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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed preprint proposing a computational solution to instability in shared attention vectors under multi-outcome learning in artificial neural networks. The work derives conditions for failure of shared vectors and presents three synthetic experiments showing the proposed outcome-indexed attention matrices converge to meaningful representations where shared vectors do not. No clinical, human, or real-world empirical data are presented.
Preprint.
Shared attention vectors collapse to bounds under multi-outcome learning, preventing meaningful attentional tuning. Outcome-indexed attentional matrices converge to meaningful representations in three synthetic experiments. Analytical conditions derived for when shared vector instability occurs.
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This is an unreviewed preprint describing a theoretical problem in machine learning and proposing a computational fix, with synthetic validation but no clinical or empirical human data.
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Dimensional attention in learning is often implemented as a globally shared attention vector, where each stimulus dimension corresponds to a single scalar. These scalars are learned by models through gradient-descent on error, where predictive features acquire more salience. We show that under multi-outcome learning, where models predict more than one outcome, this shared vector becomes unstable; it collapses to its bounds and prevents the models from learning meaningful attentional tunings for learning and generalization. We address this by introducing an outcome-indexed attentional matrix that converts globally shared attentional tuning into an outcome-indexed representation. We present an analysis of the unstable shared vectors and derive the conditions under which it holds. Empirically, three synthetic experiments benchmark the proposed attention matrices and show that they converge to meaningful representations, something shared attention vectors fail to do. These results suggest that outcome-indexed attentional matrices are a general fix for gradient-based attentional processes, which improves models of learning under multi-outcome conditions.
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