href="./results"><span class="side__n">03</span><span class="side__t">测量结果</span></a></li>
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<li><a class="side__a" href="./nm21"><span class="side__n">04</span><span class="side__t">NM2.1 · 之后做了什么</span></a></li>
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<li><a class="side__a" href="./examples"><span class="side__n">05</span><span class="side__t">实测样例</span></a></li>
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<li><a class="side__a" href="./ledger"><span class="side__n">06</span><span class="side__t">真实评测台账</span></a></li>
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</ul>
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<p class="side__grp">工程</p>
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<ul class="side__list">
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<li><a class="side__a" href="./cost"><span class="side__n">07</span><span class="side__t">代价账本</span></a></li>
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<li><a class="side__a" href="./capacity"><span class="side__n">08</span><span class="side__t">容量与存储</span></a></li>
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<li><a class="side__a" href="./limits"><span class="side__n">09</span><span class="side__t">我们不宣称的内容</span></a></li>
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<li><a class="side__a" href="./roadmap"><span class="side__n">10</span><span class="side__t">已解决的与下一步</span></a></li>
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<li><a class="side__a" href="./reproduce"><span class="side__n">11</span><span class="side__t">复现实验</span></a></li>
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</ul>
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</nav>
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</aside>
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<main class="doc" id="doc">
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<header class="doc__hd">
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<span class="kicker">01 / DIVISION OF LABOUR</span>
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<h1 class="doc__h">记忆与 KV 的分工</h1>
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<p class="doc__lede">为什么不是简单加长上下文：哪些东西该留在 Qwen 的热 KV 里，哪些该写入带地址的记忆记录，各自的边界在哪里。</p>
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<p class="doc__figure">
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<span class="mega">441.19<small>tok</small></span>
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<span class="doc__figure-n">批次 B 平均输入前缀 token</span>
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</p>
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</header>
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<p class="lede2">
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这一节回答一个问题：<strong>既然模型已经有上下文窗口，为什么还要单独做一层记忆？</strong>
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答案不是「窗口不够长」，而是<strong>把长期事实塞进上下文，会以线性成本换来极低的复用率</strong>。
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下面把「哪些东西留在 Qwen 的热 KV 里、哪些写进记忆记录」这条分界线逐层讲清楚。
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</p>
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<h2 class="h3" >先看没有这层记忆时会发生什么</h2>
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<p class="small" style="max-width:78ch">
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同一套题、同一协议下，原版 Qwen3.5-4B 在 246 道<strong>可回答</strong>题上只答对 3 道（1.22%）。
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它的典型回答不是答错，而是明确表示拿不到信息：
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</p>
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<div class="quote" >
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<q>作为一个人工智能模型，我无法访问您的个人历史对话记录、系统配置或您之前设定的具体规则……每次对话对我来说都是全新的开始。</q>
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<cite>原版 Qwen3.5-4B · 评测样本 user-009 原始回答</cite>
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</div>
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<p class="small" style="max-width:78ch">
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这句话在事实上完全正确——模型确实没有这些信息。问题出在：这些信息<strong>本来就应该被保存下来</strong>，
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而它们不属于「当前这轮对话」，所以每轮都重新粘一遍历史是最笨的保存方式。
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</p>
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<h2 class="h3" >四层分工：谁存什么、为什么</h2>
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<p class="small" style="max-width:78ch">
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这是整份设计里最核心的一张表。判断标准只有一条：<strong>这份信息是「这一轮才有意义」还是「跨轮次可复用」</strong>。
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</p>
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<div class="scrollx">
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<div class="gt gt--4">
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<div class="gt__h"><span>层级</span><span>存放什么</span><span>由谁负责</span><span>为什么放在这一层</span></div>
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<div class="gt__r">
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<span class="gt__k">最近对话</span>
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<span class="gt__v">这一轮前后的原话与顺序</span>
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<span class="gt__who">Qwen 热 KV</span>
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<span class="gt__why">指代、省略、语气、"你刚才说的那句"都依赖严格的先后顺序。这类信息复用价值低、时效性强，放 KV 最自然。</span>
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</div>
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<div class="gt__r">
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<span class="gt__k">长期个人 / 项目事实</span>
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<span class="gt__v">事实、版本、来源、置信度、短文本证据</span>
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<span class="gt__who gt__who--acc">Natural Memory 记录</span>
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<span class="gt__why">跨会话反复被问到，且需要保留"哪个版本才是当前有效"的关系。它们不依赖顺序，只依赖地址与内容。</span>
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</div>
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<div class="gt__r">
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<span class="gt__k">当前问题</span>
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<span class="gt__v">由问题生成的有界读取</span>
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<span class="gt__who gt__who--acc">路由 + Top-K</span>
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<span class="gt__why">不是所有记忆都该被读。当前问题只与少量页面和记录交互，避免"记忆越多、噪声越大"。</span>
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</div>
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<div class="gt__r">
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<span class="gt__k">原始持久化状态</span>
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<span class="gt__v">记忆快照与索引</span>
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<span class="gt__who gt__who--acc">嵌入式 memory safetensors</span>
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<span class="gt__why">随模型包一起加载，运行时不需要额外数据库进程，也避免了磁盘分页带来的延迟抖动。</span>
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</div>
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</div>
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</div>
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<div class="callout" >
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<p class="callout__t">关键判断</p>
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<p class="callout__b">
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Memory Slot <strong>不试图逐 token 模拟完整 KV</strong>。它接管的是 KV 里最昂贵、最适合长期保存、
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也最容易重复利用的那一部分；而当前对话的顺序关系，仍然由 Qwen 的原生上下文负责。
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</p>
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</div>
