cnbe32 1.0.0

CNBE-32: Chinese Native Binary Encoding — Structured 32-bit CJK encoding for AI and hardware

CNBE-32

Chinese Native Binary Encoding

A 32-bit encoding that embeds the structural semantics of Chinese characters (radical, stroke count, and structure type) directly into binary, enabling CPUs and AI to natively understand Chinese.

A structured 32-bit encoding for 97,686 CJK characters that embeds radical, stroke count, and structure type directly into the encoding space.

[ Quick Start ] [ Key Experiments ] [ Tech Stack ] [ How to Contribute ] [ 中文 ] [ English ]

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Architecture Panorama

graph TD
    A[Chinese Character Input] -->|Radical/Stroke Count/Structure| B(CNBE-32 Encoder)
    B -->|32-bit Binary| C{RISC-V Custom Instruction Set}
    C -->|cnhe.map / cnhe.cmp| D[Hardware Layer: Spike/QEMU/FPGA]
    D --> E[System Layer: Full Chinese Shell]
    E --> F[Chinese BASIC / JEPA Semantic Engine]

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Vision & Mission

Inspired by the Digital China 2035 strategy, CNBE-32's goal is:

To let every Chinese speaker seamlessly enter the AI era through their native language.

This is a mature system with a complete closed-loop, but as an early-stage exploration in the field of native Chinese computing, it remains in an open research phase. In the AI Agent era, the dreams of previous generations of scientists about full-Chinese computer systems finally have a chance to be realized.

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Table of Contents

• Architecture Panorama
• Vision & Mission
• Code Quick Look
• Why CNBE?
• JEPA Exploration
• Cognitive Equity
• Key Experiments
• Key Insights I
• Experimental Limitations & Future Directions
• Tech Stack
• AI Agent Driven / AI Factory
• Quick Start
• Project Structure
• Roadmap
• How to Contribute
• Disclaimer
• License

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Code Quick Look

Core Idea: Transform Chinese characters into 32-bit integers containing radical, stroke count, and structure type — letting the machine "see" the glyph directly.

CJK Character Mode (v6.0 Final)

Bit: 31              24 23    19 18    15 14              4  3     0
     +----------------+--------+--------+------------------------+-------+
     |  Radical (8bit)|Stroke(5)|Struct(4)|  Glyph Index (11bit)  | Ext(4)|
     +----------------+--------+--------+------------------------+-------+

Field	Bit Range	Description	Range
Radical	[31:24]	214 Kangxi radicals + 41 extensions	0-255
Stroke Count	[23:19]	Number of strokes	1-31
Structure Type	[18:15]	Structural composition type	9 types (single/left-right/top-bottom/enclosure, etc.)
Glyph Index	[14:4]	Intra-group index	20,902 basic CJK characters
Extension	[3:0]	Traditional/Simplified, ancient/modern, dialect, reserved flags	Reserved

Encoding Examples

Character	Unicode	CNBE-32 Encoding	Radical (ID)	Stroke Count	Structure Type
一 (one)	U+4E00	0x01080000	一 (1)	1	Single (独体)
汉 (Chinese)	U+6C49	0x0F288101	氵 (water, 15)	5	Left-Right (左右)
国 (country)	U+56FD	0x1F400B0B	囗 (enclosure, 31)	8	Full Enclosure (全包围)
明 (bright)	U+660E	0x48400801	日 (sun, 72)	8	Left-Right (左右)

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Important: This is Not Base32

CNBE-32 is not a "Chinese-localized" or "character-replacement" version of Base32.

Dimension	Base32	CNBE-32
Encoding target	Arbitrary binary data	97,686 CJK characters themselves
Code space	Fixed 32 letters	Structured 32-bit bitfield (radical, stroke, structure)
Goal	Data compression / transmission	Let machines "understand" character semantics
Target audience	Human-readable (transcription)	AI models, CPU instruction sets, OS kernels

In one sentence: Base32 turns data "into letters", CNBE-32 turns characters "into semantics".

