GENESIS - Cognitive Computing Platform - Multi-Technology Cognitive Computing Research Platform

Private Research Prototype

100%

Internal, unaudited status as of the last recorded update: semantic tokenizer component complete, HDC system in research phase

GENESIS - Cognitive Computing Platform

Multi-Technology Cognitive Computing Research Platform

Live Dashboard Training Progress Technical Architecture Innovations

Project Overview

Private research prototype exploring semantic-guided tokenization, quantum-inspired HDC systems, and neural-symbolic integration. Combines multiple experimental techniques with a research goal of reducing hallucination and enabling real-time knowledge transfer. Not independently audited or publicly reproduced; figures below are internal, unaudited measurements from the project's own last recorded update.

Key Metrics

20,000

Vector Dimensions

8.8GB

HDC Lexicon

23+ GFLOPS

Performance

<100MB

Memory Usage

Technology Stack

Julia Rust Neural-Symbolic AI Quantum Computing

Key Features

A semantic-aware BPE tokenizer architecture (internal research, not independently verified as novel)
S-P-A framework: Subject-Predicate-Attribute semantic roles
20,000-dimensional hypervectors with quantum enhancement concepts
Designed to reduce hallucination through symbolic reasoning constraints (internal, unaudited evaluation)
Real-time knowledge transfer during training (71.6% complete)
Local deployment optimized for consumer hardware (<100MB memory)
Cross-lingual native understanding (German/English/Romanian)
2,088 protected German legal terms preserved during training
Neural-symbolic integration for explainable AI decisions

Project Timeline

Started: 2025-06 Last Updated: 2025-11-09

Semantic Tokenizer Development

2025-06 - 2025-08

Semantic-aware BPE tokenizer (internal research)

Julia Performance Optimization

2025-07

Achieved 23+ GFLOPS throughput

HDC System Architecture

2025-08

20,000-dimensional hypervector design

Quantum Enhancement Framework

2025-09

Quantum-inspired optimization techniques

Neural-Symbolic Integration

2025-10

Hybrid reasoning system development

Live Training Dashboard

2025-11

Real-time metrics at 71.6% progress (2,088 terms)

Technical Details

Tokenizer:: Semantic-guided BPE with S-P-A framework
Languages:: Julia (performance), Rust (system components)
Hdc System:: 20,000-dimensional hypervectors with quantum enhancement
Data Size:: 8.8GB lexicon integration (5.0GB + 3.8GB)
Performance:: 23+ GFLOPS on AMD Ryzen systems
Memory:: <100MB runtime with enterprise pooling
Specialization:: German legal terminology + cross-lingual coherence
Deployment:: Local-first architecture for consumer hardware