State Propagation Also Satisfies: A Complex-Valued State-Space Model for Deterministic State Tracking 文章

ArXiv CS.AI2026-08-05PAPERen作者: Xiaohe Li, Yang Lu

详细信息

来源站点
ArXiv CS.AI
作者
Xiaohe Li, Yang Lu
文章类型
PAPER
语言
en
发布日期
2026-08-05

摘要

arXiv:2608.03425v1 Announce Type: new Abstract: Transformer-based architectures have dominated sequence modeling, largely due to the expressive power of attention mechanisms. However, for a class of deterministic state tracking tasks---such as parity checking, modular counting, and parenthesis matching---attention may be overkill. In this paper, we show that \textbf{state propagation alone is sufficient}. We propose the \textbf{Complex State Propagator (CSP)}, a minimalistic recurrent architecture that \textbf{only propagates hidden states} across layers without output projections at intermediate steps. The state is represented as a complex-valued vector, updated via input-dependent rotations in the complex domain. To enable deep propagation without gradient vanishing or degradation, we introduce a \textbf{block-level skip connection} alongside element-wise complex normalization and SiLU activation at sequence boundaries.

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