LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval
We introduce LEDGER to tackle the novel context engineering challenge of agentic document editing, where localized edits to long, structured documents must be applied efficiently without breaking cross-references or semantic consistency. LEDGER constructs a lightweight dependency graph that explicitly models document structure, including hierarchical organization, explicit references, implicit dependencies, and semantic relationships. For each edit, graph-guided retrieval selects only the necessary context, avoiding full-document processing while preserving consistency. We evaluate LEDGER on a curated benchmark of 1.9k test cases with various document types and lengths, spanning six state...
| Authors | Mike Hang Wang, Utkarsh Garg, Reza Davari et al. |
|---|---|
| Primary category | cs.IR |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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