@inproceedings{dong-etal-2026-evaluating,
title = "Evaluating Time Awareness and Cross-modal Active Perception of Large Models via 4{D} Escape Room Task",
author = "Dong, Yurui and
Wang, Ziyue and
Liu, Dairu and
Lu, Shuyun and
Liu, Xuechen and
Luo, Fuwen and
Li, Peng and
Liu, Yang",
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.2232/",
pages = "50801--50830",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal Large Language Models (MLLMs) have made rapid progress toward unified Omni models that integrate language, vision, and audio. However, existing environments largely focus on 2D or 3D visual context and vision-language tasks, offering limited support for temporally dependent auditory signals and selective cross-modal integration, where different modalities may provide complementary or interfering information, which are essential capabilities for realistic multimodal reasoning. As a result, whether models can actively coordinate modalities and reason under time-varying, irreversible conditions remains underexplored. To this end, we introduce \textbf{EscapeCraft-4D}, a customizable 4D environment for assessing selective cross-modal perception and time awareness in Omni models. It incorporates trigger-based auditory sources, temporally transient evidence, and location-dependent cues, requiring agents to perform spatio-temporal reasoning and proactive multimodal integration under time constraints. Building on this environment, we curate a benchmark to evaluate corresponding abilities across powerful models. Evaluation results suggest that models struggle with modality bias, and reveal significant gaps in current model{'}s ability to integrate multiple modalities under time constraints. Further in-depth analysis uncovers how multiple modalities interact and jointly influence model decisions in complex multimodal reasoning environments."
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<abstract>Multimodal Large Language Models (MLLMs) have made rapid progress toward unified Omni models that integrate language, vision, and audio. However, existing environments largely focus on 2D or 3D visual context and vision-language tasks, offering limited support for temporally dependent auditory signals and selective cross-modal integration, where different modalities may provide complementary or interfering information, which are essential capabilities for realistic multimodal reasoning. As a result, whether models can actively coordinate modalities and reason under time-varying, irreversible conditions remains underexplored. To this end, we introduce EscapeCraft-4D, a customizable 4D environment for assessing selective cross-modal perception and time awareness in Omni models. It incorporates trigger-based auditory sources, temporally transient evidence, and location-dependent cues, requiring agents to perform spatio-temporal reasoning and proactive multimodal integration under time constraints. Building on this environment, we curate a benchmark to evaluate corresponding abilities across powerful models. Evaluation results suggest that models struggle with modality bias, and reveal significant gaps in current model’s ability to integrate multiple modalities under time constraints. Further in-depth analysis uncovers how multiple modalities interact and jointly influence model decisions in complex multimodal reasoning environments.</abstract>
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%0 Conference Proceedings
%T Evaluating Time Awareness and Cross-modal Active Perception of Large Models via 4D Escape Room Task
%A Dong, Yurui
%A Wang, Ziyue
%A Liu, Dairu
%A Lu, Shuyun
%A Liu, Xuechen
%A Luo, Fuwen
%A Li, Peng
%A Liu, Yang
%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 dong-etal-2026-evaluating
%X Multimodal Large Language Models (MLLMs) have made rapid progress toward unified Omni models that integrate language, vision, and audio. However, existing environments largely focus on 2D or 3D visual context and vision-language tasks, offering limited support for temporally dependent auditory signals and selective cross-modal integration, where different modalities may provide complementary or interfering information, which are essential capabilities for realistic multimodal reasoning. As a result, whether models can actively coordinate modalities and reason under time-varying, irreversible conditions remains underexplored. To this end, we introduce EscapeCraft-4D, a customizable 4D environment for assessing selective cross-modal perception and time awareness in Omni models. It incorporates trigger-based auditory sources, temporally transient evidence, and location-dependent cues, requiring agents to perform spatio-temporal reasoning and proactive multimodal integration under time constraints. Building on this environment, we curate a benchmark to evaluate corresponding abilities across powerful models. Evaluation results suggest that models struggle with modality bias, and reveal significant gaps in current model’s ability to integrate multiple modalities under time constraints. Further in-depth analysis uncovers how multiple modalities interact and jointly influence model decisions in complex multimodal reasoning environments.
%U https://aclanthology.org/2026.emnlp-main.2232/
%P 50801-50830
Markdown (Informal)
[Evaluating Time Awareness and Cross-modal Active Perception of Large Models via 4D Escape Room Task](https://aclanthology.org/2026.emnlp-main.2232/) (Dong et al., EMNLP 2026)
ACL
- Yurui Dong, Ziyue Wang, Dairu Liu, Shuyun Lu, Xuechen Liu, Fuwen Luo, Peng Li, and Yang Liu. 2026. Evaluating Time Awareness and Cross-modal Active Perception of Large Models via 4D Escape Room Task. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50801–50830, Budapest, Hungary. Association for Computational Linguistics.