MTT-Bench: Predicting Social Dominance in Mice via Multimodal Large Language Models
For ethologists and neuroscientists, this work demonstrates that foundation models can analyze animal social behavior without domain-specific models, though results are preliminary and incremental.
The authors introduce MTT-Bench, a benchmark for predicting social dominance in mice from raw behavioral videos using fine-tuned Multimodal Large Language Models, achieving high agreement with tube test rankings.
Understanding social dominance in animal behavior is critical for neuroscience and behavioral studies. In this work, we explore the capability of Multimodal Large Language Models(MLLMs) to analyze raw behavioral video of mice and predict their dominance hierarchy. We introduce MTT-Bench, a novel benchmark comprising annotated videos of pairwise mouse interactions for Mouse Tube Test analysis. Building on existing MLLM architectures, we fine-tune these models to perform zero-shot inference on unseen behavioral sequences, predicting social dominance without explicit labels during testing. Our framework demonstrates promising results, showing high agreement with tube test rankings. This work opens a new direction for applying foundation models to ethology and social behavior analysis, without the need to design domain-specific models.