The paper introduces CTAN, a Cycle-Temporal Attention Network for audio‑visual embodied navigation. It proposes an Audio‑Visual Reconstruction Cross‑Attention module that uses bidirectional cycle‑consistency to strengthen spatial semantics across visual and acoustic modalities, and a Temporal Cross‑Modal Memory to fuse real‑time multimodal features with historical context. Experiments on Replica and Matterport3D show that CTAN outperforms prior methods in success rate, SPL, and scene navigation accuracy.
By Teng Liu, Yinfeng Yu
arXiv:2610.14077v1 Announce Type: cross
Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has...
By Jia Li, Yinfeng Yu
arXiv:2610.15956v1 Announce Type: new
Abstract: Vision-language-action models for embodied navigation typically rely on RGB observations, limiting their applicability in lights-out industrial environ...
By Wenjun Huang, Shenghao Fu, Yian Jin, Yang Ni, Yifan Li, Lichi Li, Xingjian Diao, Pengfei Zhang, Yutong Song, Hanning Chen, Jiarui Cui, Tianyu Yang, Shilong Li, Yu Kong, Mohsen Imani
BinauralVAE is an open‑source pipeline that reconstructs spatial audio using various Variational Autoencoder architectures, including complex‑valued variants, to learn latent representations of binaural signals. The project builds on realistic acoustic data from a simulated robot navigating an environment, providing a foundation for audio‑centric world models. It aims to map the causal link between navigational actions and their acoustic outcomes, positioning sound as a complementary modality for spatial awareness.
By Luis Vitor Zerkowski, Luiz Velho
arXiv:2609.23407v1 Announce Type: cross
Abstract: Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for em...
By Ruixun Liu, Yuxuan Wang, Jiacheng Xie, Yuhuan You, Donghua Cai, Junming Lin, Xiong-Hui Chen, Zhifang Guo, Yunfei Chu, Qize Yang, Xize Cheng, Jin Xu, Yiwu Zhong
arXiv:2609.23407v2 Announce Type: replace-cross
Abstract: Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challengin...
By Ruixun Liu, Yuxuan Wang, Jiacheng Xie, Yuhuan You, Donghua Cai, Junming Lin, Xiong-Hui Chen, Zhifang Guo, Yunfei Chu, Qize Yang, Xize Cheng, Jin Xu, Yiwu Zhong
arXiv:2605. 06317v4 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulation and limits efficiency.
By Dijia Zhan, Jinyi Li, Chenxi Zheng, Shaoyu Huang, Yong Li, Jie Tang, Xuemiao Xu
arXiv:2607. 13110v1 Announce Type: cross 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.
By Yi Wang, Yinfeng Yu
The fourth Perception Test challenge was held as a workshop at ECCV 2026 in Malmö, Sweden, focusing on spatial intelligence. It included four tracks: unified multiple-choice videoQA, grounded videoQA from the original benchmark, and two new city-scale walking-tour tracks—KilometerAudio and KilometerVision. The report details the new benchmarks and summarizes the winning solutions, noting that while expensive agentic pipelines succeeded in the city-scale tracks, standalone multimodal models still struggle with complex spatial and multimodal reasoning.
By Fedor Kitashov, Jo\~ao Carreira, Shiry Ginosar, Dima Damen, Andrew Zisserman, Viorica P\u{a}tr\u{a}ucean
FloorSAV is a framework that enhances audio‑visual large language models (AV‑LLMs) by embedding a dynamic 2D floormap derived from 3D point clouds, camera trajectories, spatial audio cues, and object landmarks. The floormap is fed into the AV‑LLM as a synchronized stream with egocentric video, enabling the model to jointly reason over visual, auditory, and geometric information in a single inference. The authors also introduce SAVED‑Bench, a benchmark for spatial reasoning tasks, and show that FloorSAV improves AV‑LLM performance on both SAVED‑Bench and SAVVY‑Bench, with ground‑truth floormaps highlighting its potential.
By Kyeong-Rae Kim, Sungnyun Kim, Tae-Hyun Oh
arXiv:2603.16086v2 Announce Type: replace-cross
Abstract: While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts...
By Chang Nie, Tianchen Deng, Guangming Wang, Zhe Liu, Hesheng Wang
arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.
By Kailing Li, Tianwen Qian, Lijin Yang, Yuqian Fu, Jingyu Gong, Xiaoling Wang, Liang He