TL;DR
- Use Case: Real business for PDF/image-text layout and knowledge base retrieval
- Conclusion: “Visual compression → Structured → Retrieval/Memory” three-stage approach
- Output: Research agenda, application demo directions, engineering risk checklist, and error quick reference
Version Matrix
| Project/Direction | Status | Description |
|---|---|---|
| Context optical compression → Long memory prototype | No/Planned | Layered memory based on “fuzzy compression of old info + clear retention of new info” |
| Cross-modal information extraction | Partial/Reproducible | Direct output of tables/key info from image-text pages |
| Domain-specific models | No/Planned | Domain fine-tuning and layout priors on general models |
| Distillation and small model deployment | No/Planned | Target: 2-3× compression on edge devices |
| PDF AI assistant Demo | Partial/Reproducible | PDF→Markdown→Summary/Q&A |
| Multimodal document retrieval | Partial/Reproducible | Unified embedding using text/visual tokens |
| Optical compression theory evaluation | No/Research topic | Information theory metrics and attention heatmap analysis |
Research Directions
1. Infinite Context Memory Mechanism
Layered memory based on “fuzzy compression of old information + clear retention of new information,” simulating human memory patterns.
2. Cross-modal Information Extraction
Image-text hybrid information extraction, moving from “recognizing characters” to “understanding content.”
3. Larger Scale and Domain-specific Models
- 3B→30B→100B parameters
- Domain fine-tuning: Medical, Legal OCR-VL
4. Model Compression and Distillation
500M parameter Tiny OCR model for edge device deployment.
5. Theoretical Exploration of Visual Compression
Quantifying information content of visual tokens from information theory perspective.
Innovative Application Ideas
1. PDF AI Assistant
Build PDF Q&A AI in a few lines of code.
2. AI Learning Note Organization
Convert handwritten notes to Markdown format via photo capture.
3. Knowledge Base Optical Compression
Store text as images, decode at query time.
4. Multimodal Document Search
Search images by text, search text by images.
5. Gamified Science Videos
“What would happen if AI only looked at pictures but not text?”
Error Quick Reference
| Symptom | Root Cause | Fix |
|---|---|---|
| PDF→Markdown loses structure | Complex layout | Add layout analysis |
| Chart value restoration inaccurate | Curves not semantic | Dedicated benchmark |
| Long memory Q&A contradictions | Unstable compression strategy | Re-retrieve and re-decode |
| Low recall in image-text search | Embedding misalignment | Contrastive learning distillation |
| Edge device inference timeout | Model too large | Quantization + distillation |
| Math/formula recognition errors | Unstable tokenization | LaTeX constrained generation |
| Compliance/privacy risks | Sensitive info | Sensitive word detection |