pith:G3RPO4EW
FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks
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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Record completeness
Claims
Evaluation results confirmed that performance evaluation considering the characteristics of Multimodal LLM (MLLM) such as GPT-4o is feasible.
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.
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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Receipt and verification
| 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
· · · · ·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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