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--- |
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license: mit |
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tags: |
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- spatial-database |
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- memory |
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- embeddings |
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- ai |
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- vector-search |
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- rust |
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library_name: arms-core |
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pipeline_tag: feature-extraction |
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--- |
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# ARMS - Attention Reasoning Memory Store |
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> **Position IS Relationship** - A Spatial Memory Fabric for AI Systems |
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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**. |
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## The Problem |
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Current AI memory approaches all lose information: |
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- **Extended context**: Expensive, doesn't scale beyond training length |
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- **RAG retrieval**: Retrieves text, requires recomputation of attention |
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- **Vector databases**: Treat all data as unstructured point clouds |
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- **External memory**: Key-value stores with explicit indexing |
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## The ARMS Insight |
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``` |
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Traditional: State → Project → Index → Retrieve → Reconstruct |
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(lossy at each step) |
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ARMS: State → Store AT coordinates → Retrieve → Inject directly |
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(native representation preserved) |
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``` |
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## The Five Primitives |
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Everything in ARMS reduces to five operations: |
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| Primitive | Type | Purpose | |
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|-----------|------|---------| |
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| **Point** | `Vec<f32>` | Any dimensionality | |
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| **Proximity** | `fn(a, b) -> f32` | How related? | |
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| **Merge** | `fn(points) -> point` | Compose together | |
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| **Place** | `fn(point, data) -> id` | Exist in space | |
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| **Near** | `fn(point, k) -> ids` | What's related? | |
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## Quick Start |
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```rust |
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use arms_core::{Arms, ArmsConfig, Point}; |
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// Create ARMS with default config |
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let mut arms = Arms::new(ArmsConfig::new(768)); |
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// Place a point in the space |
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let point = Point::new(vec![0.1; 768]); |
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let id = arms.place(point, b"my data".to_vec()).unwrap(); |
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// Find nearby points |
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let query = Point::new(vec![0.1; 768]); |
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let neighbors = arms.near(&query, 5).unwrap(); |
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``` |
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## Hexagonal Architecture |
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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. |
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``` |
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┌─────────────────────────────────────────────────────────────┐ |
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│ ARMS │ |
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├─────────────────────────────────────────────────────────────┤ |
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│ CORE (pure math, no I/O) │ |
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│ Point, Id, Blob, Proximity, Merge │ |
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│ │ |
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│ PORTS (trait contracts) │ |
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│ Place, Near, Latency │ |
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│ │ |
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│ ADAPTERS (swappable implementations) │ |
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│ Storage: Memory, NVMe (planned) │ |
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│ Index: Flat, HAT (see arms-hat crate) │ |
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│ │ |
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│ ENGINE (orchestration) │ |
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│ Arms - the main entry point │ |
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└─────────────────────────────────────────────────────────────┘ |
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``` |
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## The Hippocampus Analogy |
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ARMS functions as an artificial hippocampus for AI systems: |
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| Hippocampus | ARMS | |
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|-------------|------| |
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| Encodes episodic memories | Stores attention states | |
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| Spatial navigation | High-dimensional proximity | |
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| Pattern completion | Near queries | |
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| Memory consolidation | Merge operations | |
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| Place cells | Points at coordinates | |
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## Ecosystem |
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### Related Crates |
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- [`arms-hat`](https://crates.io/crates/arms-hat) - Hierarchical Attention Tree index adapter (100% recall, 70x faster than HNSW) |
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### Planned Adapters |
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- `arms-nvme` - Persistent storage via memory-mapped files |
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- `arms-distributed` - Sharded storage across machines |
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- `arms-gpu` - CUDA-accelerated similarity search |
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- `arms-py` - Python bindings |
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## Proximity Functions |
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Built-in proximity measures: |
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- **Cosine** - Angle between vectors (semantic similarity) |
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- **Euclidean** - Straight-line distance |
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- **DotProduct** - Raw dot product |
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- **Manhattan** - L1 distance |
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## Installation |
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```toml |
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[dependencies] |
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arms-core = "0.1" |
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``` |
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## Paper |
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The research paper is available in the [`paper/`](paper/) directory. |
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**ARMS: A Spatial Memory Fabric for AI Systems** |
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Andrew Young, 2026 |
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## License |
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MIT License - see [LICENSE](LICENSE) |
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## Citation |
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If you use ARMS in research, please cite: |
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```bibtex |
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@article{young2026arms, |
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author = {Young, Andrew}, |
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title = {ARMS: A Spatial Memory Fabric for AI Systems}, |
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journal = {arXiv preprint}, |
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year = {2026}, |
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url = {https://github.com/automate-capture/arms} |
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} |
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``` |
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## Author |
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Andrew Young - [andrew@automate-capture.com](mailto:andrew@automate-capture.com) |
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