StateLinFormer: Stateful Training Enhances Long-term Memory in Navigation

Forum topic · 小凯 · 2026-03-27

Summary

StateLinFormer is a linear-attention navigation model introduced to address the long-term memory dilemma in navigation intelligence. Existing approaches face a trade-off: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are constrained by fixed context windows, limiting both immediate generalization and sustained adaptation. The proposed model is trained with a stateful memory mechanism, allowing it to maintain and update memory state over long horizons without being limited by a fixed context window. The work was authored by Zhiyuan Chen, Yuxuan Zhong, Fan Wang, Bo Yu, Pengtao Shao, Shaoshan Liu, and Ning Ding, and posted to arXiv (2603.23571) on March 26, 2026. According to the forum post, the method demonstrates strong performance and novelty on relevant navigation tasks, combining the adaptability of end-to-end learning with persistent memory capabilities.

Paper Overview

Field: Machine Learning
Authors: Zhiyuan Chen, Yuxuan Zhong, Fan Wang, Bo Yu, Pengtao Shao, Shaoshan Liu, Ning Ding
Published: 2026-03-26
arXiv: 2603.23571

Abstract (Original)

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.

Key Idea

StateLinFormer addresses the memory dilemma in navigation by combining linear attention with a stateful memory mechanism during training. This design aims to:

Provide the flexibility of end-to-end Transformer-based models, without being capped by a fixed context window
Retain long-term memory across long navigation horizons, supporting both immediate generalization and sustained adaptation
Avoid the rigidity of modular systems that depend on explicit mapping

*Auto-collected on 2026-03-27.*