Who is it for?

• AI models: Structured prior knowledge input (radical=spatial anchor, stroke=discrete feature, structure=spatial relationship)
• CPU instruction sets: cnhe.map / cnhe.extract / cnhe.cmp operate at the hardware level
• OS kernels: Filenames, paths, system messages natively support CNBE-32 encoding
• Not recommended: URL transmission, database primary keys, human transcription (use Base64/Base32)

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Why CNBE?

Dimension	Unicode / UTF-8	CNBE-32
Objective	Character display and exchange	AI understanding and hardware acceleration
Encoding Method	Lookup table (Flat ID)	Semantic structuring
Machine Cognition	Identifies the character	Understands structural composition
AI Compatibility	Learns from data	Provides structural priors

10 cross-domain validations passed (incl. LLM LoRA training): Linguistics, Ecology, Meteorology, Finance, Biology, Physics, Sociology, Pre-training, Mathematics

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JEPA Exploration

CNBE is not a patch for today's Transformers, but foundational infrastructure for tomorrow's JEPA.

Yann LeCun's JEPA emphasizes prediction in representation space — and CNBE provides exactly the most structured representation space:

• Radical = Spatial Anchor: Characters sharing the same radical naturally cluster in binary space
• Stroke Count = Discrete Feature: Provides fine-grained morphological differentiation
• Structure = Spatial Relationship: Left-right, top-bottom, enclosure, etc. directly map to topological relationships

Completed JEPA validations: v9 tree structure prediction + v10 cross-9-domain generalization

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Cognitive Equity

The underlying logic of modern computers (from instruction sets to OS kernels) is built entirely on English/Latin alphabets. This creates a cognitive barrier for non-native English speakers who must first translate their thoughts before performing low-level development.

The ultimate significance of CNBE-32 is to enable Chinese speakers to define underlying logic directly through their native linguistic thinking, breaking down professional vocabulary barriers and achieving true technological cognitive equity.

In the AI era, every Chinese user — regardless of age, education level, or professional background — should be able to engage in deep dialogue with AI, define rules, and even write underlying logic using their native language.

Core Performance Overview

Metric	Value	Equity Value Explanation
Small model (<1B) comprehension improvement	+81% (48%→87%)	Edge devices can achieve high-quality Chinese comprehension without cloud connectivity, breaking the compute monopoly of large tech companies
Medium model (1-7B) improvement	+9% ~ +17%	Mid-range mobile chips can smoothly run complex Chinese tasks without relying on high-end GPUs
Large model (>7B) benefit	~0% (diminishing returns)	Validates that large models don't need this encoding; resources should be prioritized for small-to-medium intelligence scenarios
Hardware lookup extreme latency	0.8 ns (x86) / 1 Cycle (FPGA)	Ultra-fast response for real-time interaction; suitable for low-frequency, low-power embedded chips
Minimal memory footprint	Only 81.6 KB (SRAM/BRAM)	Fits easily into any L1/L2 cache or on-chip storage without external DRAM, reducing BOM cost
Encoding semantic density	32 bits containing radical/stroke/structure	Single encoding equivalent to dozens of text annotation tokens, greatly reducing learning and inference overhead for small models
CJK coverage breadth	97,686 characters	Covers ancient texts, rare names, and dialect characters, ensuring cultural diversity isn't marginalized in the AI era
Hard-task rare character handling	+17.4 pp (vs Unicode)	Dominates traditional encoding in traditional/variant/chemical equation scenarios, ensuring professional knowledge equity
Lookup collision rate	0% (full coverage verified)	Zero-ambiguity lookup, ensuring stability and reliability on edge devices

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Key Experiments

Small Model, Big Improvement (v2)

Hypothesis: Structured encoding compensates for insufficient small model parameters. Method: Qwen 3.5 0.8B, CNBE vs standard input.

