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New benchmark FormGym tests AI agents on form-filling tasks

Researchers have introduced FormGym, a new benchmark designed to evaluate AI agents' ability to complete paperwork, particularly in image-only formats without OCR. The benchmark comprises 55 documents with 432 fields, requiring multi-modal understanding and tool-use. Initial tests showed very low accuracy for baseline Vision-Language Agents (VLAs) and GUI agents, prompting the development of FieldFinder, a tool to aid LLMs in locating form fields. The integration of FieldFinder significantly improved model performance across various conditions. AI

IMPACT This benchmark could accelerate research into AI agents capable of complex, multi-modal tasks like form completion.

RANK_REASON The cluster describes a new academic paper introducing a benchmark and a tool for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark FormGym tests AI agents on form-filling tasks

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Matthew Toles, Rattandeep Singh, Isaac Song, Zhou Yu ·

    FormGym: Doing Paperwork with Agents

    arXiv:2506.14079v4 Announce Type: replace Abstract: Completing paperwork is a challenging and time-consuming problem. Form filling is especially challenging in the pure-image domain without access to OCR, typeset PDF text, or a DOM. For computer agents, it requires multiple abili…