@inproceedings{chen-etal-2026-omnireasoner,
title = "{O}mni{R}easoner: Thinking with Long Audio-Video via Native Tool Use",
author = "Chen, Yu and
Wang, Qiufeng and
Li, Caorui and
Xiong, Ziyu and
Wang, Yi-Dong and
Gao, Mingqi and
Liu, Shuman and
Liu, Biao and
Yang, Chunfeng and
Zeng, Anxiang and
Zhang, Haibo and
Chen, Chaofan",
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.2157/",
pages = "49078--49096",
ISBN = "979-8-89176-459-0",
abstract = "Long audio-video reasoning is difficult for omni-modal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs. We introduce \textbf{OmniReasoner}, a tool-use post-training framework for \textit{Thinking with Long Audio-Video}: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering. OmniReasoner first builds a low-cost global preview of the full stream and then, when needed, calls the zoom-in tool with a requested temporal interval for higher-fidelity visual and audio inspection before answering. Because the model observes different sampling granularities before and after this call{---}a sparse global preview and a denser local clip{---}we introduce \textit{TimeAnchor}, which keeps the tool{'}s temporal argument valid and round-trip-consistent across these granularities, rather than tied to frame indices from a particular sampling rate. To make this tool-use behavior trainable without expensive manual interval annotation, we build a \textit{Temporal Augmented Data Engine} that synthesizes tool-use post-training trajectories by video editing and composition. Experiments across omni-modal and video benchmarks show that OmniReasoner improves both answer accuracy and temporal grounding while concentrating high-fidelity computation on informative regions. Code is available at \url{https://github.com/RockyChen0205/OmniReasoner}."
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<abstract>Long audio-video reasoning is difficult for omni-modal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs. We introduce OmniReasoner, a tool-use post-training framework for Thinking with Long Audio-Video: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering. OmniReasoner first builds a low-cost global preview of the full stream and then, when needed, calls the zoom-in tool with a requested temporal interval for higher-fidelity visual and audio inspection before answering. Because the model observes different sampling granularities before and after this call—a sparse global preview and a denser local clip—we introduce TimeAnchor, which keeps the tool’s temporal argument valid and round-trip-consistent across these granularities, rather than tied to frame indices from a particular sampling rate. To make this tool-use behavior trainable without expensive manual interval annotation, we build a Temporal Augmented Data Engine that synthesizes tool-use post-training trajectories by video editing and composition. Experiments across omni-modal and video benchmarks show that OmniReasoner improves both answer accuracy and temporal grounding while concentrating high-fidelity computation on informative regions. Code is available at https://github.com/RockyChen0205/OmniReasoner.</abstract>
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%0 Conference Proceedings
%T OmniReasoner: Thinking with Long Audio-Video via Native Tool Use
%A Chen, Yu
%A Wang, Qiufeng
%A Li, Caorui
%A Xiong, Ziyu
%A Wang, Yi-Dong
%A Gao, Mingqi
%A Liu, Shuman
%A Liu, Biao
%A Yang, Chunfeng
%A Zeng, Anxiang
%A Zhang, Haibo
%A Chen, Chaofan
%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 chen-etal-2026-omnireasoner
%X Long audio-video reasoning is difficult for omni-modal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs. We introduce OmniReasoner, a tool-use post-training framework for Thinking with Long Audio-Video: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering. OmniReasoner first builds a low-cost global preview of the full stream and then, when needed, calls the zoom-in tool with a requested temporal interval for higher-fidelity visual and audio inspection before answering. Because the model observes different sampling granularities before and after this call—a sparse global preview and a denser local clip—we introduce TimeAnchor, which keeps the tool’s temporal argument valid and round-trip-consistent across these granularities, rather than tied to frame indices from a particular sampling rate. To make this tool-use behavior trainable without expensive manual interval annotation, we build a Temporal Augmented Data Engine that synthesizes tool-use post-training trajectories by video editing and composition. Experiments across omni-modal and video benchmarks show that OmniReasoner improves both answer accuracy and temporal grounding while concentrating high-fidelity computation on informative regions. Code is available at https://github.com/RockyChen0205/OmniReasoner.
%U https://aclanthology.org/2026.emnlp-main.2157/
%P 49078-49096
Markdown (Informal)
[OmniReasoner: Thinking with Long Audio-Video via Native Tool Use](https://aclanthology.org/2026.emnlp-main.2157/) (Chen et al., EMNLP 2026)
ACL
- Yu Chen, Qiufeng Wang, Caorui Li, Ziyu Xiong, Yi-Dong Wang, Mingqi Gao, Shuman Liu, Biao Liu, Chunfeng Yang, Anxiang Zeng, Haibo Zhang, and Chaofan Chen. 2026. OmniReasoner: Thinking with Long Audio-Video via Native Tool Use. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49078–49096, Budapest, Hungary. Association for Computational Linguistics.