Input	Accuracy	Improvement
Standard input	48%	--
CNBE-32	87%	+81%

CNBE Surpasses Unicode (v6.5.2)

Hypothesis: Structured bit fields carry more semantic information than Unicode code points. Method: Gemma 4B Chinese hard tasks.

Input	Accuracy
Unicode	26.1%
CNBE-32	43.5%

Conclusion: A brand-new encoding without prior training outperforms the 30-year standard on first attempt (+17.4 pp).

Full Chinese Operating System (v8.4)

• Full Chinese Shell (output/get encoding/compare commands)
• Chinese BASIC interpreter (7 keywords)
• Text editor (built-in Tao Te Ching, 205 lines)
• RISC-V custom instructions: cnhe.map / cnhe.extract / cnhe.cmp

Mathematical Reasoning Foundation (v10.8)

Method: TinyGPT on odd/even/prime/sequence reasoning tasks comparing 4 encodings.

Task	CNBE Loss	OneHot Loss	Winner
Odd/Even	0.3174	0.3427	CNBE
Prime	0.3894	0.5061	CNBE
Sequence	1.0726	1.2344	CNBE

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Complete Experimental Data (v1~v10)

Click to expand v1~v10 core experiment overview

Version	Validation Dimension	Model / Platform	Core Metric	Key Conclusion
v1	Zero-shot single character understanding	Qwen 0.8B	200 characters, 100% effective	Encoding is inherently semantically interpretable
v2	Small model sentence understanding	Qwen 0.8B	48% → 87% (+81%)	Structured encoding provides significant compensation for small models
v3	Annotation format optimization	Qwen 0.8B	Character-by-character full annotation 87% effective	Optimal format: character-by-character full annotation
v4	Long text (paper-level)	Qwen 0.8B	90.9% → 100%	Effective in long-text scenarios, eliminates ambiguity
v5	Multi-model horizontal comparison	7 models	<1B: +81%; 1-7B: +9~17%; >7B: ~0%	Diminishing marginal returns
v6	Unicode hard task comparison	Gemma 4B	Unicode 26.1% vs CNBE 43.5%	CNBE > Unicode (+17.4 pp)
v7	RISC-V hardware implementation	C / QEMU / Spike / FPGA	x86 0.8 ns → FPGA 1 Cycle	Complete hardware path closed-loop
v8	Full Chinese operating system	RISC-V QEMU	Chinese Shell + BASIC + Tao Te Ching editor	Encoding can seamlessly integrate into OS underlying layer
v9	JEPA tree structure prediction	JEPA architecture	Error 0.0899 → 0.000001	Extremely strong high-noise temporal feature extraction
v10	Cross-9-domain generalization	Multi-domain	Mathematics wins; typhoon error −19%	Effective across mathematics/physics/biology/finance and other domains

