@inproceedings{wang-etal-2026-ledger,
title = "{LEDGER}: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval",
author = "Wang, Hang and
Garg, Utkarsh and
Davari, Reza and
Jiao, Huitian and
Cheng, Hao and
Peng, Baolin and
Chen, Si-Qing and
Ge, Tao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.515/",
pages = "10614--10644",
ISBN = "979-8-89176-395-1",
abstract = "Large language models increasingly power AI agents for tasks requiring iterative refinement: document editing demands targeted revisions while preserving cross-references, code refactoring requires tracking function dependencies, and knowledge base updates cascade through related entities. Iterative editing with AI agents faces a fundamental efficiency-consistency tradeoff: maintaining consistency requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. Isolated edits improve efficiency but risk breaking cross-references and violating semantic constraints. We introduce LEDGER (scaLing Agentic document editing with Dependency-aware Graph rEtRieval), a framework that constructs lightweight dependency graphs capturing semantic relationships and structural hierarchies across document elements. For each edit, graph traversal identifies affected elements and retrieves only necessary context. Experiments across 1,900 test cases spanning six state-of-the-art models show LEDGER achieves 76 consistency versus 56 baseline while reducing token usage by 85 . Critically, LEDGER with low reasoning effort matches baseline performance at high reasoning effort using 70 fewer tokens, suggesting explicit dependency representations can substitute for expensive internal reasoning with implications for agentic systems operating on structured data."
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<abstract>Large language models increasingly power AI agents for tasks requiring iterative refinement: document editing demands targeted revisions while preserving cross-references, code refactoring requires tracking function dependencies, and knowledge base updates cascade through related entities. Iterative editing with AI agents faces a fundamental efficiency-consistency tradeoff: maintaining consistency requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. Isolated edits improve efficiency but risk breaking cross-references and violating semantic constraints. We introduce LEDGER (scaLing Agentic document editing with Dependency-aware Graph rEtRieval), a framework that constructs lightweight dependency graphs capturing semantic relationships and structural hierarchies across document elements. For each edit, graph traversal identifies affected elements and retrieves only necessary context. Experiments across 1,900 test cases spanning six state-of-the-art models show LEDGER achieves 76 consistency versus 56 baseline while reducing token usage by 85 . Critically, LEDGER with low reasoning effort matches baseline performance at high reasoning effort using 70 fewer tokens, suggesting explicit dependency representations can substitute for expensive internal reasoning with implications for agentic systems operating on structured data.</abstract>
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%0 Conference Proceedings
%T LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval
%A Wang, Hang
%A Garg, Utkarsh
%A Davari, Reza
%A Jiao, Huitian
%A Cheng, Hao
%A Peng, Baolin
%A Chen, Si-Qing
%A Ge, Tao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-ledger
%X Large language models increasingly power AI agents for tasks requiring iterative refinement: document editing demands targeted revisions while preserving cross-references, code refactoring requires tracking function dependencies, and knowledge base updates cascade through related entities. Iterative editing with AI agents faces a fundamental efficiency-consistency tradeoff: maintaining consistency requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. Isolated edits improve efficiency but risk breaking cross-references and violating semantic constraints. We introduce LEDGER (scaLing Agentic document editing with Dependency-aware Graph rEtRieval), a framework that constructs lightweight dependency graphs capturing semantic relationships and structural hierarchies across document elements. For each edit, graph traversal identifies affected elements and retrieves only necessary context. Experiments across 1,900 test cases spanning six state-of-the-art models show LEDGER achieves 76 consistency versus 56 baseline while reducing token usage by 85 . Critically, LEDGER with low reasoning effort matches baseline performance at high reasoning effort using 70 fewer tokens, suggesting explicit dependency representations can substitute for expensive internal reasoning with implications for agentic systems operating on structured data.
%U https://aclanthology.org/2026.findings-acl.515/
%P 10614-10644
Markdown (Informal)
[LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval](https://aclanthology.org/2026.findings-acl.515/) (Wang et al., Findings 2026)
ACL
- Hang Wang, Utkarsh Garg, Reza Davari, Huitian Jiao, Hao Cheng, Baolin Peng, Si-Qing Chen, and Tao Ge. 2026. LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval. In Findings of the Association for Computational Linguistics: ACL 2026, pages 10614–10644, San Diego, California, United States. Association for Computational Linguistics.