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arxiv: 2505.19662 · v3 · submitted 2025-05-26 · 💻 cs.AI · cs.CV

FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks

Jun Takahashi , Atsunori Moteki , Akiyoshi Uchida , Shoichi Masui , Fan Yang , Kanji Uchino , Yueqi Song , Yonatan Bisk
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Graham Neubig Ikuo Kusajima Yasuto Watanabe Hiroyuki Ishida Koki Nakagawa Shan Jiang
This is my paper

Pith reviewed 2026-05-19 14:32 UTC · model grok-4.3

classification 💻 cs.AI cs.CV
keywords agentic AIbenchmarkreal-world evaluationmultimodal LLMsafety hazard detectionfield workmanufacturingretail
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The pith

FieldWorkArena uses real factory and retail photos to test whether agentic AI can spot safety hazards and rule violations on site.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces FieldWorkArena as a benchmark that moves agentic AI evaluation out of simulations and into actual manufacturing, warehouse, and retail settings. It builds a dataset from on-site images and videos paired with tasks created through direct interviews with workers and managers. The work improves the scoring method to better match how multimodal models process visual and textual information together. If successful, this approach would let developers measure real-world reliability instead of relying on synthetic test environments.

Core claim

FieldWorkArena supplies a publicly available dataset of real-world images and videos from factories, warehouses, and retail sites together with tasks derived from site-worker interviews, plus a revised evaluation function that accounts for the visual-textual reasoning patterns of models such as GPT-4o. Evaluation on this benchmark demonstrates that performance measurement of agentic AI under these conditions is feasible, while also surfacing both strengths and remaining limitations of the new scoring approach.

What carries the argument

FieldWorkArena benchmark: a collection of on-site visual data and interview-based tasks paired with an evaluation function redesigned to handle multimodal LLM characteristics when scoring agent responses on hazard detection and compliance checks.

If this is right

  • Agentic systems can be compared on tasks that require interpreting genuine workplace visuals rather than generated scenes.
  • Evaluation scores can reflect how well a model integrates image content with task instructions in the style of current multimodal models.
  • Public release of the dataset and scoring code enables repeated testing and incremental improvement of field-deployed agents.
  • The identified limitations point to specific areas where multimodal reasoning still needs strengthening for practical use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same data-collection method could be repeated in construction or logistics to create comparable benchmarks for those domains.
  • Over time the benchmark could serve as a training signal for agents that improve through repeated real-site feedback loops.
  • Widespread adoption might encourage standardization of safety-inspection procedures across different companies and sites.

Load-bearing premise

The on-site images, videos, and tasks gathered from a limited set of factories, warehouses, and retail locations are representative enough of broader real-world field work to support general performance claims.

What would settle it

Running the same agents on the benchmark and then deploying them live at the original sites and finding that benchmark scores fail to predict actual success rates in spotting hazards or violations.

Figures

Figures reproduced from arXiv: 2505.19662 by Akiyoshi Uchida, Atsunori Moteki, Fan Yang, Graham Neubig, Hiroyuki Ishida, Ikuo Kusajima, Jun Takahashi, Kanji Uchino, Koki Nakagawa, Shan Jiang, Shoichi Masui, Yasuto Watanabe, Yonatan Bisk, Yueqi Song.

Figure 1
Figure 1. Figure 1: Example of FieldWorkArena dataset which includes images and videos taken on site, documents, queries, and ground truth. We propose an agentic AI benchmark suite FieldWorkArena, aimed at pro￾moting the introduction of field-monitoring oriented agents in fieldwork envi￾ronments. FieldWorkArena includes over 400 types of data (images, videos, work manuals) and approximately 900 field-specific queries from thr… view at source ↗
Figure 2
Figure 2. Figure 2: Overall system configuration of FieldWorkArena. 4.1 Definition of Action Space In the context of complex, real-world scenarios, the ability of an intelligent agent to effectively interact with its environment is fundamentally defined by its action space. In this first-step implementation of FieldWorkArena, we define a coarse action space and add it to BrowserGym. The agent invokes an action space, and the … view at source ↗
read the original abstract

This paper introduces FieldWorkArena, a benchmark for agentic AI targeting real-world field work. With the recent increase in demand for agentic AI, they are built to detect and document safety hazards, procedural violations, and other critical incidents across real-world manufacturing and retail environments. Whereas most agentic AI benchmarks focus on performance in simulated or digital environments, our work addresses the fundamental challenge of evaluating agents in the real-world. In this paper, we improve the evaluation function from previous methods to assess the performance of agentic AI in diverse real-world tasks. Our dataset comprises on-site captured images/videos in factories, warehouses and retails. Tasks were meticulously developed through interviews with site workers and managers. Evaluation results confirmed that performance evaluation considering the characteristics of Multimodal LLM (MLLM) such as GPT-4o is feasible. Furthermore, this study identifies both the effectiveness and limitations of the proposed new evaluation methodology. The complete dataset and evaluation program are publicly accessible on the website (https://en-documents.research.global.fujitsu.com/fieldworkarena/)

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces FieldWorkArena, a benchmark for agentic AI targeting real-world field work tasks such as detecting safety hazards and procedural violations in manufacturing, warehouse, and retail environments. It relies on on-site captured images and videos, with tasks developed from interviews with site workers and managers. The authors describe an improved evaluation function designed to account for Multimodal LLM characteristics (e.g., GPT-4o) and report that their evaluations confirm the feasibility of performance assessment in these settings, while also identifying the methodology's effectiveness and limitations. The full dataset and evaluation program are released publicly.

