[2605.09152] Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology

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Computer Science > Computation and Language

arXiv:2605.09152 (cs)

[Submitted on 9 May 2026]

Title:Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology

Authors:Jucheng Hu, Zhangquan Chen, Yulin Chen, Chengjie Hong, Liang Zhou, Tairan Wang, Sifei Li, Giulio Zhu, Feng Zhou, Yiheng Zeng, Suorong Yang, Dongzhan Zhou<br>View a PDF of the paper titled Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology, by Jucheng Hu and 11 other authors

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Abstract:Deciphering animal intent is a fundamental challenge in computational ethology, largely because of semantic aliasing, the phenomenon where identical external signals (e.g., a cat's purr) correspond to radically different internal states depending on physiological context. Existing Multimodal Large Language Models (MLLMs) are blind to high-frequency biological time-series data, restricting them to superficial behavioural pattern matching rather than genuine latent-state reasoning. To bridge this gap, we introduce Meow-Omni 1, the first open-source, quad-modal MLLM purpose-built for computational ethology. It natively fuses video, audio, and physiological time-series streams with textual reasoning. Through targeted architectural adaptation, we integrate specialized scientific encoders into a unified backbone and formalize intent inference via physiologically grounded cross-modal alignment. Evaluated on MeowBench, a novel, expert-verified quad-modal benchmark, Meow-Omni 1 achieves state-of-the-art intent-recognition accuracy (71.16%), substantially outperforming leading vision-language and omni-modal baselines. We release the complete open-source pipeline including model weights, training framework, and the Meow-10K dataset, to establish a scalable paradigm for inter-species intent understanding and to advance foundation models toward real-world veterinary diagnostics and wildlife conservation.

Subjects:

Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC)

Cite as:<br>arXiv:2605.09152 [cs.CL]

(or<br>arXiv:2605.09152v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.09152

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arXiv-issued DOI via DataCite

Submission history<br>From: Jucheng Hu [view email]<br>[v1]<br>Sat, 9 May 2026 20:30:15 UTC (1,583 KB)

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