Aionoscope shows that time-series representations recover coarse signal types reliably but expose dense latent states like phase and amplitude much less reliably, with best dense-probe R² at 0.689 versus oracle 0.999.
48550/arXiv.2508.02879
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
ReGeN decomposes references into periodic, stochastic, and causal components to generate synthetic multivariate time series that preserve domain structure and support improved forecasting in low-data settings.
CausalTimePrior generates synthetic temporal structural causal models with paired observational and interventional time series to train prior-data fitted networks for in-context causal effect estimation on held-out data.
LeNEPA proposes a no-augmentation next-latent prediction recipe that maintains frozen-probe performance across ECG and synthetic diagnostic time-series datasets under fixed-recipe conditions where a tuned JEPA baseline degrades.
citing papers explorer
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Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations
Aionoscope shows that time-series representations recover coarse signal types reliably but expose dense latent states like phase and amplitude much less reliably, with best dense-probe R² at 0.689 versus oracle 0.999.
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REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
ReGeN decomposes references into periodic, stochastic, and causal components to generate synthetic multivariate time series that preserve domain structure and support improved forecasting in low-data settings.
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Interventional Time Series Priors for Causal Foundation Models
CausalTimePrior generates synthetic temporal structural causal models with paired observational and interventional time series to train prior-data fitted networks for in-context causal effect estimation on held-out data.
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LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
LeNEPA proposes a no-augmentation next-latent prediction recipe that maintains frozen-probe performance across ECG and synthetic diagnostic time-series datasets under fixed-recipe conditions where a tuned JEPA baseline degrades.