IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations
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-ser...
| Authors | David Kaleko, Sergey Ivanov, Md Mofijul Islam |
|---|---|
| Primary category | cs.IR |
| Published | 13 July 2026 |
| Last updated at source | 22 August 2026 |
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