MemWalker

Tree of summary nodes: Preprocesses long context into a hierarchical tree of summary nodes, compressing and structuring the information.

Query-driven traversal: Given a query, the LLM traverses the tree through iterative prompting, descending into subtrees that are most relevant to the question.

Reasoning-based reading: The traversal decisions are reasoning-based, so the model can explain which part of the document it consulted and why.

Explainability bonus: The traversal trace serves as a human-readable explanation of the model's document reading, improving debuggability of long-context QA.