pprp · pprp · Apr 22, 2026 · Apr 23, 2026
diff --git a/_posts/2026-04-21-state-centric-serving-zh.md b/_posts/2026-04-21-state-centric-serving-zh.md
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diff --git a/docs/state-serving-review/blog.zh.md b/docs/state-serving-review/blog.zh.md
@@ -152,11 +152,11 @@
 
 图 5 和图 6 之所以重要，不是因为它们告诉我们哪个数字更大，而是因为它们把两个不同的轴分开了：
 
-![图 5：代表性系统公开给出的上下文窗口信号](assets/fig05_context_windows.png)
+![图 5：代表性系统公开给出的上下文窗口信号](assets/fig05_context_windows_zh.png)
 
 *图 5. 长上下文的公开信号仍然重要，但它只是服务系统的一部分接口。数据来自模型卡与官方文档。*
 
-![图 6：从长窗口到长时服务的产品信号](assets/fig06_service_horizon.png)
+![图 6：从长窗口到长时服务的产品信号](assets/fig06_service_horizon_zh.png)
 
 *图 6. 更值得关注的变化，是长任务被显式产品化：规划、连续执行、工具使用与长时交付正在进入主流叙述。*
 
@@ -212,7 +212,7 @@
 
 初稿中的图 2 仍然保留，因为它很好地说明了一个工程直觉：
 
-![图 2：长上下文下 KV Cache 仍会成为瓶颈](assets/fig02_kv_cache_curve.png)
+![图 2：长上下文下 KV Cache 仍会成为瓶颈](assets/fig02_kv_cache_curve_zh.png)
 
 *图 2. 即便只保留少量 attention 层，长度相关的 cache 压力依然会随着上下文增长持续放大。*
 
@@ -271,7 +271,7 @@ MiniMax 关于 M2 的复盘同样值得重视。
 
 图 1 是本文对当前路线的压缩总结。
 
-![图 1：从长上下文到 state-centric serving 的路线图](assets/fig01_architecture_landscape.png)
+![图 1：从长上下文到 state-centric serving 的路线图](assets/fig01_architecture_landscape_zh.png)
 
 *图 1. 这张图有意把“已观察到的工程响应”和“本文的前瞻性判断”分开，以避免把推演写成既成事实。*
 
@@ -570,7 +570,7 @@ HALO 进一步说明，把 Transformer 蒸馏进 Hybrid 也可以是有效路径
 
 图 3 给出了本文使用的四层记忆栈。
 
-![图 3：面向 state-centric serving 的四层记忆栈](assets/fig03_memory_stack.png)
+![图 3：面向 state-centric serving 的四层记忆栈](assets/fig03_memory_stack_zh.png)
 
 *图 3. 这张图刻意把 parameter memory、live state、external memory artifact 和 tool-ground truth 分开。因为在长时服务里，这四者的更新频率、成本、可审计性和治理方式完全不同。*
 
@@ -646,7 +646,7 @@ Anthropic 与 OpenAI 在 agent 文档里给出的实践，实际上已经越来
 
 下面这张图，把这种动态再压缩一步。
 
-![图 7：长时 Agent 的状态服务闭环](assets/fig07_state_serving_loop.png)
+![图 7：长时 Agent 的状态服务闭环](assets/fig07_state_serving_loop_zh.png)
 
 *图 7. 一次长时服务循环里，真正被反复操作的对象并不是纯文本历史，而是 live state、摘要、外部记忆、工具反馈和 carry-over residue。*
 
@@ -696,7 +696,7 @@ LoCoMo、LongMemEval、BEAM 这类工作都非常有价值。
 
 ### 7.2 图 8：从 long-context eval 到 state-serving eval
 
-![图 8：面向 state-centric serving 的评估地图](assets/fig08_evaluation_map.png)
+![图 8：面向 state-centric serving 的评估地图](assets/fig08_evaluation_map_zh.png)
 
 *图 8. 一旦系统被当作长时服务系统来构造，它就必须被问：哪些能力在 compaction 之后还能留存？哪些错误在 tool failure 后能恢复？哪些记忆写入只是 token 浪费？*
 
@@ -810,7 +810,7 @@ MCP-Atlas 之类的基准和 long-context benchmark 看起来不是一个题目
 
 ### 8.2 图 4：最小生命周期
 
-![图 4：从状态复用到状态资产化的最小生命周期](assets/fig04_state_market.png)
+![图 4：从状态复用到状态资产化的最小生命周期](assets/fig04_state_market_zh.png)
 
 *图 4. 图中“Settle”与“artifact economy”都属于本文的前瞻推演，而不是已存在的行业标准。*
 

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