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MegaRAG: Stop Chunking Blindly, Start Reading Like a Human
If you have ever built a RAG system for real documents, you probably know the feeling: the model finds the right page, but still misses the point.
There is a question: What if the system could build a document-level view, instead of reasoning over isolated chunks alone?
The Problems of Current RAG and GraphRAG Systems
Many existing RAG and GraphRAG systems share a few structural weaknesses. They often miss the big picture, struggle with deeper reasoning, and tend to treat documents as pure text. Charts, diagrams, tables, and layout are usually processed in isolation, if at all.
There are five underlying reasons behind this.
Long Documents are Inherently Difficult to Handle
Even though modern multimodal models can process both images and text, they are still constrained by context window limits. When dealing with entire books, lengthy reports, or full lecture decks, true global understanding becomes elusive.
This limitation directly affects a model’s ability to grasp high-level concepts and the overall structure of the content. Without a holistic view, deeper reasoning suffers.
Most Existing GraphRAG Approaches Remain Text-Centric
Methods such as GraphRAG and LightRAG excel at extracting entities and relations…
