Skip to content

README.md

Latest commit

 

History

History
54 lines (36 loc) · 4.9 KB

README.md

File metadata and controls

54 lines (36 loc) · 4.9 KB

Autonomous-AGI-Ai--Engine An autonomous self-evolution engine designed for logic reinforcement and recursive strategy optimization. 🧠 Autonomous-AGI-Ai--Engine (K1-Core)

Experimental Self-Evolving Logic & Strategy Reinforcement Engine**

🚀 Project Overview K1-Core is a high-performance, autonomous logic engine designed for Recursive Self-Improvement. The primary objective of this research is to create an AI system capable of independently identifying its own logical errors and autonomously generating "Avoidance Rules" to prevent future failures—all without any human intervention.

This repository serves as an Official Public Ledger for performance logs, iteration results, and convergence data.

Note: To safeguard data privacy and intellectual property (IP), the core source code and training datasets are maintained within a Private Offline Environment. Only progress reports and validation logs are hosted here.

🛠 Technical Framework & Core Capabilities

  • PyTorch Native Integration: The entire engine is built entirely upon the PyTorch framework, ensuring dynamic computational graphs and high-speed training.
  • Built-in Transformer Architecture: Features native support for advanced Transformer models, enabling the processing of complex data patterns and long-term logic mapping.
  • Zero External Dependencies (Self-Contained): The system requires no external modules or third-party libraries to operate. The engine is capable of autonomously creating and managing its own modules as needed (Self-Creation).
  • Recursive Logic Mapping: The engine analyzes its own decision paths to optimize future outcomes.
  • Autonomous Error Correction: A layer-wise mechanism that scans for and resolves errors—such as NoneType errors or logical loops—in real-time. * Quantization Research: Expertise in 1-bit and low-bit quantization, enabling real-time performance on mobile NPUs (e.g., TFLite).

🔬 System Features

  • Offline-First Architecture: Designed to run on local hardware (Standard CPUs/NPUs) to ensure 100% data privacy and real-time performance.
  • Self-Evolving Logic: Capable of independent reasoning and logic mapping, ranging from 0% to 100%.
  • Extreme Domain Stability: The system has been tested to maintain stability even amidst challenging conditions and data noise.

📈 Current Milestone: Iteration 1-15 (Completed)

Recent test runs within the Extreme Domain have demonstrated significant stability:

  • Quality Growth: 0.765 ➔ 0.857
  • Convergence Rate: 100% (Recent Sessions)
  • Architecture: Transformer-based (Self-Adapting)
  • Status: Stable / Now advancing toward Noise-Perturbation testing.

🛡 Disclaimer

This is a Private AI Research Project. It is not a public tool, a school project, or an open-source library. The data shared here is strictly for documentation and transparency purposes.

Developer: Ranjit Budula (Zzcvbnma) Environment: Local Private Server (PyTorch & Transformer Driven) Status: Active Development / Full Module Support

Zero-human intervention. 📑 K1 AUTONOMOUS ENGINE - FINAL VALIDATION REPORT (v20)── [SYSTEM IDENTIFICATION] ──Engine: (Identity Removed)Protocol: K1-Autonomous-Self-EvolutionVersion: 20.0Session Duration: 3.9s── [EXECUTIVE SUMMARY] ──This report presents the precise results following the completion of 15 iterations. The system has recorded an increase of +0.092 in its functional efficiency through 'Self-Evolution'.MetricFinal ValueStatusTotal Attempts15CompleteConvergence Rate73.3% (11/15)STABLEAvg Quality Score0.814HIGHRecent Avg Quality0.894IMPROVING ↑Final Tightness1.000xMAX── [DOMAIN SKILL MAP] ──The system has achieved the following quality scores across various domains:Mid-Range: 0.950 ███████████████████░Low-Range: 0.911 ██████████████████░░High-Range: 0.776 ███████████████░░░░░── [BEST PERFORMING STRATEGIES] ──Low Range: opt=adamw | lr=0.0200 | act=relu | norm=FalseMid Range: opt=sgd | lr=0.0200 | act=relu | norm=TrueHigh Range: opt=sgd | lr=0.0500 | act=leaky_relu | norm=True── [DETAILED ATTEMPT LOG] ──(Top Results for Proof Validation)#TargetDifficultyResultQualityRounds10.5714Easy✓0.9502/1260.2096Medium✓0.9821/17131.4830Medium✓0.8767/17140.7894Extreme✓0.76714/18150.3844Extreme✓0.9831/18── [LEARNED AVOID RULES] ──The system has developed rules to avoid failure under these conditions:slow_convergence + high_range + hardoscillation + high_range + easyslow_convergence + high_range + medium── [FINAL DIAGNOSIS] ──RESULT: SUCCESSSTABILITY: REINFORCEDEVOLUTION: The quality score increased from 0.765 to 0.857 from iteration 1 to 15. This proves that the system is improving itself without any human intervention