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Life sciences · Preprint
arXiv · September 8, 2026
Raises a question worth testing. It does not answer one.
This preprint proposes Credit Stabilization through Time (CST), a method to rescale state-credit signals during backpropagation to improve recurrent model generalization beyond training length. The work is mechanistic and exploratory, testing a novel hypothesis about why recurrent models fail at long sequences; it does not yet represent a validated or established advance, and has not been peer reviewed.
Mechanistic exploration with synthetic and empirical validation. Intervention: Credit Stabilization through Time (CST): local rescaling of state-credit signal norm during backward propagation, specialized to synthetic and real-data regimes..
CST improves recurrent model performance beyond the training horizon Gains observed at up to 128x the training length Controlled synthetic tasks and real data exhibit different credit dynamics, prompting regime-specific CST specialization
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This is a preprint presenting a novel mechanistic hypothesis about recurrent neural network training, supported by controlled experiments and synthetic tasks, but lacks clinical or established-domain validation and has not undergone peer review.
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Recurrent models provide a natural path to long-context modeling, yet models trained with backpropagation through time (BPTT) often fail beyond their training horizon. Classical analyses emphasize gradients that vanish or explode along temporal paths. However, dense per-token losses can still train a shared recurrent rule despite severe decay, showing that decay alone does not determine whether learning fails. We instead study state credit: the signal through which future losses reach earlier recurrent states before contributing to parameter updates. Accordingly, we intervene directly on state credit and propose Credit Stabilization through Time (CST). During backward propagation, CST locally rescales the state-credit signal to stabilize its norm without rotating the component being corrected, while leaving the forward computation unchanged. Because controlled synthetic tasks and real data exhibit different credit dynamics, we specialize CST to each regime. In both settings, CST improves performance beyond the training horizon, with gains observed at up to 128x the training length.
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