README.md

metadata

title: LatticeMemory
emoji: 🔷
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.0.1
app_file: app.py
pinned: true
short_description: 32x compressed semantic index with E8 addresses

LatticeMemory

32x smaller index. Same retrieval quality on cache hits. Every concept has a permanent address.

→ GitHub | → 2-minute pitch deck

What This Is

LatticeMemory uses the E8 lattice — the densest mathematical sphere packing in 8 dimensions — as a universal address space for meaning. Every text embedding snaps to its nearest E8 coordinate: a 128-byte address that is 32x smaller than float32 and enables O(1) retrieval on exact or near-duplicate cache hits.

Benchmarks

Compression (bge-large 1024-dim, 1M docs):

Method	Compression	Index size	Retrieval p50 @ 100K docs
Float32	1x	4.1 GB	20.8 ms
E8 keys (LatticeMemory)	32x	0.13 GB	O(1) on key hit

Fallback quality (1K docs, 100 paraphrase queries, recall vs float32):

Fallback	Compression vs float32	Recall@10 overlap	Search p50
Float32	1x	100.0%	0.14 ms
Int8	4x	95.1%	1.97 ms
Int4	8x	12.1% (retrieval-unsafe)	4.21 ms

• E8 keys are a fast-path cache layer, not a drop-in replacement for ANN search — they catch exact/near-identical text instantly; novel queries fall through to the dense fallback (Int8 recommended for RAG/QA).
• STS quality: 0.8714 (bge-large RF-Snap) vs 0.8637 float32 baseline (+0.0077)

Model

This Space uses dfrokido/bge-large-e8-snap — bge-large-en-v1.5 fine-tuned with RF-Snap training to align embeddings to the E8 lattice while improving STS quality above the float baseline.

Try It

Type any text to see its E8 address (permanent semantic coordinate) and retrieve from a 500-doc MS-MARCO corpus. Compare float32 vs RF-Snap latency live.

E8 Shape Memory (experimental, separate project)

A parallel experiment applying the same E8 lattice addressing to 3D point cloud data instead of text. Point cloud patches are split into 8-coordinate blocks and snapped to their nearest E8 point, giving each geometric region a permanent lattice address. This is packaged as a standalone desktop prototype (CLIP + OpenShape PointBERT encoders, Rust backend, Blender integration) — it is not part of the latticememory Python package or this Space's demo.

Links

• GitHub (latticememory)
• 2-min pitch deck
• Model: dfrokido/bge-large-e8-snap
• Contact for design partnerships: dfrokido@gmail.com