Memory Forest - The Classics Revival

Associative Memory with Learned Routing Through Decision Trees

Experimental Research Code - Functional but unoptimized, expect rough edges

What Is This?

Memory Forest combines decision tree routing with associative hash buckets to create a scalable memory system. Instead of searching all memory linearly, learned decision trees route queries to the most relevant memory buckets.

Core Innovation: Trees learn to route based on retrieval success, creating adaptive memory organization that gets better over time.

Architecture Highlights

Learned Routing: Decision trees adapt based on retrieval performance
Associative Storage: Hash buckets with similarity-based clustering
Ensemble Retrieval: Multiple trees vote on best memories
Eviction Policies: Automatic memory management
Scalable Design: O(log n) routing instead of O(n) search

Quick Start

from memory_forest import MemoryForest

# Create memory system
forest = MemoryForest(
    input_dim=64,
    num_trees=3,
    max_depth=4,
    bucket_size=32
)

# Store some memories
features = torch.randn(100, 64)
forest.store(features)

# Retrieve similar items
query = torch.randn(5, 64)
results = forest.retrieve(query, top_k=3)

Current Status

Working: Hierarchical storage, associative clustering, tree routing, ensemble retrieval
Rough Edges: Adaptive learning can be overly aggressive, bucket utilization optimization needed
Still Missing: Learning rate scheduling, advanced eviction policies, distributed routing
Performance: Excellent retrieval quality (0.95+ similarity), needs learning component tuning
Memory Usage: Not optimized, expect high RAM usage
Speed: Baseline implementation, significant optimization possible

Mathematical Foundation

The decision trees learn routing functions f: R^d -> {0,1,...,B-1} where B is the number of buckets. Trees update based on retrieval success using simple gradient-free optimization:

success_rate = retrieved_similarity / max_possible_similarity
tree_update ∝ success_rate * path_taken

Associative buckets use learnable hash functions with Hebbian-style updates for similarity clustering.

Research Applications

Large-scale retrieval systems
Adaptive memory architectures
Decision tree optimization
Associative memory research
Hierarchical clustering

Installation

pip install torch numpy
# Download memory_forest.py from this repo

The Classics Revival Collection

Memory Forest is part of a larger exploration of foundational algorithms enhanced with modern neural techniques:

Evolutionary Turing Machine
Hebbian Bloom Filter
Hopfield Decision Graph
Liquid Bayes Chain
Liquid State Space Model
** Memory Forest** ← You are here

Citation

@misc{memoryforest2025,
  title={Memory Forest: Associative Memory with Learned Routing},
  author={Jae Parker 𓅸 1990two},
  year={2025},
  note={Part of The Classics Revival Collection}
}

1990two
/

memory_forest