Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13504 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910954991648768 |
|---|---|
| author | Amjad, Ayesha Sthapit, Saurav Syed, Tahir Qasim |
| author_facet | Amjad, Ayesha Sthapit, Saurav Syed, Tahir Qasim |
| contents | Extracting alphanumeric data from form-like documents such as invoices, purchase orders, bills, and financial documents is often performed via vision (OCR) and learning algorithms or monolithic pipelines with limited potential for systemic improvements. We propose an agentic AI system that leverages Large Language Model (LLM) agents and a reinforcement learning (RL) driver agent to automate consistent, self-improving extraction under LLM inference uncertainty. Our work highlights the limitations of monolithic LLM-based extraction and introduces a modular, multi-agent framework with task-specific prompts and an RL policy of rewards and penalties to guide a meta-prompting agent to learn from past errors and improve prompt-based actor agents. This self-corrective adaptive system handles diverse documents, file formats, layouts, and LLMs, aiming to automate accurate information extraction without the need for human intervention. Results as reported on two benchmark datasets of SOIRE, and CORD, are promising for the agentic AI framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents Amjad, Ayesha Sthapit, Saurav Syed, Tahir Qasim Information Retrieval Artificial Intelligence Multiagent Systems Extracting alphanumeric data from form-like documents such as invoices, purchase orders, bills, and financial documents is often performed via vision (OCR) and learning algorithms or monolithic pipelines with limited potential for systemic improvements. We propose an agentic AI system that leverages Large Language Model (LLM) agents and a reinforcement learning (RL) driver agent to automate consistent, self-improving extraction under LLM inference uncertainty. Our work highlights the limitations of monolithic LLM-based extraction and introduces a modular, multi-agent framework with task-specific prompts and an RL policy of rewards and penalties to guide a meta-prompting agent to learn from past errors and improve prompt-based actor agents. This self-corrective adaptive system handles diverse documents, file formats, layouts, and LLMs, aiming to automate accurate information extraction without the need for human intervention. Results as reported on two benchmark datasets of SOIRE, and CORD, are promising for the agentic AI framework. |
| title | An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents |
| topic | Information Retrieval Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2505.13504 |