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pith:G3RPO4EW

pith:2025:G3RPO4EW75ZJPTNBY45CFQIFKD
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FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks

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

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

arxiv:2505.19662 v4 · 2025-05-26 · cs.AI · cs.CV

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\pithnumber{G3RPO4EW75ZJPTNBY45CFQIFKD}

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1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
Portable graph bundle live · download bundle · merged state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Evaluation results confirmed that performance evaluation considering the characteristics of Multimodal LLM (MLLM) such as GPT-4o is feasible.

C2weakest assumption

The assumption that on-site captured images/videos from factories, warehouses and retails combined with tasks developed through interviews with site workers and managers provide a representative and sufficient basis for evaluating agentic AI performance in real-world conditions.

C3one line summary

A new benchmark dataset and evaluation framework for testing multimodal AI agents on real field work tasks derived from on-site data and worker interviews.

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1 paper in Pith

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First computed 2026-06-09T01:05:06.742875Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

36e2f77096ff7297cda1c73a22c10550df3eef12f83c6dc2ac9cd3179d5d6437

Aliases

arxiv: 2505.19662 · arxiv_version: 2505.19662v4 · doi: 10.48550/arxiv.2505.19662 · pith_short_12: G3RPO4EW75ZJ · pith_short_16: G3RPO4EW75ZJPTNB · pith_short_8: G3RPO4EW
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/G3RPO4EW75ZJPTNBY45CFQIFKD \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 36e2f77096ff7297cda1c73a22c10550df3eef12f83c6dc2ac9cd3179d5d6437
Canonical record JSON
{
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    "abstract_canon_sha256": "80937a264f776a71d0908a4c1b8e2aaa0d8b65b57033dd5834526d08a5ee610c",
    "cross_cats_sorted": [
      "cs.CV"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-05-26T08:21:46Z",
    "title_canon_sha256": "061a781c0d1f2abecb28708a3c1c766d094299596be3fb43553a38286a2fce65"
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  "source": {
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    "kind": "arxiv",
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}