Skip to main content

ceflobhash

Connect Everything Forever Low-Bit Hash 连接永恒 · 低比特哈希

Root-Boolean Dual Node Inteligic — 一种可验证的双节点共识判定结构。 A verifiable dual-node consensus decision structure.

无点积、无Softmax、无概率。只有比特和XOR。 No dot product. No softmax. No probability. Just bits and XOR.


🇨🇳 中文说明

这是什么?

ceflobhash 将高维浮点向量(例如LLM的768维嵌入)压缩为紧凑的二进制表示,并通过独立双节点共识进行判定验证。每个判定产生可审计的指纹(SHA-256)。

核心理念

概念 说明
祖布尔双节点 节点A(字符匹配) + 节点B(语义类型匹配),独立判定
可审计指纹 每个决策输出 SHA-256 哈希,可事后重新验证
零概率 所有判定基于确定性门电路逻辑,非概率推理

安装

pip install ceflobhash

核心API

from root_boolean import (
    binarize,           # float向量 → N位二进制向量
    hamming_distance,   # 二进制向量 → 汉明距离
    DualNode,           # 双节点共识 + 审计
)
函数 功能
binarize(vec, bits=256) 浮点向量 → 二进制向量(比特宽度可配)
hamming_distance(a, b) XOR + 位计数
DualNode(anchor_a, anchor_b) 两个独立判定节点

双节点判定示例

from root_boolean import DualNode

node = DualNode(
    anchor_a=([1,0,1,0], (1,0,0,0), 3.0),
    anchor_b=([0,1,0,1], (0,1,0,0), 3.0),
)
v = node.evaluate(([1,0,1,0], (1,0,0,0)))
# → "TRUE" | "FALSE" | "UNKNOWN"
h = node.audit(([1,0,1,0], (1,0,0,0)), v)
# → SHA-256 审计指纹

性能对比

指标 float32 (768维) binary (256位)
内存 3 KB/向量 32 bytes (−99%)
距离计算 点积 (768次乘加) XOR + 位计数 (~10-50×更快)
可验证 ❌ ✅ 双节点审计

不是替代注意力机制。而是为检索、缓存、决策验证提供补充。


🇬🇧 English

What is this?

ceflobhash reduces high-dimensional float vectors (e.g. 768d LLM embeddings) to compact binary representations, and verifies decisions via independent dual-node consensus. Every decision produces an auditable SHA-256 fingerprint.

Core Concepts

Concept Description
Dual Node (Zubu'er) Node A (character match) + Node B (semantic type match), independently judge
Auditable Fingerprint Each decision outputs SHA-256, re-verifiable later
Zero Probability All decisions based on deterministic gate logic, not probabilistic

Install

pip install ceflobhash

Core API

from root_boolean import (
    binarize,           # float vector → N-bit binary vector
    hamming_distance,   # binary vector → distance
    DualNode,           # dual-node consensus + audit
)
Function What
binarize(vec, bits=256) Float → binary vector (bit-width configurable)
hamming_distance(a, b) XOR + popcount
DualNode(anchor_a, anchor_b) Dual independent decision nodes

DualNode Example

from root_boolean import DualNode

node = DualNode(
    anchor_a=([1,0,1,0], (1,0,0,0), 3.0),
    anchor_b=([0,1,0,1], (0,1,0,0), 3.0),
)
v = node.evaluate(([1,0,1,0], (1,0,0,0)))
# → "TRUE" | "FALSE" | "UNKNOWN"
h = node.audit(([1,0,1,0], (1,0,0,0)), v)
# → SHA-256 — can be logged, compared, re-verified

Why Binary for LLM?

Metric float32 (768d) binary (256b)
Memory 3 KB per vector 32 bytes (−99%)
Distance dot product (768 mul+add) XOR + popcount (~10-50× faster)
Verifiable No Yes (DualNode audit)

Not a replacement for attention. A complement for lookups, caching, and decision verification.


Status / 状态

v0.1.1 — experimental but functional. MIT License.


CEF Powered — Connect Everything Forever

Metadata

Release files for ceflobhash 0.1.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ceflobhash 0.1.3
File Size Uploaded
ceflobhash-0.1.3.tar.gz 9.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for ceflobhash 0.1.3
File Interpreter ABI Platform
ceflobhash-0.1.3-py3-none-any.whl Python 3 none any Details

Total release size: 19.1 kB

Release files / ceflobhash-0.1.3.tar.gz

Download URL ceflobhash-0.1.3.tar.gz
Size 9.4 kB
Tags Source
SHA-256 checksum
How to use checksums
c1f141bed47d876cbe32e11d1d2263ccf56f55015d31022820e4a8c5fa4c6203
BLAKE2b-256 checksum
How to use checksums
6be4bf7a38267f61cfde056cbb389293feed4ab1cf80a56a8d703ad087d5153d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.13.5

Release files / ceflobhash-0.1.3-py3-none-any.whl

Download URL ceflobhash-0.1.3-py3-none-any.whl
Size 9.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b30d05895eb068a86a63a23d2d6706dd499b1c2e9e51b00aa03ebc2e1e7b4ad4
BLAKE2b-256 checksum
How to use checksums
2c0460e7c09053610619927787ae889844c91cbf3da0662fc6d22fd9dcff10b7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.13.5

Release history Release notifications | RSS feed

This release

0.1.3 This release

2 release files

0.1.2

2 release files

0.1.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page