ArticleNeurophotonics2026
Toward video-LLM driven workflow for behavioral segmentation and scoring in mice performing a skilled water-reaching task: an evaluation of recent LLM models.
Article in Neurophotonics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Manual behavior scoring is labor-intensive and subjective. Video-capable large language models (LLMs) offer a transformative, scalable solution for accelerating and standardizing neuroscience workflows. We benchmarked state-of-the-art video LLMs (Gemini 2.5 Pro, Qwen3-VL, and VideoLLaMA3) for automated behavioral segmentation and scoring of mice performing a water-reaching task. Videos of mice performing water reaching were analyzed by the LLMs. Accuracy was compared across different models and against prompt adjustments within Gemini. To assess classification determinants, video fidelity was altered through pixel interpolation and key regions blurred (paws/snout-mouth). In addition, the models were asked to describe the mouse's actions over time. Finally, an open-source rat lever-pressing dataset was utilized to validate behavioral segmentation under a few-shot learning framework, assessing the impact of visual examples on the identification of discrete action sequences. Gemini 2.5 Pro (
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