mlsanityData to 14 Oct 2026

Rodent-Bench

arXiv 2602.18540 ↗First posted 20 Feb 2026cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

We present Rodent-Bench, a novel benchmark designed to evaluate the ability of Multimodal Large Language Models (MLLMs) to annotate rodent behaviour footage. We evaluate state-of-the-art MLLMs, including Gemini-2.5-Pro, Gemini-2.5-Flash and Qwen-VL-Max, using this benchmark and find that none of these models perform strongly enough to be used as an assistant for this task. Our benchmark encompasses diverse datasets spanning multiple behavioral paradigms including social interactions, grooming, scratching, and freezing behaviors, with videos ranging from 10 minutes to 35 minutes in length. We provide two benchmark versions to accommodate varying model capabilities and establish standardized evaluation metrics including second-wise accuracy, macro F1, mean average precision, mutual information, and Matthew's correlation coefficient. While some models show modest performance on certain datasets (notably grooming detection), overall results reveal significant challenges in temporal segmentation, handling extended video sequences, and distinguishing subtle behavioral states. Our analysis identifies key limitations in current MLLMs for scientific video annotation and provides insights for future model development. Rodent-Bench serves as a foundation for tracking progress toward reliable automated behavioral annotation in neuroscience research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 7 of its 20 references that are in the map. First posted on arXiv 20 Feb 2026.

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7 of 20 references.

References · 7

  1. 2024Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysiscited by 1,413
  2. 2023A Survey on Multimodal Large Language Modelscited by 909
  3. 2023Video Understanding with Large Language Models: A Surveycited by 206
  4. 2019Deep learning tools for the measurement of animal behavior in neurosciencecited by 44
  5. 2016End-to-end training of object class detectors for mean average precisioncited by 35
  6. 2021The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactionscited by 31
  7. 2024From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understandingcited by 30

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  3. 2025MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis
  4. 2025Preliminary Use of Vision Language Model Driven Extraction of Mouse Behavior Towards Understanding Fear Expression
  5. 2024GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding
  6. 2026CheeseBench: Evaluating Large Language Models on Rodent Behavioral Neuroscience Paradigms
  7. 2023AmadeusGPT: a natural language interface for interactive animal behavioral analysis
  8. 2025Fine-Tuning Video-Text Contrastive Model for Primate Behavior Retrieval from Unlabeled Raw Videos