@inproceedings{kim-etal-2026-omni,
title = "Can Omni-models Imagine the Future? A Diagnostic Benchmark for Multimodal World Modeling",
author = "Kim, Junyeob and
Kim, Jihwan and
Jhang, Minha and
Jeon, Sungwook and
Jo, Hwiyeol and
Kim, Jeonghoon and
Lee, Sang-goo and
Lee, Joosung",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2199/",
pages = "47099--47115",
ISBN = "979-8-89176-462-0",
abstract = "Recent progress in Omni-models has yielded strong performance on benchmarks focused on static observations. However, their ability to maintain spatiotemporal continuity when predicting coherent future states from previous context remains underexplored. We address this by formulating Next Scene Prediction (NSP) as a diagnostic task for assessing multimodal near-future state prediction, requiring models to anticipate the immediate future from a given context. We introduce OmniNSP, a benchmark evaluating causal and cross-modal coherence across physical, logical, and social dynamics. Unlike prior tasks that often rely on textual or discriminative proxies, OmniNSP asks models to express predicted future states through text-mediated visual and speech synthesis. Our analysis shows that current Omni-models can produce plausible textual forecasts while still failing to preserve future-state consistency in generated outputs. A frozen-generator control substantially narrows cross-model visual-consistency gaps, attributing much of the original system-level difference to model-specific realization stacks rather than textual foresight alone. A modality-input ablation further shows that audio provides complementary predictive cues beyond vision, with the largest gains for logical transitions and contexts with informative speech. These results position OmniNSP as a diagnostic of both near-future prediction and the preservation of predicted state through multimodal realization."
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<abstract>Recent progress in Omni-models has yielded strong performance on benchmarks focused on static observations. However, their ability to maintain spatiotemporal continuity when predicting coherent future states from previous context remains underexplored. We address this by formulating Next Scene Prediction (NSP) as a diagnostic task for assessing multimodal near-future state prediction, requiring models to anticipate the immediate future from a given context. We introduce OmniNSP, a benchmark evaluating causal and cross-modal coherence across physical, logical, and social dynamics. Unlike prior tasks that often rely on textual or discriminative proxies, OmniNSP asks models to express predicted future states through text-mediated visual and speech synthesis. Our analysis shows that current Omni-models can produce plausible textual forecasts while still failing to preserve future-state consistency in generated outputs. A frozen-generator control substantially narrows cross-model visual-consistency gaps, attributing much of the original system-level difference to model-specific realization stacks rather than textual foresight alone. A modality-input ablation further shows that audio provides complementary predictive cues beyond vision, with the largest gains for logical transitions and contexts with informative speech. These results position OmniNSP as a diagnostic of both near-future prediction and the preservation of predicted state through multimodal realization.</abstract>
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%0 Conference Proceedings
%T Can Omni-models Imagine the Future? A Diagnostic Benchmark for Multimodal World Modeling
%A Kim, Junyeob
%A Kim, Jihwan
%A Jhang, Minha
%A Jeon, Sungwook
%A Jo, Hwiyeol
%A Kim, Jeonghoon
%A Lee, Sang-goo
%A Lee, Joosung
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F kim-etal-2026-omni
%X Recent progress in Omni-models has yielded strong performance on benchmarks focused on static observations. However, their ability to maintain spatiotemporal continuity when predicting coherent future states from previous context remains underexplored. We address this by formulating Next Scene Prediction (NSP) as a diagnostic task for assessing multimodal near-future state prediction, requiring models to anticipate the immediate future from a given context. We introduce OmniNSP, a benchmark evaluating causal and cross-modal coherence across physical, logical, and social dynamics. Unlike prior tasks that often rely on textual or discriminative proxies, OmniNSP asks models to express predicted future states through text-mediated visual and speech synthesis. Our analysis shows that current Omni-models can produce plausible textual forecasts while still failing to preserve future-state consistency in generated outputs. A frozen-generator control substantially narrows cross-model visual-consistency gaps, attributing much of the original system-level difference to model-specific realization stacks rather than textual foresight alone. A modality-input ablation further shows that audio provides complementary predictive cues beyond vision, with the largest gains for logical transitions and contexts with informative speech. These results position OmniNSP as a diagnostic of both near-future prediction and the preservation of predicted state through multimodal realization.
%U https://aclanthology.org/2026.findings-emnlp.2199/
%P 47099-47115
Markdown (Informal)
[Can Omni-models Imagine the Future? A Diagnostic Benchmark for Multimodal World Modeling](https://aclanthology.org/2026.findings-emnlp.2199/) (Kim et al., Findings 2026)
ACL
- Junyeob Kim, Jihwan Kim, Minha Jhang, Sungwook Jeon, Hwiyeol Jo, Jeonghoon Kim, Sang-goo Lee, and Joosung Lee. 2026. Can Omni-models Imagine the Future? A Diagnostic Benchmark for Multimodal World Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47099–47115, Budapest, Hungary. Association for Computational Linguistics.