Dataset Card for MeowBench

MeowBench is a high-fidelity, expert-verified quad-modal benchmark designed to evaluate Multimodal Large Language Models (MLLMs) on feline intention decoding. It is the official evaluation suite for the Meow-Omni 1 model.

Dataset Summary

MeowBench is designed to solve the challenge of "semantic aliasing" in animal behaviour. It provides a rigorous testing ground for models to determine if they can move beyond superficial pattern matching to genuine latent state reasoning by correlating external observational data (video/audio) with internal biological markers (ECG/EEG/IMU).

Uses

Direct Use

• Benchmarking Multimodal Large Language Models on animal behaviour interpretation.
• Evaluating a model's ability to ingest and reason over video, audio, and high-frequency biological time-series data.

Out-of-Scope Use

• Real-world veterinary diagnosis without human oversight.
• Direct application to non-feline species (unless testing for zero-shot transfer capabilities).

Dataset Structure

Each sample in MeowBench is structured as a Multiple Choice Question (MCQ):

• Input: A synchronized (or intent-matched) triplet of Video, Audio, and Time-Series data.
• Question: A natural language prompt asking for the animal's underlying intent (e.g., "Based on the provided biometrics and visual cues, is the subject exhibiting play-aggression or predatory intent?").
• Options: One ground-truth intention label and three expert-curated distractors sampled from the broader intention collection.

Construction & Verification

1. Next-Behaviour Prediction (NBP) Logic: Intent labels are derived from the behaviour immediately following a temporal transition point in the raw data.
2. Intent-Matched Synthesis: Due to the scarcity of naturally synchronized quad-modal data, samples were synthesized by matching unimodal data sharing the same intent.
3. Ethologist Audit: To ensure biological and biomechanical plausibility, eight Professional Feline Ethologists manually reviewed every sample. Only samples where the biometric acceleration and heart-rate patterns were consistent with the audio-visual displays were retained.

🔗 The Meow-Omni Ecosystem

To facilitate reproducibility and further research in computational ethology, we have released the following components:

• Main Model: Meow-Omni 1 — The full fine-tuned quad-modal MLLM.
• Base Model: Meow-Omni 1-Base — The model weights prior to specific intent-alignment.
• Training Dataset: Meow-10K — The synchronized 10k sample dataset used for training.

Citation

Coming Soon.