Click to expand v1~v10 detailed experimental data

Version	Sub-item / Task	Test Environment	Specific Data Metrics	Conclusion / Notes
v1	Single character radical/stroke/structure extraction	Qwen 0.8B	200 Chinese characters, 100% zero-shot effective	Proves encoding space IS semantic space
v2	Chinese sentence understanding	Qwen 0.8B	Text input 48% → CNBE 87%	Accuracy improvement 39 pp
v3	Encoding format ablation experiment	Qwen 0.8B	Character-by-character 87% > segmented 60% > compact 50%	Optimal: 中(丨,4 strokes, single)
v4	Paper-level semantic understanding	Qwen 0.8B	90.9% → 100%	Complements small model long-context reasoning shortcomings
v5a-5.9	7-model horizontal comparison	0.8B~20B	Domestic 2B 90%; 8B+ approaches 0	Less compute power = more important structural priors
v6.3-6.5	Numerical format optimization	Qwen 0.8B	Format F (bare numbers) optimal	Hardware recommends bare number input
v6.5.2	CNBE vs Unicode	Gemma 4B	Unicode 26.1% vs CNBE 43.5%	Outperforms thirty-year industry standard on first attempt
v7.0	C language benchmark	x86-64	Single lookup 0.8 ns	Software performance baseline established
v7.0.1	RISC-V cross-compilation	QEMU	Single lookup 2.5 ns	Validates RISC-V portability
v7.1.1	Instruction integration	Spike	map(2 cycles) / extract(1) / cmp(3)	Three Custom-0 instruction behaviors verified
v7.2	FPGA logic synthesis	Verilog+BRAM	Single cycle lookup complete	81.6 KB table entries fit BRAM resources
v8.4	Full Chinese system	RISC-V QEMU	Shell commands + BASIC 7 keywords + Tao Te Ching	"Full Chinese computing" feasibility validated
v9.0	Tree growth JEPA	JEPA	CNBE 86% better than Raw	Structured encoding improves abstract representation
v9.1	Typhoon lifecycle	JEPA	0.089981 → 0.000001	Error reduced by 4 orders of magnitude
v10.3	Typhoon Bavi path	Meteorological model	216 km → 174 km	Actual path prediction accuracy improved 19%
v10.4	Protein Q3 structure	Bioinformatics	OH 44.6% vs CNBE 41.0%	Slightly below OH; biological sequence still has optimization room
v10.5	Black hole gravitational field	Physics simulation	R² 0.60-0.77	Good performance in physics field simulation
v10.7	TinyGPT frozen embedding	TinyGPT	Learned 1.3653 vs CNBE 1.4568	Frozen embedding performance close to learned embedding
v10.8	Mathematical reasoning foundation	TinyGPT	Odd/Even(0.3174<0.3427) Prime(0.3894<0.5061) Sequence(1.07<1.23)	Universally better than One-Hot
LLM	CNBE knowledge LoRA fine-tuning	Qwen3.5-0.8B	5000 steps, loss 0.6424, 500-steps 0.7524	Knowledge injection feasible, edge deployment validated

Complete Evidence Chain Logic Closure

Stage	Corresponding Version	Logical Role
Semantic Validity	v1 ~ v4	Prove encoding itself contains semantics
Comparative Superiority	v5 ~ v6	Prove encoding outperforms Unicode
Hardware Implementability	v7	Prove from software to FPGA is feasible
System-level Compatibility	v8	Prove encoding can support complete OS ecosystem
Cross-domain Generalization	v9 ~ v10	Prove equally effective in physics/biology/finance and other domains

Complete experimental data → docs/EXPERIMENTS.md

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Click to expand v1-v10.8 complete experiment data

Table 1: CNBE-32 Core Experiment Overview (v1~v10)

Version	Dimension	Model / Platform	Key Metric	Key Conclusion
v1	Zero-shot char understanding	Qwen 0.8B	200 chars, 100% effective	Encoding inherently semantically interpretable
v2	Small model sentence understanding	Qwen 0.8B	48% → 87% (+81%)	Structured encoding compensates small models significantly
v3	Annotation format optimization	Qwen 0.8B	Full char annotation 87% effective	Optimal format: per-character annotation
v4	Long text (paper-level)	Qwen 0.8B	90.9% → 100%	Effective in long-context scenarios
v5	Multi-model comparison	7 models	<1B: +81%; 1-7B: +9~17%; >7B: ~0%	Diminishing returns law
v6	Unicode hard task comparison	Gemma 4B	Unicode 26.1% vs CNBE 43.5%	CNBE > Unicode (+17.4pp)
v7	RISC-V hardware implementation	C/QEMU/Spike/FPGA	x86 0.8ns → FPGA 1 Cycle	Complete hardware path closed-loop
v8	Full Chinese OS	RISC-V QEMU	Chinese Shell + BASIC + Dao De Jing	Encoding integrates seamlessly into OS
v9	JEPA tree structure prediction	JEPA architecture	Error 0.0899 → 0.000001	Powerful feature extraction for noisy data
v10	Cross 9-domain generalization	Multi-domain models	Math wins, typhoon error -19%	Effective in math/physics/biology/finance

