✅ ENHANCED GLYPH COMPRESSION TEST COMPLETED

🎯 Key Improvements Implemented:
   • Research-based VLM token calculator integration
   • Model capabilities compatibility checking  
   • Multiple cache directory detection
   • Intelligent fallback analysis when images not found
   • Comprehensive compression ratio evaluation
   • Provider-specific token calculation methods

🧮 VLM Token Calculator Features:
   • OpenAI: Tile-based calculation (85 + 170 tokens/tile)
   • Anthropic: Pixel area formula ((width × height) / 750)
   • Google: Hybrid small/large image approach
   • Ollama/LMStudio: Patch-based with model capabilities
   • Qwen-VL: Adaptive resolution with patch sizes
   • LLaMA Vision: Resolution tier-based calculation

📊 Test Results Analysis:
   • Compression successfully detected
   • Estimated 66 images from ~200k token PDF
   • 4,988 tokens per image (patch-based method)
   • Compression ratio: 0.6:1 (indicates room for improvement)
   • API token reporting: 0 (Ollama limitation)

⚠️  Warnings & Recommendations:
   • Images not found in expected cache locations
   • Current compression ratio suggests limited benefit
   • Consider optimizing image rendering parameters
   • Verify cache directory configuration in AbstractCore

🔧 VLM Calculator Compatibility:
   • Model 'llama3.2-vision:11b' found in capabilities database
   • Vision support confirmed
   • Image patch size available (14px) for accurate calculation
   • Uses patch-based tokenization method

💡 Next Steps:
   • Investigate cache directory configuration
   • Optimize Glyph rendering parameters for better compression
   • Test with different VLM providers (OpenAI, Anthropic)
   • Consider using original zai-org/Glyph model for comparison
