Unverified50% confidenceConceptExact time
The study proposes the temporal variability score (TVS), defined as the coefficient of variation of a knowledge item's negative log-likelihood across time-point models trained on web snapshots from multiple different years.
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50%
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Long-term
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10/28/2026
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UnverifiedOne possible explanation is that high-TVS items correspond to knowledge that appears frequently in a specific time period but is not universally present throughout, and the model may better capture these time-varying knowledge signals in the later stage of pretraining.68% similarUnverifiedAggregated benchmark scores may mask different patterns of knowledge learning, and TVS can serve as a diagnostic tool to help understand how models absorb different types of knowledge during pretraining.68% similarUnverifiedIn the early stage of training, low-TVS and high-TVS items are learned at similar speeds; as training progresses, high-TVS items improve faster.67% similarUnverifiedThe upper bound of generation quality driven by trend data depends on the timeliness of training data and the breadth of platforms covered; data lag may cause the system to chase content formats that have already fallen out of favor.59% similarUnverifiedTimesFM completed pre-training on thousands of time series datasets spanning different domains, frequencies, and patterns.59% similar
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