NuoNuo: Hippocampal memory module prototype

Hopfield + Hebbian hybrid memory system for LLMs.
Two nights of experiments (16 iterations), validated on LongMemEval (ICLR 2025).

Architecture:
- Single-hop: Two-Stage Hopfield (NN top-20 → softmax settle)
- Multi-hop: Hebbian W matrix with WTA pattern separation
- 64% on LongMemEval (500 questions), retrieval-only, no LLM dependency
- 4ms latency @ 20K memories, ~1GB VRAM

Key findings:
- Hopfield attention solved noise tolerance (20% → 100% vs flat Hebbian)
- WTA pattern separation enables 20K+ capacity
- Multi-hop associative chains (6 hops, CosSim=1.0) — RAG can't do this
- MiniLM-L6 is optimal (discrimination gap > absolute similarity)
- Paraphrase cue augmentation: 55% → 100% on synthetic, 36% → 64% on benchmark
- SNN encoder viable (CosSim 0.99) but not needed for current architecture

doc/p4_lifecycle.md

@@ -0,0 +1,35 @@
+# P4: 记忆生命周期管理
+
+## Deduplication
+
+**可行**。cosine threshold=0.7 正确识别了 2 组近似重复（9 → 6 memories）。
+- "The database is slow" / "Database is really slow today" / "DB performance terrible" → 合并
+- "The API returns 500 errors" / "Getting 500 errors from API" → 合并
+
+实现简单：pairwise cosine on cue embeddings → group → keep best per group.
+O(N²) 但可以离线做（夜间整合），或用 ANN 加速。
+
+## Importance Scoring
+
+Heuristic 规则 6/7 准确：
+- 关键词检测（crash, compromised, secret → critical）有效
+- 回答长度 > 15 词 → 更可能包含有用信息
+- 简单问答（时间、天气）正确标记为 low
+
+待 LLM 可用时，可以让 LLM 评分——更准确但有延迟。
+
+## Forgetting 策略
+
+三种策略（FIFO / LRU / 重要性加权）在当前测试中效果相同——因为没有差异化的 access pattern。
+
+实际系统中应该用 **importance + access count + recency** 的加权组合：
+```
+forget_score = age_days * 0.3 + (max_access - access_count) * 0.5 + (1 - importance) * 0.2
+```
+低分优先遗忘。
+
+## 整合到 hippocampus.py 的建议
+
+1. **Store 时**：importance scoring（heuristic 或 LLM），低于阈值不存
+2. **每晚**：deduplication（cos > 0.7 合并）+ capacity check（超限时按 forget_score 裁剪）
+3. **Recall 时**：自动 +1 access_count（已实现）