docs: update vectorization algorithm - LocalWordEmbedder (jieba + TF-IDF + PMI co-occurrence)

Context layer uses LocalWordEmbedder not plain TF-IDF:
- jieba segmentation, TF-IDF weights, sliding window PMI co-occurrence
- Document/Indexer layers still use char-bigram TF-IDF (TFIDFVectorizer)
- Add 'protect last 10 events' detail to Context pruning description

docs/en/ARCHITECTURE.md

@@ -117,13 +117,25 @@ Setting `ctx.Response` at any stage jumps to `after_output`.
      → triples → GraphDB.Commit
 ```
 
-### TF-IDF Vectorization (char 1-2 gram)
+### Vectorization: Two Strategies
 
-TF-IDF is the core algorithm running through all three memory layers, used in 4 independent locations:
+Vectorization is used in 4 independent locations with different strategies:
+
+**Strategy A — Local Word Embedding** (`LocalWordEmbedder`, `internal/memory/embedder.go`), used by Context layer:
+
+- **jieba tokenization** → removes stop words and single characters
+- **TF-IDF** as base word weight
+- **Sliding window (size=5)** counts word co-occurrence → **PMI (Pointwise Mutual Information)** → keeps top 50
+- **Vectorization**: `vec[ctx] += TF-IDF × PMI` + self-tag `__w__` + TF-IDF
+
+**Strategy B — char-bigram TF-IDF** (`TFIDFVectorizer`, `internal/memory/vector/`), used by Document and Indexer layers:
+
+- **char bigram tokenization** (1-2 gram)
+- **TF-IDF weights** + **inverted index**
 
 | Location | File | Purpose | Algorithm |
 |----------|------|---------|-----------|
-| Context Prune | `context.go:161` | Trim low-relevance context events | CosineSimilarity(queryVec, evt.Vector) |
+| Context Prune | `context.go:161` | Trim low-relevance context events | LocalWordEmbedder → CosineSimilarity(queryVec, evt.Vector) |
 | DocStore Query | `document.go:198` | Recall related content from document memory | InvertedIndex + CosineSimilarity |
 | Indexer Entity Search | `indexer.go:149` | Recall related entities from Graph | InvertedIndex + CosineSimilarity |
 | Entity Similarity Detection | `agent.go:2297` | Detect similar entities in Graph | Bigram Jaccard (>0.75 → consolidation) |
@@ -132,7 +144,8 @@ TF-IDF is the core algorithm running through all three memory layers, used in 4
 
 `internal/agent/core/context.go` — `RelevanceContext`
 - Maintains recent event list, writes JSON on each Append/Prune to prevent data loss
-- TF-IDF relevance scoring on user input, keeps topK
+- Word embedding relevance scoring on user input (LocalWordEmbedder → CosineSimilarity), keeps topK
+- Protects last 10 events from eviction; excess candidates are sorted by relevance and archived to document memory
 
 ### Document Layer
 

docs/en/OVERVIEW.md

@@ -19,7 +19,7 @@ Core architecture: a long-running kernel process (`homed`) that connects to vari
 The significance: the kernel stays pure (zero IO, only orchestration and memory), plugins stay flexible (each does its job, hot-loadable), with no cross-contamination.
 
 **Three-Layer Memory Architecture** — Solves the memory decay problem for long-running agents:
-- **Context Layer**: In-memory TF-IDF scored event window, maintains recent context in real-time, low-relevance events automatically sink to the next layer
+- **Context Layer**: In-memory local word embedding scored event window (jieba + TF-IDF + PMI → CosineSimilarity), maintains recent context in real-time, low-relevance events automatically sink to the next layer
 - **Document Layer**: JSON files + TF-IDF vector-indexed temporary memory, supports explicit submission and implicit archival, cold data distills to Graph
 - **Graph Layer**: SQLite graph database, persists entities and relations, BFS traversal recall, distillation pipeline extracts triples from conversations
 
@@ -33,7 +33,7 @@ Code is in the project root, implemented in Go.
 - Maintains a message loop (`eventLoop`), queuing input from the IO layer
 - Each input goes through the full processing pipeline: memory recall → persona injection → LLM call → tool execution → output delivery
 - LLM calls abstracted through Provider interface, supports 8 LLM sources with automatic fallback
-- Context management (`context.go`) based on TF-IDF scoring, automatic pruning of low-relevance events
+- Context management (`context.go`) based on word embedding scoring (LocalWordEmbedder → CosineSimilarity), automatic pruning of low-relevance events
 
 **Memory System** (`internal/memory/`):
 - **GraphDB** (`graph.go`) — SQLite, entities + relations tables, BFS traversal