StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation

论文概要

研究领域: ML 作者: Zhiyuan Chen, Yuxuan Zhong, Fan Wang, Bo Yu, Pengtao Shao, Shaoshan Liu, Ning Ding 发布时间: 2026-03-26 arXiv: 2603.23571

中文摘要

本研究探索了ML领域的前沿问题。研究团队来自Zhiyuan Chen, Yuxuan Zhong等。该方法在相关任务中展现了良好的性能和创新性。

原文摘要：Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are constrained by fix...

原文摘要

Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are constrained by fixed context windows. We introduce StateLinFormer, a linear-attention navigation model trained with a stateful memory mechanism.

--- *自动采集于 2026-03-27*

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