@inproceedings{yan-etal-2026-pyravid,
title = "{P}yra{V}id: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning",
author = "Yan, Sikuan and
Dong, Sicheng and
Wang, Haotong and
Nie, Ercong and
Liu, Yilun and
Bi, Jinhe and
Xu, Yingjie and
Schwarzmann, Susanna and
Trivisonno, Riccardo and
Tresp, Volker and
Ma, Yunpu",
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.777/",
pages = "18065--18084",
ISBN = "979-8-89176-459-0",
abstract = "Memory has become an increasingly important component of agentic systems, as these systems are expected to reason over long-term experience. However, prior work has largely focused on unimodal memory, leaving multimodal memory relatively underexplored despite its central role in real-world applications. Compared with unimodal settings, multimodal memory introduces additional challenges, including heterogeneous input integration, person-centric information alignment, and evidence aggregation across different granularities. We present PyraVid, a hierarchical multimodal memory framework inspired by Event Segmentation Theory from cognitive science. PyraVid organizes long videos into a coarse-to-fine pyramid structure, enabling structured memory access and effective evidence aggregation. It further supports structure-guided memory expansion with pruning, allowing the retrieval of related events with strong causal connectivity but low semantic similarity while reducing noise. Experiments on multiple long-video understanding benchmarks show that PyraVid consistently improves performance across datasets, model scales, and question types, highlighting the effectiveness of hierarchical multimodal memory for long-horizon reasoning."
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%0 Conference Proceedings
%T PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning
%A Yan, Sikuan
%A Dong, Sicheng
%A Wang, Haotong
%A Nie, Ercong
%A Liu, Yilun
%A Bi, Jinhe
%A Xu, Yingjie
%A Schwarzmann, Susanna
%A Trivisonno, Riccardo
%A Tresp, Volker
%A Ma, Yunpu
%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 yan-etal-2026-pyravid
%X Memory has become an increasingly important component of agentic systems, as these systems are expected to reason over long-term experience. However, prior work has largely focused on unimodal memory, leaving multimodal memory relatively underexplored despite its central role in real-world applications. Compared with unimodal settings, multimodal memory introduces additional challenges, including heterogeneous input integration, person-centric information alignment, and evidence aggregation across different granularities. We present PyraVid, a hierarchical multimodal memory framework inspired by Event Segmentation Theory from cognitive science. PyraVid organizes long videos into a coarse-to-fine pyramid structure, enabling structured memory access and effective evidence aggregation. It further supports structure-guided memory expansion with pruning, allowing the retrieval of related events with strong causal connectivity but low semantic similarity while reducing noise. Experiments on multiple long-video understanding benchmarks show that PyraVid consistently improves performance across datasets, model scales, and question types, highlighting the effectiveness of hierarchical multimodal memory for long-horizon reasoning.
%U https://aclanthology.org/2026.emnlp-main.777/
%P 18065-18084
Markdown (Informal)
[PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning](https://aclanthology.org/2026.emnlp-main.777/) (Yan et al., EMNLP 2026)
ACL
- Sikuan Yan, Sicheng Dong, Haotong Wang, Ercong Nie, Yilun Liu, Jinhe Bi, Yingjie Xu, Susanna Schwarzmann, Riccardo Trivisonno, Volker Tresp, and Yunpu Ma. 2026. PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 18065–18084, Budapest, Hungary. Association for Computational Linguistics.