HEPA pretrains via horizon-conditioned JEPA on unlabeled data then fine-tunes only the predictor for event survival CDFs, outperforming PatchTST, iTransformer, MAE and Chronos-2 on at least 10 of 14 benchmarks with fixed hyperparameters, an order of magnitude fewer tuned parameters and less labeled
FactoryBench: Evaluating Industrial Machine Understanding
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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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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
HEPA pretrains via horizon-conditioned JEPA on unlabeled data then fine-tunes only the predictor for event survival CDFs, outperforming PatchTST, iTransformer, MAE and Chronos-2 on at least 10 of 14 benchmarks with fixed hyperparameters, an order of magnitude fewer tuned parameters and less labeled