DocMemo: Dynamic Evidence Discovery via Probabilistic Memory-Guided Retrieval for Multi-Modal Document Understanding

TL;DR - DocMemo is a memory-guided retrieval framework that treats long multi-modal document understanding as iterative, dynamic evidence exploration rather than a one-shot top-k page fetch. It matters because it addresses a core failure mode of RAG over hundreds of pages: early retrieval mistakes that current single-round systems cannot recover from.

• Maintains a tri-level retrieval state: Document Schema Memory (structural priors), Page Belief Memory (dynamic relevance estimates), and Question Episodic Memory (query-specific reasoning trajectories), explicitly modeling how state propagates across rounds.
• Page selection is refined via Bayesian belief updating with Thompson sampling for exploration/exploitation, plus spatial proximity propagation so relevance spreads to neighboring pages.
• Uses structure-aware adaptive-granularity access, supplementing page-level retrieval with fine-grained visual regions for multi-modal evidence.
• Reports state-of-the-art results on 3 benchmarks (specific datasets/metrics not given in the abstract); code released at github.com/Harrygof/DocMemo.