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🧠 Cognitive Workspace - Towards Functional Infinite Context Through Active Memory Management

A proof-of-concept implementation demonstrating how active memory management outperforms traditional RAG systems through:

✨ Key Features: • Active memory management vs passive retrieval • Hierarchical memory buffers (immediate → working → episodic) • State persistence across multi-turn dialogues • Metacognitive control with confidence tracking • 54-59% memory reuse rate in conversations

📊 Performance: • 17-18% net efficiency gain over traditional RAG • Sub-linear operation growth pattern • Statistical significance: p < 0.001 • Cohen's d effect size: 23-195 (huge to extremely large)

🔬 Experiments: • Single-turn complex task processing • Multi-turn dialogue (4-10 rounds) • Multi-hop reasoning chains • Information conflict resolution

🚀 Quick Start: Supports OpenAI API, Azure OpenAI, local models (Ollama), and simulation mode for testing without API keys.

📝 Research paper: arXiv:2508.13171