A Hybrid Mamba for Audio-Visual Navigation
We propose Samba, a hybrid Mamba architecture for audio-visual navigation. It uses an adaptive selection-enabled Mamba State Encoder (M-SE) to replace conventional GRUs for temporal aggregation, and an Audio Mamba Encoder (AME) to address limitations of convolutional operators in capturing global time-frequency dependencies in spectrograms. Experiments show an 11.3% improvement in success rate on Matterport3D and even better performance on Replica, with lower computational cost. Accepted at IEEE SMC 2026.
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[Submitted on 14 Jul 2026]
Title:A Hybrid Mamba for Audio-Visual Navigation
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Abstract:Since the paradigm centered on convolutional neural networks and recurrent architectures was established in 2020, the fundamental backbone networks for audio-visual navigation have undergone no essential changes for more than five years, making them inadequate to support efficient representation of dynamic multimodal sequences. This paper proposes Samba(A Hybrid Mamba for Audio-Visual Navigation). It uses the adaptive selection-enabled Mamba State Encoder (M-SE) to replace conventional GRUs for temporal aggregation, and constructs an Audio Mamba Encoder (AME) to remedy the limitations of convolutional operators in capturing global time-frequency dependencies in spectrograms. Experiments demonstrate that Samba exhibits exceptional generalization performance when facing unheard sound sources and unseen scenes. On the Matterport3D dataset, it improves the navigation success rate (SR) by 11.3\% compared with existing state-of-the-art models, and the performance gain is even more pronounced on the Replica dataset, which features finer scene structures. Such modernized architectural reconstruction unlocks stronger embodied representation capabilities at a lower computational cost, thereby providing a highly robust technical pathway for paradigm evolution in the field of audio-visual navigation.
Comments: Main paper (6 pages). Accepted for publication by IEEE International Conference on Systems and Man and Cybernetics 2026 (IEEE SMC 2026)
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
Cite as: arXiv:2607.13110 [cs.LG]
(or arXiv:2607.13110v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.13110
arXiv-issued DOI via DataCite
Submission history
From: Yinfeng Yu [view email] [v1] Tue, 14 Jul 2026 11:55:42 UTC (1,878 KB)
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