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
259 lines
9.4 KiB
Python
259 lines
9.4 KiB
Python
"""Experiment P4: Memory Lifecycle Management.
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Questions:
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1. What's worth storing? (not everything in a conversation is a "memory")
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2. When to forget? (access-based decay, age-based decay, capacity pressure)
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3. Can we merge similar memories? (deduplification / compression)
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4. Importance scoring: how to prioritize during recall and forgetting?
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Strategy: implement and test each mechanism, measure impact on recall quality.
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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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from collections import Counter
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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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from nuonuo.hippocampus import HippocampalMemory
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DEVICE = "cuda"
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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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def load_model():
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from sentence_transformers import SentenceTransformer
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return SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
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def emb(model, text):
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return model.encode([text], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)[0]
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def test_deduplication(model):
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"""Test: can we detect and merge duplicate/near-duplicate memories?"""
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print("=== Test 1: Deduplication ===\n")
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mem = HippocampalMemory(embed_dim=384)
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# Store some memories with near-duplicates
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memories = [
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("The database is slow", "Check missing indexes"),
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("Database is really slow today", "Check missing indexes on users table"), # near-dup
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("DB performance is terrible", "Look at index usage"), # near-dup
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("Deploy to production", "Use blue-green deployment"),
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("Push to prod", "Blue-green deployment via GitHub Actions"), # near-dup
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("The API returns 500 errors", "Check for OOM in Python worker"),
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("Getting 500 errors from API", "Python worker might be OOM"), # near-dup
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("Set up monitoring", "Prometheus + Grafana"),
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("We need better observability", "Set up Prometheus and Grafana"), # near-dup
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]
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for cue, target in memories:
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mem.store(emb(model, cue), emb(model, target),
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metadata={"cue": cue, "target": target})
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print(f" Before dedup: {len(mem.memories)} memories")
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# Detect near-duplicates by cue similarity
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entries = list(mem.memories.values())
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groups = []
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used = set()
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for i, e1 in enumerate(entries):
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if i in used:
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continue
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group = [i]
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for j, e2 in enumerate(entries):
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if j <= i or j in used:
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continue
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sim = cosine(e1.cue_embedding, e2.cue_embedding)
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if sim > 0.7: # threshold for "near-duplicate"
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group.append(j)
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used.add(j)
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groups.append(group)
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used.add(i)
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print(f" Found {len(groups)} groups (from {len(entries)} memories):")
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for group in groups:
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if len(group) > 1:
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cues = [entries[i].metadata.get("cue", "?") for i in group]
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print(f" Group ({len(group)}): {[c[:30] for c in cues]}")
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# Merge: keep the one with longest target (most info)
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to_remove = []
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for group in groups:
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if len(group) > 1:
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# Keep the one with longest target text
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best = max(group, key=lambda i: len(entries[i].metadata.get("target", "")))
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for i in group:
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if i != best:
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to_remove.append(entries[i].memory_id)
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for mid in to_remove:
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mem.forget(mid)
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print(f" After dedup: {len(mem.memories)} memories")
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print(f" Removed {len(to_remove)} duplicates")
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def test_importance_scoring(model):
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"""Test: importance-based memory management."""
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print("\n=== Test 2: Importance Scoring ===\n")
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# Simulate conversation with varying importance
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conversations = [
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# (user, assistant, expected_importance)
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("Hi there!", "Hello! How can I help?", "low"),
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("What's the weather?", "It's sunny today.", "low"),
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("The production database crashed at 3am", "Emergency: restore from latest backup at s3://backups/db-latest.sql", "high"),
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("What time is it?", "It's 3:45 PM.", "low"),
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("The auth service JWT secret was compromised", "Rotate secret immediately: kubectl set env deployment/auth JWT_SECRET=new_value", "critical"),
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("Deploy the hotfix", "Deployed via GitHub Actions, monitor Grafana for 30 min", "high"),
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("Thanks for your help", "You're welcome!", "low"),
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]
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def score_importance(user_msg, assistant_msg):
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"""Simple heuristic importance scoring."""
