English(EN) Rethinking State Tracking in Recurrent Models Through Error Control Dynamics

循环模型因误差动力学而在状态跟踪方面失败

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 13:59

研究人员引入了一种关于循环神经网络架构中状态跟踪的新视角，强调误差控制动力学而非理论表达能力。他们证明了仿射循环网络（包括状态空间模型和线性注意力）由于无法在状态分离子空间上纠正误差，因此在鲁棒状态跟踪方面存在困难。这种限制导致了由累积误差决定的有限视界解决方案，并且随着可区分性比率跨越临界阈值，跟踪精度会可预测地下降。 AI

影响引入了一个新的理论框架来理解循环模型状态跟踪的局限性，可能指导未来的架构设计。

排序理由阐述模型能力新理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Seon Joo Kim · 2026-05-08 13:59

通过误差控制动力学重新思考循环模型中的状态跟踪

The theory of state tracking in recurrent architectures has predominantly focused on expressive capacity: whether a fixed architecture can theoretically realize a set of symbolic transition rules. We argue that equally important is error control, the dynamics governing hidden-sta…

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通过误差控制动力学重新思考循环模型中的状态跟踪

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