license	tags	library_name	pipeline_tag
mit	spatial-database memory embeddings ai vector-search rust	arms-core	feature-extraction

ARMS - Attention Reasoning Memory Store

Position IS Relationship - A Spatial Memory Fabric for AI Systems

ARMS is a spatial memory fabric that enables AI systems to store and retrieve computed states by their native dimensional coordinates. Unlike traditional databases that require explicit relationships through foreign keys or learned topology through approximate nearest neighbor algorithms, ARMS operates on a fundamental principle: proximity defines connection.

The Problem

Current AI memory approaches all lose information:

• Extended context: Expensive, doesn't scale beyond training length
• RAG retrieval: Retrieves text, requires recomputation of attention
• Vector databases: Treat all data as unstructured point clouds
• External memory: Key-value stores with explicit indexing

The ARMS Insight

Traditional:  State → Project → Index → Retrieve → Reconstruct
              (lossy at each step)

ARMS:         State → Store AT coordinates → Retrieve → Inject directly
              (native representation preserved)

The Five Primitives

Everything in ARMS reduces to five operations:

Primitive	Type	Purpose
Point	Vec<f32>	Any dimensionality
Proximity	fn(a, b) -> f32	How related?
Merge	fn(points) -> point	Compose together
Place	fn(point, data) -> id	Exist in space
Near	fn(point, k) -> ids	What's related?

Quick Start

use arms_core::{Arms, ArmsConfig, Point};

// Create ARMS with default config
let mut arms = Arms::new(ArmsConfig::new(768));

// Place a point in the space
let point = Point::new(vec![0.1; 768]);
let id = arms.place(point, b"my data".to_vec()).unwrap();

// Find nearby points
let query = Point::new(vec![0.1; 768]);
let neighbors = arms.near(&query, 5).unwrap();

Hexagonal Architecture

ARMS follows a hexagonal (ports-and-adapters) architecture. The core domain contains pure math with no I/O. Ports define trait contracts. Adapters provide swappable implementations.

┌─────────────────────────────────────────────────────────────┐
│                         ARMS                                │
├─────────────────────────────────────────────────────────────┤
│  CORE (pure math, no I/O)                                   │
│    Point, Id, Blob, Proximity, Merge                        │
│                                                             │
│  PORTS (trait contracts)                                    │
│    Place, Near, Latency                                     │
│                                                             │
│  ADAPTERS (swappable implementations)                       │
│    Storage: Memory, NVMe (planned)                          │
│    Index: Flat, HAT (see arms-hat crate)                    │
│                                                             │
│  ENGINE (orchestration)                                     │
│    Arms - the main entry point                              │
└─────────────────────────────────────────────────────────────┘

The Hippocampus Analogy

ARMS functions as an artificial hippocampus for AI systems:

Hippocampus	ARMS
Encodes episodic memories	Stores attention states
Spatial navigation	High-dimensional proximity
Pattern completion	Near queries
Memory consolidation	Merge operations
Place cells	Points at coordinates

Ecosystem

Related Crates

• arms-hat - Hierarchical Attention Tree index adapter (100% recall, 70x faster than HNSW)

Planned Adapters

• arms-nvme - Persistent storage via memory-mapped files
• arms-distributed - Sharded storage across machines
• arms-gpu - CUDA-accelerated similarity search
• arms-py - Python bindings

Proximity Functions

Built-in proximity measures:

• Cosine - Angle between vectors (semantic similarity)
• Euclidean - Straight-line distance
• DotProduct - Raw dot product
• Manhattan - L1 distance

Installation

[dependencies]
arms-core = "0.1"

Paper

The research paper is available in the paper/ directory.

ARMS: A Spatial Memory Fabric for AI Systems Andrew Young, 2026

License

MIT License - see LICENSE

Citation

If you use ARMS in research, please cite:

@article{young2026arms,
  author = {Young, Andrew},
  title = {ARMS: A Spatial Memory Fabric for AI Systems},
  journal = {arXiv preprint},
  year = {2026},
  url = {https://github.com/automate-capture/arms}
}

Author

Andrew Young - andrew@automate-capture.com