FactoryBench: Evaluating Industrial Machine Understanding

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.

Oral at PUDM, NeurIPS 2026. Code: this https URL

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

cs.AI (primary), cs.RO, cs.LG, cs.CL

arXiv:2605.07675 [cs.AI]

(or arXiv:2605.07675v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.07675 Focus to learn more arXiv-issued DOI via DataCite

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