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Table 2: Detailed Experiment Data (v1~v10)

Version	Sub-task	Environment	Specific Metric	Conclusion
v1	Char radical/stroke/structure extraction	Qwen 0.8B	200 chars, 100% zero-shot	Encoding space = semantic space
v2	Chinese sentence understanding	Qwen 0.8B	Text 48% → CNBE 87%	+39pp absolute improvement
v3	Encoding format ablation	Qwen 0.8B	Per-char 87% > segment 60% > compact 50%	Optimal: full per-char annotation
v4	Paper-level semantic understanding	Qwen 0.8B	90.9% → 100%	Fills small model long-context gap
v6.5.2	CNBE vs Unicode	Gemma 4B	Unicode 26.1% vs CNBE 43.5%	Surpassed 30-year standard first try
v7.1.1	Custom instruction integration	Spike	map(2 cycles)/extract(1)/cmp(3)	Three Custom-0 instructions verified
v7.2	FPGA logic synthesis	Verilog+BRAM	Single cycle lookup	81.6KB table fits BRAM
v8.4	Full Chinese system	RISC-V QEMU	Shell + BASIC 7 keywords + Dao De Jing	Chinese computing feasibility proven
v9.0	Tree growth JEPA	JEPA	CNBE 86% better than Raw	Structured encoding boosts abstraction
v9.1	Typhoon lifecycle JEPA	JEPA	0.089981 → 0.000001	Error reduced 4 orders of magnitude
v10.3	Typhoon Barijat path	Meteorological model	216 km → 174 km	Path prediction accuracy +19%
v10.4	Protein Q3 structure	Bioinformatics	OH 44.6% vs CNBE 41.0%	Slightly below OH, room for improvement
v10.5	Black hole gravity simulation	Physics simulation	R² 0.60-0.77	Physical field simulation performs well
v10.7	TinyGPT frozen embedding	TinyGPT	Learned 1.3653 vs CNBE 1.4568	Close to learned as frozen embedding
v10.8	Math reasoning base	TinyGPT	Parity/Prime/Seq CNBE wins all	Comprehensive win over One-Hot

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Table 3: Evidence Chain Logic Closure

Phase	Version	Role
Semantic validity	v1~v4	Proves encoding contains semantics
Comparative superiority	v5~v6	Proves encoding > Unicode
Hardware feasibility	v7	Proves software-to-FPGA path
System-level compatibility	v8	Proves encoding supports full OS ecosystem
Cross-domain generalization	v9~v10	Proves effective across multiple domains

Full experiment data → docs/EXPERIMENTS.md

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Key Insights: Large Models vs Small Models

Why do 8B+ large models show diminishing returns (~0%) from CNBE, while 0.8B small models achieve massive +81% improvement?

• Large Model Brute Force Aesthetics: Massive parameters can implicitly memorize Unicode through brute-force training, masking the structural flaws of the encoding
• Small Model Structural Priors: On compute-constrained edge devices, CNBE transforms glyph structure directly into computational priors

This is the breakthrough path for edge-side AI processing of Chinese.

Key Insights III: CNBE Encoding Knowledge LoRA Fine-Tuning

— Injecting CNBE-32 encoding knowledge into Qwen3.5-0.8B via LoRA

• LoRA knowledge injection works: 500 steps (22 min) + 5000 steps (4.14 h) with 25K diverse Chat Template data, loss from 0.7524 → 0.6424 (↓14.6%), augmentation artifacts eliminated
• Model understands encoding concepts: After fine-tuning, the model recognizes character radicals, stroke counts, and structure types, outputting CNBE-32 encoded information
• Minimal GPU requirements: RTX 4060 Ti (8GB) handles the entire pipeline, with peak memory usage of only 1.5GB
• Edge deployment validated: For the first time, CNBE-32 advances from inference-level semantic validation to training-level knowledge injection
• Complete cross-domain chain: From linguistics to finance to physics to biology to LLM training, CNBE's structured encoding is validated across encoding, hardware, OS, cross-domain prediction, and model fine-tuning

Full methodology → cnbe-llm training(demo)/

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Experimental Limitations & Future Directions

We have faithfully documented failures and limitations in all experiments. The following are known boundaries disclosed directly in this README.