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<h2 class="h3" >分工落到数字上是什么样</h2>
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<p class="small" style="max-width:78ch">
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下面这组对照最能说明「不是把历史灌进去，而是把命中的少量证据灌进去」。两组数字分别来自批次 A 与批次 B（同一题集、同一协议）。
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</p>
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<div class="statrow">
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<div class="stat">
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<span class="stat__k">无记忆时的平均输入</span>
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<span class="stat__v">30.75<small>token</small></span>
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<span class="stat__d">批次 A 基线：只有问题本身，没有历史</span>
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</div>
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<div class="stat stat--acc">
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<span class="stat__k">有记忆时的平均输入</span>
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<span class="stat__v">441.19<small>token</small></span>
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<span class="stat__d">批次 B：命中的证据前缀（批次 A 为 641.42）</span>
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</div>
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<div class="stat">
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<span class="stat__k">前缀命中率</span>
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<span class="stat__v">96.97<small>%</small></span>
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<span class="stat__d">批次 B 实际注入证据的题目占比（批次 A 为 90.91%）</span>
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</div>
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<div class="stat">
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<span class="stat__k">GPU cache 实际用量</span>
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<span class="stat__v">17,990<small>token</small></span>
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<span class="stat__d">有界上限 131,072 token，用到约 13.7%</span>
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</div>
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</div>
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<p class="small" style="max-width:78ch">
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注意最后一项：<strong>上限是 131,072 token，实际只用了 17,990</strong>。
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这说明读取路径被"有界"约束住了——不会因为记忆库变大就把显存吃满。
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</p>
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<h2 class="h3" >这条分工线换来了什么、付出了什么</h2>
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<div class="grid g2" >
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<div class="card">
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<p class="eyebrow" >换来的</p>
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<div class="mini">
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<div><span>可回答正确率</span><b>1.22% → 82.52%</b></div>
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<div><span>目标记忆召回</span><b>0% → 92.68%</b></div>
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<div><span>符号定位（192 题）</span><b>0/192 → 181/192</b></div>
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<div><span>跨会话问题</span><b>1/6 → 5/6</b></div>
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</div>
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</div>
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<div class="card card--warn">
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<p class="eyebrow" style="color:var(--warn)">付出的</p>
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<div class="mini">
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<div><span>平均输入前缀</span><b>30.75 → 441.19 token</b></div>
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<div><span>平均总延迟（批次 B）</span><b>2854.9 → 2878.3 ms</b></div>
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<div><span>平均解码速度（批次 B）</span><b>22.34 → 20.68 tok/s</b></div>
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<div><span>reserved 显存峰值</span><b>3.234 → 5.180 GiB</b></div>
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</div>
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</div>
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</div>
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<h2 class="h3" >这条线不能推到哪里</h2>
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<div class="claims claims--loose" >
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<div class="claim"><span class="claim__x">✕</span><span class="claim__t">Natural Memory 已经等价于百万 token 的完整 KV</span><span class="claim__s">UNSUPPORTED</span></div>
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<div class="claim"><span class="claim__x">✕</span><span class="claim__t">长上下文限制已被解除（实测 8K 可跑，16K / 32K OOM）</span><span class="claim__s">UNSUPPORTED</span></div>
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<div class="claim"><span class="claim__x">✕</span><span class="claim__t">设计容量 1,048,576 条记录等于已经装入并验证了这么多</span><span class="claim__s">UNSUPPORTED</span></div>
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</div>
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<p class="small" style="max-width:78ch">
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第三条尤其要注意：<strong>1,048,576 是地址空间与分页结构的容量</strong>，
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本次两个批次实际装载的是 723 / 738 条 active records，分别是 23 / 24 个页面。
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容量上限与已装入量是两个概念，我们不在页面上把它们混为一谈。
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</p>
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<nav class="pn" aria-label="章节切换"><a class="pn__a pn__a--prev" href="./" data-dir="prev"><span class="pn__k">上一节 · 00</span><span class="pn__t">总览</span><span class="kbd">←</span></a><a class="pn__a pn__a--next" href="./mechanism" data-dir="next"><span class="pn__k">下一节 · 02</span><span class="pn__t">一次读取与一次写入</span><span class="kbd">→</span></a></nav>
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<footer class="footer">
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<div class="wrap footer__in">
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<div class="footer__col">
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<p class="footer__h">作者</p>
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<div class="author">
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<a class="author__a" href="https://wpyw.site" target="_blank" rel="noopener">
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<span class="author__k">个人网站</span><span class="author__v">wpyw.site</span>
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</a>
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<a class="author__a" href="mailto:[email protected]">
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<span class="author__k">邮箱</span><span class="author__v">[email protected]</span>
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<p class="footer__h">来源</p>
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<div class="readout" id="footFiles"></div>
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<p class="footer__h" style="margin-top:20px">服务端读数</p>
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<div class="readout mono" id="footServer"><span>读取中…</span></div>
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<p class="footer__h">说明</p>
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<p class="footer__note" id="disclaimer"></p>
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<p class="footer__meta">构建于 2026-09-16 20:39 · 所有数字取自工程内的正式报告，未做外推或估算。</p>
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