ceflobhash 0.1.3

Root-Boolean Dual Node Inteligic — 可验证双节点共识判定 / verifiable dual-node consensus decision structure

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.

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🇨🇳 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。

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🇬🇧 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.

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Status / 状态

v0.1.1 — experimental but functional. MIT License.

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