d923aa1e31
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
89 lines
2.8 KiB
Markdown
89 lines
2.8 KiB
Markdown
# 实验7：Hopfield + 网络结构探索
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## 背景
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exp02-06 的核心问题：Hebbian W 矩阵做模糊单跳检索在大规模下失败（SNR 不够）。
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Fam 指出这是**网络结构问题**，不是 hash 函数问题。
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## 7a: 架构对比
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| 架构 | bg=0 | bg=100 | bg=500 | bg=1000 |
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|------|------|--------|--------|---------|
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| Flat Hebbian | 80% | 60% | 30% | 20% |
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| Attractor (auto+hetero) | 90% | 40% | 30% | 10% |
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| **Hopfield (β=16)** | **100%** | **90%** | **90%** | **100%** |
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| Recurrent+inhibition | 20% | 20% | 10% | 10% |
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**Hopfield 完胜。** softmax attention 天然解决了归一化和锐化问题。
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## 7b: Hopfield 深入测试
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- **Multi-hop**: 3 跳 × 3 链 + 200 bg = 全部 sim=1.0 ✓
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- **Scale (code space)**: 100+ bg 后不稳定（60-80%）
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- **Hard distractors**: 高 β 下被语义相似的干扰项吸走
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- **关键发现**: WTA code 空间的距离不忠实于语义距离
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## 7c: Embedding-Space Hopfield
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直接在 embedding 空间做 Hopfield attention（不经过 WTA）：
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- 比 code-space 在中等规模（≤2K）更稳定
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- Multi-hop 在 embedding 空间也完美（500 bg, sim=1.0）
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- Hard distractors 在 β=8 时正确（attention 分散但正确）
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## 7d: Two-Stage 检索
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NN pre-filter (top-K) → Hopfield settle on candidates:
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| N | K=20 | K=50 | 延迟 |
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|---|------|------|------|
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| 110 | 90% | 90% | 1ms |
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| 1010 | 80% | 80% | 1ms |
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| 5010 | 80% | 70% | 2ms |
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| 10010 | 80% | 70% | 2ms |
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| 20010 | **80%** | 70% | 4ms |
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**K=20 最稳定**：20K 规模下 80%，4ms。
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Diverse query test (20 对 + 2000 bg): 70% baseline → 分析 failure 发现是 embedding 模型质量问题。
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## 7e: Cue Augmentation ⭐
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| 方法 | 准确率 (20 对 + 2000 bg) |
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|------|------------------------|
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| 无 augmentation | 70% |
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| Noise augmentation (各种参数) | 70% |
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| **Paraphrase augmentation** | **95%** |
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Noise 完全无效（高斯噪声 ≠ 真实 paraphrase 方向）。
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Hand-crafted paraphrase 直接 70% → 95%。
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实际系统中让 LLM 生成 3-5 个 paraphrase 一起存。
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## 最终架构
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```
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Query → Two-Stage Hopfield (NN top-20 → softmax settle) → Target
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↓
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Hebbian W matrix (multi-hop chain from settled cue)
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```
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### 组件职责
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| 组件 | 功能 | 容错 |
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|------|------|------|
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| Hopfield attention | 单跳检索 | 噪声/paraphrase 容忍 |
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| Cue augmentation | 扩大记忆覆盖 | 弥补 embedding 模型不足 |
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| NN pre-filter | 缩小候选集 | O(N) → O(K) |
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| Hebbian W | 多跳联想 | 精确 cue 下完美 |
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| WTA separation | 稀疏编码 | 20K+ 容量 |
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### 性能指标
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| 指标 | 数值 |
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|------|------|
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| Paraphrase recall (+ augmentation) | 95% |
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| Multi-hop (3 hops, 500 bg) | 100% |
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| Scale (20K memories) | 80% |
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| Latency (20K) | 4ms |
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| VRAM (W=16384²) | 1GB |
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