cubesquare 1.0.3

Instant Semantic Memory - 83M ops/sec, 0.04ms queries, 100% deterministic

Cubesquare

Instant Semantic Memory — 83M ops/sec, 0.04ms queries, 100% deterministic.

Embeddings drift. Vectors collapse. Cubesquare doesn't.

Install

pip install cubesquare

Zero dependencies. Works on Python 3.10+.

Quick Start

from cubesquare import CSTMemory

memory = CSTMemory()

# Write content — returns a permanent coordinate
token = memory.write("/src/auth/login.py")
print(f"Basin: {token.basin}, Layer: L{token.layer}")

# Query by meaning — O(1) computation, not O(N) search
results = memory.query("authentication", limit=5)

# Navigate by layer
siblings = memory.L1.siblings(token)    # L1: Geometric — file hierarchy
similar  = memory.L2.similar(token)     # L2: Semantic — meaning clusters
nearby   = memory.L3.nearby(token)      # L3: Spatial — coordinate proximity

Why Not Embeddings?

Problem	Embeddings + Vector DB	Cubesquare
Identity at scale	Vectors crowd together — distinct concepts become indistinguishable	Every input has a unique, permanent coordinate
Model dependency	Change embedding model → all vectors shift	No model. The hash IS the identity
Determinism	Same text → different vectors across versions	Same text → same coordinate, forever
Query cost	O(N) approximate search, 10-50ms+	O(1) computation, 0.04ms
Dependencies	Embedding model + vector DB + GPU	Zero. Python stdlib only

How It Works

Content → Hash (FNV-1a) → 3D Coordinate (Morton) → Basin (mod 131) → Layer (L1-L9)

The coordinate IS the meaning. No lookup tables, no search, no approximation.

Concept	What it does	Nearest analog
Token	Content → 32-bit hex ID	Content hash
Morton code	3D coords → single integer	Geohash (but 3D)
Basin (0-130)	Cluster into 131 groups	Consistent hash ring
Layer (L1-L9)	Semantic type	Index namespace
I-Logic	5-state routing algebra	Finite state machine
SRL	t = √d iteration collapse	Skip list shortcut

9 Layers

Layer	Name	Basins	Question
L1	Geometric	0-14	What structure?
L2	Semantic	15-29	What meaning?
L3	Spatial	30-43	Where?
L4	Functional	44-58	What operation?
L5	Probabilistic	59-72	How certain?
L6	Emotional	73-87	What state?
L7	Frequency	88-101	What timing?
L8	Electromagnetic	102-116	What force?
L9	Membrane	117-130	What boundary?

I-Logic Algebra

5-state algebra for predicting routing outcomes before execution:

State	Value	Meaning
DIRECT	1	Pass through
INVERSE	-1	Reflect
SPIRAL	+i	Expand
ANTI_SPIRAL	-i	Contract
ABSORBED	0	Terminate

from cubesquare.i_logic import multiply, ILogicState

result = multiply(ILogicState.SPIRAL, ILogicState.SPIRAL)
# Returns INVERSE (i × i = -1)

Performance

Tier	Throughput	Use case
T1 C Internal	83M ops/sec	Base throughput
T2 Batch (1000)	57M ops/sec	Production
T3 Single call	0.3M ops/sec	Avoid — 190× slower

# Batch through C — 57M ops/sec
from cubesquare._core import morton_encode_batch_raw, create_coords_array
coords = create_coords_array([100,200,0, 101,201,0, 102,202,0])
results = morton_encode_batch_raw(coords, 3)

CLI

cubesquare --status                        # System status
cubesquare ilogic multiply SPIRAL INVERSE  # I-Logic algebra
cubesquare primitives status               # O(1) primitive checks
cubesquare --list                          # All 35 shells

Verified

• 68/68 pytest tests passing
• 43/43 agent validation checks
• 400/400 Morton roundtrips
• 131/131 basin coverage
• 31 native DLLs loaded

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For Contributors

Clone the repo and install in development mode:

git clone https://github.com/Cubesquare-Token/CST.git
cd CST
pip install -e .

Run tests:

pytest tests/ -v                              # 68 tests
python agent_sandbox/agent_in_loop_validation.py  # 43 checks

Project Structure

cubesquare/
├── _core/          # C backend bindings, Morton encoding
├── i_logic/        # 5-state algebra
├── layers/         # L1-L9 semantic layers
├── shells/         # 35 command shells
├── circular/       # Loop detection, SRL
├── primitives/     # O(1) verified operations
├── data/beehive/   # Binary CST index (601KB)
└── native/hex_core/ # 31 DLLs

Documentation

Document	Description
CLAUDE.md	Full technical reference
docs/LAYERS_MAP.md	12-layer architecture
docs/VERIFIED_PRIMITIVES.md	O(1) proofs

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MIT License

Created by Jesus Fernandez. Offline-first. Training-free. Deterministic.