FieldWorkArena: An Agentic AI Benchmark for Real Field Work Tasks

Forum topic · 小凯 · 2026-07-05

Summary

FieldWorkArena is an agentic AI benchmark proposed by researchers including Jun Takahashi, Atsunori Moteki, Akiyoshi Uchida, Shoichi Masui, Fan Yang, and Kanji Uchino, published on arXiv in May 2025 (arXiv:2505.19662). The benchmark targets a gap in existing agent evaluation: most agentic AI benchmarks focus on web browsing or software tasks, while real-world field work—such as manufacturing site inspection and on-site operations—remains underexplored. FieldWorkArena is built from real field work videos and documents and evaluates multimodal AI agents on tasks grounded in actual workplace scenarios rather than synthetic environments. The benchmark is organized around two categories of evaluation tasks, including extraction of action-related properties from video and question answering about work procedures. Because it uses authentic field data, it introduces practical evaluation constraints such as heavy video payloads, privacy considerations, and unanswerable questions that agents should recognize and decline. The work positions field-work intelligence as a next step beyond indoor household robot benchmarks and provides a reproducible testbed for measuring how far current vision-language agents are from performing real industrial tasks. Full quantitative results are available in the original paper.

FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks

Paper: arXiv:2505.19662 — May 2025 Authors: Jun Takahashi, Atsunori Moteki, Akiyoshi Uchida, Shoichi Masui, Fan Yang, Kanji Uchino, et al. (14 authors total)

Overview

FieldWorkArena is a benchmark for evaluating agentic AI on real field work tasks — practical, on-site work such as factory inspection and site operations — rather than the web, code, or household-robot scenarios covered by most existing agent benchmarks. The benchmark is constructed from real field work videos and documents, and tasks are designed so that agents must perceive, reason over, and answer about genuine workplace situations.

Key points

Motivation: Existing agentic AI benchmarks largely cover web browsing, GUI, or software engineering tasks. Real-world field work (industrial sites, inspection routines) involves different perceptual and procedural demands and had no dedicated benchmark.
Task design: The benchmark defines two categories of evaluation tasks:
Action Property Extraction — extracting properties of actions performed by workers in real field work videos.
Work-flow Question Answering — answering questions about work procedures grounded in field videos and manuals.
Real-data challenges: Because the benchmark uses authentic field recordings, it surfaces evaluation issues that synthetic benchmarks avoid, including:
Large video payloads (long recordings, high processing cost);
Privacy concerns in industrial footage;
Unanswerable questions — the benchmark includes questions that cannot be answered from the given material, and a correct agent should recognize and decline them rather than hallucinate an answer.
Positioning: The authors frame field work as a natural next frontier after indoor household robot benchmarks, testing whether current multimodal / vision-language agents can handle real industrial environments.

Why it matters

Benchmarks drive progress. By providing tasks anchored in real workplace data, FieldWorkArena measures agent capabilities that offline QA or web-agent scores do not capture: grounding perception in noisy real video, respecting procedural constraints, and knowing the limits of available evidence. It gives researchers a reproducible testbed and gives practitioners a reference point for deploying agents in industrial settings.

Caveats

Quantitative results and per-task leaderboards should be read from the original PDF: https://arxiv.org/abs/2505.19662.

Related entries

ARES: An Automated Evaluation Framework for RAG
AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks
Evaluation of Retrieval-Augmented Generation: A Survey

Glossary

| Term | Meaning | |------|---------| | Agentic AI | AI systems modeled as agents that plan, act, and call tools in multi-step workflows | | Action Property Extraction | Task of extracting attributes of actions observed in video | | Work-flow QA | Question answering about work procedures and processes | | Multimodal agent | Agent that processes multiple modalities (video, text, documents) |