Researchers propose recurrent architectures to improve transformer state tracking

A new paper proposes that the feedforward architecture of Transformers fundamentally limits their ability to dynamically track evolving states. The authors argue that this limitation forces state representations deeper into the model, eventually exhausting its depth and leading to information inaccessibility. They suggest that recurrent architectures, rather than explicit thought traces, are necessary for temporally extended cognition and propose a taxonomy of recurrent transformer architectures to address this issue. AI

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IMPACT Suggests a potential architectural shift for future foundation models to improve state tracking capabilities.

RANK_REASON Academic paper discussing limitations of current transformer architectures and proposing new directions.

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• Transformers
• Michael Mozer
• arXiv

• paper
• other