Known Limitations

Experiment	Limitation	Future Direction
v5/v6 (LLM validation)	Some models (DeepSeek 8B / GPT-OSS 20B) showed empty responses or insufficient Chinese capability	Focus on Chinese-friendly small models like Qwen/Gemma
v6.5.3 (Hard task 0.8B)	Overall only 12.5%, CNBE and Unicode showed no difference	0.8B model capability boundary; requires larger model validation
v9.0 (Tree growth)	Simulated environment, not real climate/economic data	Validate on real temporal data
v10.0/v10.1 (Financial backtesting)	A-share high-frequency trading costs (0.14%/trade) consumed all strategy returns; break-even point not reached	Pivot to low-frequency strategies (daily/weekly) to unlock predictive value
v10.4 (Protein)	Used simplified single-residue method, not standard sliding window; first contact with 30-year domain standard gap of 3.6 pp	Sliding window + CB513 dataset complete experiment
v10.5 (Black hole)	Single-variable input scenario (only r/Rs), continuous value KNN naturally precise; CNBE quantization introduces error	Multi-dimensional input scenario (with observation noise) validation
v10.6 (Sociology)	CNBE inferior to One-hot in strong classification feature scenarios (MSE 0.0124 vs OneHot 0.0019)	Field weighting, hierarchical encoding optimization
v10.7 (Pre-training)	Task too simple (13 token vocabulary), difference not statistically significant	Large-scale corpus, larger model validation

Applicability Boundaries (Based on All Experimental Data)

Scenario Type	CNBE Performance	Typical Domains	Reason
Multi-dimensional continuous value + structured temporal	✅ Significantly better than baseline	Meteorology, ecology, finance, mathematics	Bit-field structured encoding naturally matches
Strong classification features	❌ Inferior to One-hot	Sociology (8 regions + 4 time periods)	Bit-field mixed encoding cannot distinguish classification field weights
Single-variable deterministic systems	⚠️ Equal to Raw	Physics (gravitational field)	Continuous value single-variable scenario Raw is optimal
Zero-shot unfamiliar domains	⚠️ Close to domain standard	Biology (protein)	First attempt approaches 30-year optimized standard
Pattern recognition tasks	✅ Universally better than One-hot	Mathematical reasoning	Structured encoding matches pattern recognition

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Tech Stack

Application Layer: Chinese BASIC interpreter + Text Editor + Tao Te Ching
System Layer: Full Chinese Shell + CNBE Runtime (map/extract/cmp)
Hardware Layer: RISC-V 1GHz + 1GB RAM (QEMU + Spike)
Instruction Layer: cnhe.map / cnhe.extract / cnhe.cmp
Encoding Layer: 32-bit CJK Structured Bit Fields (Radical/Stroke/Structure)

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AI Agent Driven / AI Factory

This is a project that was previously impossible to complete, but is destined to be born in the AI era.

Past	Present
97,686 Chinese character annotations required thousands of linguist man-years	AI Agent assisted automated annotation
Full-stack validation required top-tier teams for years	LLM-assisted code generation + validation
Single-team siloed development	Open source community collaborative exploration

The dreams of scientists from the last century finally have a chance to be realized in the AI Agent era.

