Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.
Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam
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SGNNs pretrain neural networks on synthetic corpora from multiple mechanistic models and noise levels to enable robust forecasting and back-to-simulation attribution across epidemiology, ecology, and other fields.
Teger is a backbone-agnostic structured uncertainty module that uses discrete Forman curvature for spatial graph rewiring inside a low-rank-plus-diagonal covariance head to mitigate over-squashing and improve residual error propagation in spatio-temporal forecasting.
citing papers explorer
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Latent Chain-of-Thought Improves Structured-Data Transformers
Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.
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Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery
SGNNs pretrain neural networks on synthetic corpora from multiple mechanistic models and noise levels to enable robust forecasting and back-to-simulation attribution across epidemiology, ecology, and other fields.
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Improving Spatio-Temporal Residual Error Propagation by Mitigating Over-Squashing
Teger is a backbone-agnostic structured uncertainty module that uses discrete Forman curvature for spatial graph rewiring inside a low-rank-plus-diagonal covariance head to mitigate over-squashing and improve residual error propagation in spatio-temporal forecasting.