docs(architecture): 记忆流转图对齐统一多模态空间

流程图里 Context/Prune/DocStore 三行仍只写 StaticEmbedder 与 TF-IDF，
读起来像"向量化只有词嵌入一条路"，与 1.2.0 实际（多模态统一空间为主，
带 fingerprint；词嵌入/TF-IDF 是降级层）不符。

- Context Append：补三层向量层级说明
- Context Prune：改为 DenseCosine（仅同指纹比较）→ StaticEmbedder 回退
- DocStore：改为稠密向量 + dense_fp 同指纹要求（不符即重算）
- 中英双版同步

assets/docs/en/ARCHITECTURE.md

@@ -90,8 +90,8 @@ Setting `ctx.Response` at any stage jumps to `after_output`.
    RelevanceContext — In-memory events[] + JSON persistence
    Append: Each input, CleanTemplateText → three-branch vector(textForVector)
            agent→Response, user→Input, cold_storage→Input+Response
-           StaticEmbedder pretrained word embedding / TF-IDF fallback
-   Prune:  StaticEmbedder CosineSimilarity, keep topK + last 10
+           Vector layers: unified multimodal space (primary, with fingerprint) → StaticEmbedder word embedding → TF-IDF (fallback)
+   Prune:  DenseCosine (compared only within the same fingerprint) → StaticEmbedder CosineSimilarity fallback; keep topK + last 10
        ├── Keep → timeline → chronologically sorted → system prompt
        └── Low score → Document layer archive (original timestamp)
    Save: 5s debounce write to disk
@@ -99,7 +99,7 @@ Setting `ctx.Response` at any stage jumps to `after_output`.
          ↓ Prune archive                       ↑ LLM active recall
 
 ② Document (File Memory)
-   DocStore — JSON files + shared StaticEmbedder vector space with Context (fallback: TF-IDF InvertedIndex)
+   DocStore — JSON files + dense vectors (unified multimodal space; dense_fp must match the current space fingerprint or the doc is recomputed; fallback: StaticEmbedder / TF-IDF InvertedIndex)
    Write: Prune archive / doc_commit / Graph snapshot (syncGraphToDocs)
    Read:
        ├── Auto-inject: Query(input, top3) → similarity summary under same vector space → [Related Memory Docs] → system prompt (read-only)

assets/docs/zh/ARCHITECTURE.md

@@ -90,8 +90,8 @@ eventLoop() → processTextInput()
    RelevanceContext — 内存 events[] + JSON持久化
    Append: 每次输入, CleanText → 三分支向量(textForVector)
             agent事件→Response, 用户事件→Input, cold_storage→Input+Response
-            StaticEmbedder 预训练词嵌入 / TF-IDF 回退
-   Prune:  StaticEmbedder CosineSimilarity, 保留 topK + 最近10条
+            向量层级：统一多模态空间（主，带 fingerprint）→ StaticEmbedder 词嵌入 → TF-IDF（回退）
+   Prune:  DenseCosine（仅同指纹才比较）→ 退化 StaticEmbedder CosineSimilarity；保留 topK + 最近10条
        ├── 保留 → timeline → 按时间排序 → system prompt
        └── 低分 → Document 层归档 (原始时间戳)
    Save: 5s debounce 写盘
@@ -99,7 +99,7 @@ eventLoop() → processTextInput()
          ↓ Prune 归档                          ↑ LLM 主动召回
 
 ② Document (文件记忆)
-   DocStore — JSON文件 + 与 Context 共享的 StaticEmbedder 向量空间（兜底: TF-IDF InvertedIndex）
+   DocStore — JSON文件 + 稠密向量（统一多模态空间；dense_fp 须与当前空间同指纹，不符即重算；兜底: StaticEmbedder / TF-IDF InvertedIndex）
    写入: Prune归档 / doc_commit / Graph快照(syncGraphToDocs)
    读取:
        ├── 自动注入: Query(input, top3) → 同一向量空间下相似度摘要 → 【相关记忆文档】→ system prompt (只读)