smgjch/meow-10k

Meow-10K是一个高保真、同步的四模态数据集，包含10,000个猫的样本，是**Meow-Omni 1**的主要训练语料库，旨在促进计算行为学中的深度意图推理。该数据集为多模态大型语言模型（MLLMs）提供了第一个大规模的训练基础，以学习外部行为与内部生理状态之间的因果关系。通过整合高频生物时间序列（ECG/EEG/IMU）和传统的视听流，数据集允许模型解决“语义混淆”问题，即相同的物理动作掩盖了不同的内部意图。

Meow-10K is a high-fidelity, synchronized quad-modal dataset comprising 10,000 feline samples. It is the primary training corpus for **Meow-Omni 1**, designed to facilitate deep intention reasoning in computational ethology. The dataset provides the first large-scale training foundation for Multimodal Large Language Models (MLLMs) to learn the causal relationships between external behaviours and internal physiological states. By integrating high-frequency biological time-series (ECG/EEG/IMU) with traditional audio-visual streams, the dataset allows models to resolve "semantic aliasing"—where identical physical actions mask different internal intents.