StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation

Forum topic · 小凯 · 2026-03-27

Summary

StateLinFormer is a linear-attention navigation model introduced by Zhiyuan Chen, Yuxuan Zhong, Fan Wang, Bo Yu, Pengtao Shao, Shaoshan Liu, and Ning Ding, published on arXiv (2603.23571) in March 2026. The work addresses a core dilemma in navigation intelligence: effective navigation requires long-term memory to support both immediate generalization and sustained adaptation, but existing approaches fall short in different ways. Modular systems rely on explicit mapping and therefore lack flexibility, while Transformer-based end-to-end models are constrained by fixed context windows, limiting how much past experience they can retain. StateLinFormer tackles this by combining a linear-attention architecture with a stateful memory mechanism during training, enabling the model to maintain and update memory state across long horizons without the bottleneck of fixed context windows. The paper targets the machine learning for robotics/navigation community and represents an alternative design point between modular mapping pipelines and fixed-context end-to-end Transformers. Full details, experiments, and results are available in the arXiv preprint.

Paper Overview

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

Original Abstract

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 Points

Problem: Navigation agents need long-term memory for both immediate generalization and sustained adaptation over time.
Limitations of existing approaches: Modular systems depend on explicit mapping and lack flexibility; Transformer-based end-to-end models are restricted by fixed context windows.
Proposed solution: StateLinFormer, a linear-attention navigation model trained with a stateful memory mechanism.

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