Significance. If the benchmark construction and evaluation improvements hold up under scrutiny, the work could meaningfully advance agentic AI assessment by moving beyond simulated environments to realistic, domain-specific tasks. The public release of data and code is a clear strength that supports reproducibility and further research.

major comments (2)
  1. [Evaluation methodology section] The abstract and evaluation methodology section state that an 'improved evaluation function' enables feasible performance assessment considering MLLM characteristics, yet provide no concrete definition of those characteristics (e.g., visual ambiguity handling, multi-frame reasoning, or hallucination mitigation), no comparison to prior evaluation methods, and no supporting metrics or examples from the reported results. This directly affects the central claim that feasibility has been confirmed.
  2. [Results and discussion] The results confirming feasibility rest on an unspecified improved evaluation function whose details and validation are not evident; without quantitative outcomes, ablation studies, or explicit handling of real-world factors such as image quality variation, the load-bearing claim cannot be assessed.
minor comments (2)
  1. [Dataset section] The dataset description would benefit from explicit counts of tasks, images/videos, and diversity statistics in the main text rather than directing readers solely to the external website.
  2. [Introduction] Clarify the exact scope of 'field work tasks' early in the introduction to distinguish them more sharply from existing agentic benchmarks.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript introducing FieldWorkArena. We have reviewed the major comments carefully and agree that the evaluation methodology and results sections require additional clarification and supporting evidence to strengthen the central claims. We address each point below and commit to revisions that will incorporate the requested details without altering the core contributions of the work.

read point-by-point responses

  1. Referee: [Evaluation methodology section] The abstract and evaluation methodology section state that an 'improved evaluation function' enables feasible performance assessment considering MLLM characteristics, yet provide no concrete definition of those characteristics (e.g., visual ambiguity handling, multi-frame reasoning, or hallucination mitigation), no comparison to prior evaluation methods, and no supporting metrics or examples from the reported results. This directly affects the central claim that feasibility has been confirmed.

    Authors: We acknowledge the referee's observation that the current description of the improved evaluation function lacks sufficient specificity. The manuscript does reference improvements over prior methods to better suit MLLM behaviors in real-world settings, but we agree that explicit definitions, comparisons, and examples are not detailed enough. In the revised version, we will expand the evaluation methodology section to define the key MLLM characteristics addressed (including handling of visual ambiguity in on-site images, multi-frame reasoning for video-based tasks, and mitigation of hallucinations in incident documentation). We will add a comparison to existing evaluation approaches in agentic AI benchmarks and include concrete metrics and result examples that support the feasibility assessment. revision: yes

  2. Referee: [Results and discussion] The results confirming feasibility rest on an unspecified improved evaluation function whose details and validation are not evident; without quantitative outcomes, ablation studies, or explicit handling of real-world factors such as image quality variation, the load-bearing claim cannot be assessed.

    Authors: We agree that the results and discussion would be strengthened by more explicit validation of the evaluation function. The current manuscript reports that evaluations confirmed feasibility and identified effectiveness and limitations, but we recognize the need for greater transparency. In revision, we will include quantitative performance outcomes, ablation studies comparing the improved function to baseline methods, and a dedicated discussion of how real-world factors such as image quality variation, lighting differences, and environmental conditions from the on-site dataset are handled. These additions will provide clearer evidence for the claims while preserving the paper's focus on the benchmark and public data release. revision: yes

Circularity Check

0 steps flagged

No significant circularity: new benchmark and empirical evaluation are self-contained

full rationale

The paper introduces FieldWorkArena as a new benchmark with on-site captured images/videos and tasks derived from worker interviews. It reports an improved evaluation function and confirms feasibility via results on MLLM models like GPT-4o. No equations, fitted parameters, or self-citations are presented that reduce the feasibility claim or any result to prior inputs by construction. The derivation consists of dataset creation followed by direct empirical testing, which is independent of the target claim.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The benchmark relies on the assumption that the collected real-world data and interview-derived tasks accurately represent field work challenges for AI evaluation.

axioms (1)
  • domain assumption Real-world images and videos can be used to evaluate agentic AI performance in field tasks.
    Core setup of the benchmark using on-site captured data.

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discussion (0)

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Forward citations

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