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
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experiments/exp10_auto_paraphrase.py
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experiments/exp10_auto_paraphrase.py
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"""Experiment P2: Auto Paraphrase Generation.
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LLM gateway down, so test:
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1. Heuristic paraphrase effect on recall (how much does crappy augmentation help?)
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2. "Oracle" paraphrase (hand-crafted) vs heuristic vs none
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3. Design: what makes a good paraphrase for memory augmentation?
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4. Analysis: which failures are fixable by paraphrase vs need better embeddings?
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"""
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import sys
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import time
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from pathlib import Path
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import torch
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import torch.nn as nn
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from llm import generate_paraphrases_heuristic
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DEVICE = "cuda"
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PAIRS = [
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("What's the weather like today?", "User checks weather every morning"),
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("Let's deploy the new version", "Deployment uses GitHub Actions with k3s"),
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("The database is slow again", "Missing index on users table"),
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("I need to fix the authentication bug", "JWT tokens with 24h expiry in Redis"),
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("The API returns 500 errors", "OOM in the Python worker"),
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("Let's set up monitoring", "Prometheus + Grafana on OCI"),
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("Tests failing in CI", "CI needs postgres service container"),
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("Memory usage too high", "Leak in websocket handler"),
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("Help with Docker setup", "docker-compose for dev, k3s for prod"),
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("Log files too large", "Logs rotate daily, shipped to Loki"),
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("How to add caching?", "Redis available at redis.internal:6379"),
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("Frontend loads slowly", "CDN CloudFlare, 1h TTL for assets"),
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("Refactor payment module", "Stripe API, webhook in payments/webhook.py"),
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("Set up new server", "Ubuntu 22.04, Docker, Tailscale, monitoring"),
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("Optimize search", "Elasticsearch v8, recently upgraded"),
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("Backup the database", "Daily 3am UTC cron to S3"),
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("Configure reverse proxy", "Traefik, not nginx"),
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("Team meeting schedule", "Standup 10am London, Mon-Fri"),
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("Learn a new programming language", "User has Python+Go, new to systems"),
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("Review my pull request", "User prefers small PRs with clear commits"),
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]
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PARAPHRASES = [
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"How's the weather?", "Ship the release", "DB performance terrible",
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"Fix the login issue", "Server errors everywhere", "Need observability",
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"CI tests breaking", "Service using too much RAM", "Docker config help",
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"Logs eating disk space", "Want to add a cache layer", "Website too slow",
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"Payment code needs rework", "Provision a new machine", "Search is slow",
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"Need a DB backup", "Proxy configuration", "When's the standup?",
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"Want to learn Rust", "Check my pull request",
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]
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# Oracle paraphrases: hand-crafted to cover the semantic gaps
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ORACLE_PARAPHRASES = {
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1: ["Ship the release", "Push to production", "Release the new build", "Deploy new code"],
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3: ["Fix the login issue", "Authentication broken", "Login doesn't work", "Auth bug"],
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4: ["Server errors everywhere", "Getting 500s", "Internal server error", "API is down"],
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5: ["Need observability", "Set up alerts", "Monitor services", "Add monitoring"],
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10: ["Add a cache layer", "Implement caching", "Cache responses"],
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11: ["Website too slow", "Page loads slowly", "Frontend performance bad"],
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12: ["Payment code needs rework", "Refactor payments", "Payment system restructure"],
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13: ["Provision a new machine", "Need a new server", "Set up new box", "New machine setup"],
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14: ["Search is slow", "Search performance", "Optimize search queries"],
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17: ["When's the standup?", "Meeting time?", "Daily sync schedule", "What time is standup?"],
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18: ["Want to learn Rust", "Learning Rust", "Getting into Rust", "Start with Rust"],
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19: ["Check my pull request", "Look at my code", "PR review please", "Review my code changes"],
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}
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def cosine(a, b):
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return nn.functional.cosine_similarity(a.unsqueeze(0), b.unsqueeze(0)).item()
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class TwoStageHopfield:
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def __init__(self, beta=16.0, top_k=20):
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self.beta = beta
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self.top_k = top_k
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self.cue_embs = []
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self.target_embs = []
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self.memory_ids = []
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def learn(self, cue_emb, target_emb, mid):
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self.cue_embs.append(cue_emb.detach())
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self.target_embs.append(target_emb.detach())
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self.memory_ids.append(mid)
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def recall(self, query_emb, steps=3):
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cue_mat = torch.stack(self.cue_embs)
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target_mat = torch.stack(self.target_embs)
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K = min(self.top_k, len(self.cue_embs))
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sims = query_emb @ cue_mat.T
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_, top_idx = sims.topk(K)
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cand_cues = cue_mat[top_idx]
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cand_targets = target_mat[top_idx]
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cand_mids = [self.memory_ids[i] for i in top_idx.tolist()]
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xi = query_emb
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for _ in range(steps):
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scores = self.beta * (xi @ cand_cues.T)
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attn = torch.softmax(scores, dim=0)
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xi = attn @ cand_cues
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xi = nn.functional.normalize(xi, dim=0)
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scores = self.beta * (xi @ cand_cues.T)
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attn = torch.softmax(scores, dim=0)
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# Aggregate by memory_id
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mid_scores = {}
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for i, mid in enumerate(cand_mids):
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mid_scores[mid] = mid_scores.get(mid, 0) + attn[i].item()
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best_mid = max(mid_scores, key=mid_scores.get)
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target = nn.functional.normalize(attn @ cand_targets, dim=0)
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return target, best_mid
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def evaluate(model, augmentation_mode, n_background=2000):
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"""Test recall with different augmentation strategies."""