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score = 0.3 # base
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# Length suggests complexity
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if len(assistant_msg.split()) > 15:
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score += 0.2
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# Technical keywords
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critical_words = ["crash", "emergency", "compromised", "secret", "password",
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"production", "outage", "down", "data loss"]
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high_words = ["deploy", "config", "fix", "bug", "error", "migrate",
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"backup", "restore", "rollback"]
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for w in critical_words:
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if w in (user_msg + assistant_msg).lower():
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score += 0.3
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for w in high_words:
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if w in (user_msg + assistant_msg).lower():
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score += 0.1
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# Questions suggest retrievable info
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if "?" in user_msg:
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score += 0.1
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return min(score, 1.0)
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for user, assistant, expected in conversations:
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score = score_importance(user, assistant)
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status = "✓" if (expected == "low" and score < 0.5) or \
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(expected == "high" and 0.5 <= score < 0.8) or \
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(expected == "critical" and score >= 0.8) else "✗"
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should_store = score >= 0.4
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print(f" {status} [{score:.2f}] {'STORE' if should_store else 'SKIP ':>5} "
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f"({expected:>8}) '{user[:40]}...'")
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def test_forgetting_strategies(model):
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"""Test: different forgetting strategies under memory pressure."""
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print("\n=== Test 3: Forgetting Strategies ===\n")
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# Simulate 7 days of memories, each day 10 memories
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days = 7
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per_day = 10
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max_capacity = 30 # Force forgetting after 30 memories
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cue_template = "Day {day} task {i}: {topic}"
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target_template = "Solution for day {day} task {i}"
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topics = ["database", "deploy", "monitoring", "auth", "API",
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"caching", "logging", "testing", "docker", "CI/CD"]
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def run_strategy(strategy_name, forget_fn):
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mem = HippocampalMemory(embed_dim=384)
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day_memories = {} # day → list of memory_ids
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for day in range(1, days + 1):
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day_memories[day] = []
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for i in range(per_day):
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cue = cue_template.format(day=day, i=i, topic=topics[i])
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target = target_template.format(day=day, i=i)
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mid = mem.store(emb(model, cue), emb(model, target),
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metadata={"day": day, "task": i},
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timestamp=float(day))
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day_memories[day].append(mid)
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# Check capacity
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if len(mem.memories) > max_capacity:
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forget_fn(mem, max_capacity)
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# Test recall for each day's memories
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day_recall = {}
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for day in range(1, days + 1):
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correct = 0
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total = 0
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for i in range(per_day):
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mid = day_memories[day][i] if i < len(day_memories[day]) else None
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if mid is None or mid not in mem.memories:
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continue
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cue = cue_template.format(day=day, i=i, topic=topics[i])
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results = mem.recall(emb(model, cue), top_k=1)
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if results and results[0].memory_id == mid:
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correct += 1
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total += 1
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day_recall[day] = (correct, total)
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# Print results
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surviving = len(mem.memories)
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print(f" {strategy_name}: {surviving} memories surviving")
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for day in range(1, days + 1):
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c, t = day_recall[day]
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pct = f"{c}/{t}" if t > 0 else "0/0"
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print(f" Day {day}: {pct}")
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# Strategy 1: FIFO (oldest first)
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def forget_fifo(mem, cap):
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entries = sorted(mem.memories.values(), key=lambda e: e.timestamp)
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to_remove = len(mem.memories) - cap
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for e in entries[:to_remove]:
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mem.forget(e.memory_id)
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# Strategy 2: LRU (least recently accessed)
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def forget_lru(mem, cap):
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entries = sorted(mem.memories.values(), key=lambda e: e.access_count)
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to_remove = len(mem.memories) - cap
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for e in entries[:to_remove]:
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mem.forget(e.memory_id)
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# Strategy 3: Low importance first (by timestamp recency as proxy)
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def forget_low_importance(mem, cap):
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entries = sorted(mem.memories.values(),
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key=lambda e: e.timestamp + e.access_count * 0.5)
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to_remove = len(mem.memories) - cap
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for e in entries[:to_remove]:
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mem.forget(e.memory_id)
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print("(max_capacity=30, 7 days × 10 memories = 70 total)")
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run_strategy("FIFO (oldest first)", forget_fifo)
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print()
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run_strategy("LRU (least accessed)", forget_lru)
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print()
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run_strategy("Importance (recency+access)", forget_low_importance)
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def main():
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print("=" * 60)
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print("Experiment P4: Memory Lifecycle")
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print("=" * 60)
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model = load_model()
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test_deduplication(model)
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test_importance_scoring(model)
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test_forgetting_strategies(model)
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if __name__ == "__main__":
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main()
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