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/p0_llm_integration.md

@@ -0,0 +1,32 @@
+# P0: LLM Integration
+
+## 状态：基础 pipeline 可用，LLM Gateway 不通需后续验证
+
+## 实现
+
+- `llm.py`: LLMClient + extract/paraphrase/format 函数
+- 支持 OpenAI-compatible API，fallback 到 heuristic
+- 端到端 pipeline: 对话 → 提取 → embed → store (with augmentation) → recall → context injection
+
+## 端到端测试结果
+
+5 轮对话存入 7 条记忆（24 个 cue entries，含 paraphrase augmentation）。
+
+查询召回结果（heuristic paraphrase）：
+| 查询 | 正确? | 说明 |
+|------|-------|------|
+| DB performance terrible | ✅ | 正确召回 missing indexes |
+| How to push a new release? | ✅ | 正确召回 blue-green deploy |
+| Redis connection info? | ✅ | 正确召回 port 6379 |
+| Login system has a problem | ❌ | 指向 database 而不是 auth |
+| Database backup | ✅ | 正确召回 cron job |
+| Deployment config? | ✅ | 正确召回 GitHub Actions |
+
+5/6 正确。失败的 case 是因为 heuristic paraphrase 没有生成 "login" ↔ "auth" 的关联。LLM paraphrase 应该能覆盖。
+
+## 待解决
+
+1. **LLM Gateway 不通** — 无法验证 LLM 提取和 paraphrase 质量
+2. **重复提取** — heuristic 会对同一对话提取 2 条相似记忆，需要去重
+3. **Heuristic paraphrase 质量差** — 机械式替换（"issue with X"）不如 LLM 生成
+4. **Auth→Login 这类语义跳跃** — 只有 LLM paraphrase 或更强 embedding 模型能解决