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arXiv · 2605.07675

FactoryBench: Evaluating Industrial Machine Understanding

Yanis Merzouki·Coral Izquierdo·Matei Ignuta-Ciuncanu·Marcos Gomez-Bracamonte·Riccardo Maggioni·Alessandro Lombardi·Camilla Mazzoleni·Federico Martelli·Balazs Gunther·Jonas Petersen·Philipp Petersen
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Abstract

We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs are organized along four causal levels (state, intervention, counterfactual, decision) instantiating Pearl's ladder of causation, and span five answer formats: four structured formats are scored deterministically and free-form answers are scored by an LLM-as-judge voting protocol. We propose a scalable Q&A generation framework built around structured question templates, present FactoryWave (a dense, multitask, multivariate sensor dataset collected from a UR3 cobot and a KUKA KR10 industrial arm), and construct FactoryBench as a large-scale benchmark of over 70k Q&A items grounded in roughly 15k normalized episodes from FactoryWave, AURSAD, and voraus-AD. Zero-shot evaluation of six frontier LLMs shows that no model exceeds 50% on structured levels or 18% on decision-making, revealing a wide gap between current models and operational machine understanding.

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Yanis Merzouki, Coral Izquierdo, Matei Ignuta-Ciuncanu, Marcos Gomez-Bracamonte, Riccardo Maggioni, Alessandro Lombardi, Camilla Mazzoleni, Federico Martelli, Balazs Gunther, Jonas Petersen, Philipp Petersen. 2026-05-08. FactoryBench: Evaluating Industrial Machine Understanding. https://arxiv.org/abs/2605.07675

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