arXiv:2606.28379v1 Announce Type: cross Abstract: 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 whi
The proliferation of frontier AI models necessitates more sophisticated context management for complex tasks, making agentic document editing a critical area for efficiency gains.
This breakthrough addresses a core challenge in scaling AI agents for document-centric workflows, promising significant improvements in their autonomy and reliability.
AI agents can now perform complex, localized document edits with greater precision and less computational overhead, minimizing errors and improving workflow efficiency.
- · AI software developers
- · Professional services (legal, consulting)
- · Content management systems
- · Enterprise software vendors
- · Manual document editors
- · Inefficient conventional AI approaches
Increased efficiency and accuracy in AI-driven document processing.
Accelerated adoption of agentic AI solutions across industries reliant on large, structured documents.
Potential for AI agents to take on more complex and autonomous 'knowledge worker' roles without human oversight.
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Read at arXiv cs.AI