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Quick Start

Environment Requirements

• Python 3.8+
• numpy, torch, scikit-learn (for experiment reproduction)

Install Python SDK

pip install numpy torch scikit-learn

Usage Example

import sys; sys.path.insert(0, 'src')
from cnbe32 import encode_cnbe, hamming_distance

code_ming = encode_cnbe(72, 8, 1)   # 明 (bright) = 日(sun, 72) + 8 strokes + left-right structure
code_an  = encode_cnbe(72, 9, 1)   # 暗 (dark) = 日(sun, 72) + 9 strokes + left-right structure
print(hamming_distance(code_ming, code_an))

Run RISC-V Simulator

cd hardware/simulator
gcc -o cnhe_sim cnhe_sim.c -Wall -O2 && ./cnhe_sim

Launch Full Chinese Operating System (QEMU)

# Ubuntu dependencies
sudo apt-get install -y gcc-riscv64-linux-gnu qemu-system-misc

cd v84_riscv_os_full
make all && make run

Reproduce Experiments

cd v10_8_math_reasoning && python run_v108.py
cd v10_3_typhoon && python v10_3_typhoon.py

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Application Scenarios

CNBE-32 is designed for AI-era Chinese computing infrastructure, not as a general-purpose encoding tool.

Scenario	Suitability	Description
AI model structured input	Recommended	Provides radical/stroke/structure priors, improves small model comprehension
RISC-V hardware acceleration	Recommended	Custom instructions operate directly on encoding bitfields
Chinese-native OS	Recommended	Native support for filenames, paths, system messages
Chinese compiler/BASIC	Recommended	Direct encoding operations at the language level
Data compression/obfuscation	Not recommended	Semantic encoding, not compression
URL transmission	Not recommended	Characters broken by %-encoding in URLs
Human transcription	Not recommended	Visually similar characters cause errors
Database primary keys	Use with caution	32-bit integers are storable but CNBE is not a unique identifier

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Project Structure

CNBE-32-Chinese-Native-Binary-Encoding/
|-- docs/specification/      # Encoding specification
|-- docs/EXPERIMENTS.md      # Experiment overview
|-- docs/VISION.md           # Strategic vision
|-- src/cnbe32/              # Python SDK
|-- include/cnbe32.h         # C header file
|-- data/                    # Encoding database
|-- tests/                   # Test suite
|-- tools/                   # Development tools
|-- bindings/rust/           # Rust bindings
|-- hardware/                # RISC-V simulator
|-- v9_jepa_tree/            # JEPA experiments (v9)
|-- v10_5~v10_8/             # Cross-domain experiments (v10)
|-- v84_riscv_os_full/       # Chinese OS prototype
|-- results/                 # White papers (41 documents)
|-- LICENSE                  # Mulan License

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Roadmap

Phase	Status	Content
Encoding & semantic validation	Completed	v1-v6 CJK encoding design
Hardware & system	Completed	v7-v8 RISC-V + Chinese OS
Complex prediction validation	Completed	v9-v10 9-domain validation
AI compiler	Planned	Chinese natural language → machine code
Edge AI integration	Planned	Edge AI default standard
Ecosystem collaboration	Vision	Open source community + industry standards

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How to Contribute

Current Directions Most Needing Community Support

• Chinese BASIC interpreter optimization - improve lexical analyzer
• RISC-V lookup logic acceleration - optimize 81.6 KB L2 Cache hit rate
• JEPA architecture extension experiments - more physics/biology system tests
• Frontend visualization tools - Web interface showing encoding decomposition process

Level	Direction
Low barrier	Encoding dictionary / Test cases / Documentation
High barrier	RISC-V pipeline / FPGA / LLM adaptation / Compiler

See CONTRIBUTING.md for details

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Disclaimer

v10.x stage financial time series (US stocks / A-shares) backtesting is solely for validating CNBE-32's feature extraction and structured prior capabilities in high-noise, non-stationary time series data, and does not constitute any investment advice.

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License

Mulan Permissive Software License v2 (Mulan PSL v2)

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Let Chinese speakers enter the AI era through their native language.

From the "Digital China 2035" vision to AI Agent era engineering practice.

Born for Chinese AI ecosystem — from encoding to hardware, from single character to operating system.

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