@inproceedings{lang-etal-2026-longspace,
title = "{L}ong{S}pace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video",
author = "Lang, Shiqiang and
Liu, Jing and
He, Haoyang and
Sun, Peiwen and
Chen, Yuanteng and
Liu, Tao and
Yang, Lan and
Guo, Longteng and
Zhang, Honggang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.227/",
pages = "5239--5256",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon tasks such as autonomous driving and robotic navigation require more than recognizing the current view, as models must remember and retrieve previously observed spatial layouts, routes, viewpoint changes, and object states. To evaluate this capability, we introduce LongSpace-Bench, a room-tour video benchmark for long-horizon spatial memory, covering scene perception, spatial relations, and spatial memory. In this work, we further propose LongSpace, a memory framework for long-video spatial reasoning. LongSpace models long videos as sequential chunks, incorporates 3D structural cues into early decoder layers, and constructs layer-aware memory for question-guided retrieval. Experiments on multiple spatial reasoning benchmarks show that LongSpace improves long-video spatial understanding, further demonstrating explicit spatial memory as a key capability for long-horizon video MLLMs."
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<abstract>Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon tasks such as autonomous driving and robotic navigation require more than recognizing the current view, as models must remember and retrieve previously observed spatial layouts, routes, viewpoint changes, and object states. To evaluate this capability, we introduce LongSpace-Bench, a room-tour video benchmark for long-horizon spatial memory, covering scene perception, spatial relations, and spatial memory. In this work, we further propose LongSpace, a memory framework for long-video spatial reasoning. LongSpace models long videos as sequential chunks, incorporates 3D structural cues into early decoder layers, and constructs layer-aware memory for question-guided retrieval. Experiments on multiple spatial reasoning benchmarks show that LongSpace improves long-video spatial understanding, further demonstrating explicit spatial memory as a key capability for long-horizon video MLLMs.</abstract>
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%0 Conference Proceedings
%T LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video
%A Lang, Shiqiang
%A Liu, Jing
%A He, Haoyang
%A Sun, Peiwen
%A Chen, Yuanteng
%A Liu, Tao
%A Yang, Lan
%A Guo, Longteng
%A Zhang, Honggang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lang-etal-2026-longspace
%X Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon tasks such as autonomous driving and robotic navigation require more than recognizing the current view, as models must remember and retrieve previously observed spatial layouts, routes, viewpoint changes, and object states. To evaluate this capability, we introduce LongSpace-Bench, a room-tour video benchmark for long-horizon spatial memory, covering scene perception, spatial relations, and spatial memory. In this work, we further propose LongSpace, a memory framework for long-video spatial reasoning. LongSpace models long videos as sequential chunks, incorporates 3D structural cues into early decoder layers, and constructs layer-aware memory for question-guided retrieval. Experiments on multiple spatial reasoning benchmarks show that LongSpace improves long-video spatial understanding, further demonstrating explicit spatial memory as a key capability for long-horizon video MLLMs.
%U https://aclanthology.org/2026.emnlp-main.227/
%P 5239-5256
Markdown (Informal)
[LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video](https://aclanthology.org/2026.emnlp-main.227/) (Lang et al., EMNLP 2026)
ACL
- Shiqiang Lang, Jing Liu, Haoyang He, Peiwen Sun, Yuanteng Chen, Tao Liu, Lan Yang, Longteng Guo, and Honggang Zhang. 2026. LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5239–5256, Budapest, Hungary. Association for Computational Linguistics.