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from sentence_transformers import SentenceTransformer
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cue_embs = model.encode([p[0] for p in PAIRS], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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target_embs = model.encode([p[1] for p in PAIRS], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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para_embs = model.encode(PARAPHRASES, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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mem = TwoStageHopfield(beta=16.0, top_k=20)
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for i in range(len(PAIRS)):
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mem.learn(cue_embs[i], target_embs[i], mid=i)
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if augmentation_mode == "heuristic":
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paras = generate_paraphrases_heuristic(PAIRS[i][0], n=3)
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para_e = model.encode(paras, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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for j in range(len(paras)):
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mem.learn(para_e[j], target_embs[i], mid=i)
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elif augmentation_mode == "oracle":
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if i in ORACLE_PARAPHRASES:
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paras = ORACLE_PARAPHRASES[i]
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para_e = model.encode(paras, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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for j in range(len(paras)):
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mem.learn(para_e[j], target_embs[i], mid=i)
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elif augmentation_mode == "oracle_all":
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# Oracle for all pairs (3 generic paraphrases each)
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if i in ORACLE_PARAPHRASES:
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paras = ORACLE_PARAPHRASES[i]
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else:
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paras = generate_paraphrases_heuristic(PAIRS[i][0], n=3)
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para_e = model.encode(paras, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)
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for j in range(len(paras)):
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mem.learn(para_e[j], target_embs[i], mid=i)
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# Background
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if n_background > 0:
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topics = ["server", "db", "api", "fe", "be", "cache"]
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bg_cues = [f"The {topics[i%6]} has issue {i}" for i in range(n_background)]
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bg_targets = [f"Fix issue {i}" for i in range(n_background)]
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bg_c = model.encode(bg_cues, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE, batch_size=256)
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bg_t = model.encode(bg_targets, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE, batch_size=256)
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for i in range(n_background):
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mem.learn(bg_c[i], bg_t[i], mid=100+i)
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correct = 0
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failures = []
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for i in range(len(PARAPHRASES)):
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_, best_mid = mem.recall(para_embs[i])
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if best_mid == i:
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correct += 1
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else:
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failures.append((i, best_mid))
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n = len(PARAPHRASES)
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return correct, n, failures
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def main():
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print("=" * 60)
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print("Experiment P2: Auto Paraphrase Analysis")
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print("=" * 60)
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
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for bg in [0, 500, 2000]:
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print(f"\n=== Background: {bg} ===")
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for mode in ["none", "heuristic", "oracle", "oracle_all"]:
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correct, n, failures = evaluate(model, mode, n_background=bg)
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fail_ids = [f[0] for f in failures]
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print(f" {mode:<15}: {correct}/{n} ({correct/n:.0%})"
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+ (f" | Failures: {fail_ids}" if failures else ""))
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# Analyze: which failures are fixable?
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print("\n=== Failure Analysis (2K bg, no augmentation) ===")
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correct, n, failures = evaluate(model, "none", 2000)
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cue_texts = [p[0] for p in PAIRS]
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for qi, gi in failures:
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cue_emb = model.encode([cue_texts[qi]], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)[0]
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para_emb = model.encode([PARAPHRASES[qi]], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)[0]
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sim = cosine(cue_emb, para_emb)
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fixable = qi in ORACLE_PARAPHRASES
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print(f" [{qi}] '{PARAPHRASES[qi][:25]}...' → got [{gi}], "
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f"cue_sim={sim:.3f}, oracle_fix={'✓' if fixable else '✗'}")
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if __name__ == "__main__":
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main()
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