IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations

[2607.26075] IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2607.26075 (cs) [Submitted on 13 Jul 2026 ( v1 ), last revised 22 Aug 2026 (this version, v2)] Title: IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations Authors: David Kaleko , Sergey Ivanov , Md Mofijul Islam View PDF HTML (experimental) Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines. Tuning IDP prompts, models, OCR settings, and schemas jointly currently costs domain specialists 20 to 80+ person-hours per document type and does not scale as enterprises add document classes. IDP AutoOpt runs a closed loop: it scores a configuration on a small labeled set, diagnoses field-level errors, generates targeted edits, and re-evaluates, guided by human-authored domain skills that encode production expertise. Across extraction, classification, and packet-splitting tasks deployed in healthcare, marketing-intelligence, and financial-services settings, IDP AutoOpt matches or exceeds human-expert accuracy at equal or lower cost (on an extraction benchmark, 90.2% vs 81.6% at 4.6 x lower per-page cost), cutting configuration time from weeks to under two hours. We further show that agent LLM capability has a hard threshold below which optimization fails, and that curated domain skills outperform raw source-code access, which can degrade performance when provided without structure. We also share practical lessons on context management and variance mitigation. Requiring only a configurable pipeline, a scoring function, and a small labeled set, the approach extends beyond IDP to other enterprise AI systems, such as RAG and multi-agent workflows, where configuration bottlenecks deployment. Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2607.26075 [cs.IR] (or arXiv:2607.26075v2 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.26075 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Md Mofijul Islam [ view email ] [v1] Mon, 13 Jul 2026 20:14:14 UTC (7,337 KB) [v2] Sat, 22 Aug 2026 05:04:27 UTC (7,348 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2026-07 Change to browse by: cs cs.AI References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations#665883
• GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings#447986
• AI-assisted, human-in-the-loop pipeline for converting historical documents into structured database: a feasibility study of environmental application in Michigan.#1001019
• LazyAgent: Demand-Driven Materialization and Physical Optimization of Agentic Programs#1048453
• Beyond Autonomy: A Dynamic Tiered AgentRunner Framework for Governable and Resilient Enterprise AI Execution#175326