FishNet++: Analyzing the capabilities of Multimodal Large Language Models in marine biology

• URL: http://arxiv.org/abs/2509.25564v1
• Date: Mon, 29 Sep 2025 22:39:58 GMT
• Title: FishNet++: Analyzing the capabilities of Multimodal Large Language Models in marine biology
• Authors: Faizan Farooq Khan, Yousef Radwan, Eslam Abdelrahman, Abdulwahab Felemban, Aymen Mir, Nico K. Michiels, Andrew J. Temple, Michael L. Berumen, Mohamed Elhoseiny,
• Abstract summary: Multimodal large language models (MLLMs) have demonstrated impressive cross-domain capabilities, yet their proficiency in specialized scientific fields like marine biology remains underexplored.<n>In this work, we systematically evaluate state-of-the-art MLLMs and reveal significant limitations in their ability to perform fine-grained recognition of fish species.<n>We introduce FishNet++, a large-scale, multimodal benchmark.<n>FishNet++ significantly extends existing resources with 35,133 textual descriptions for multimodal learning, 706,426 key-point annotations for morphological studies, and 119,399 bounding boxes for detection.
• Score: 28.683426892594458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) have demonstrated impressive cross-domain capabilities, yet their proficiency in specialized scientific fields like marine biology remains underexplored. In this work, we systematically evaluate state-of-the-art MLLMs and reveal significant limitations in their ability to perform fine-grained recognition of fish species, with the best open-source models achieving less than 10\% accuracy. This task is critical for monitoring marine ecosystems under anthropogenic pressure. To address this gap and investigate whether these failures stem from a lack of domain knowledge, we introduce FishNet++, a large-scale, multimodal benchmark. FishNet++ significantly extends existing resources with 35,133 textual descriptions for multimodal learning, 706,426 key-point annotations for morphological studies, and 119,399 bounding boxes for detection. By providing this comprehensive suite of annotations, our work facilitates the development and evaluation of specialized vision-language models capable of advancing aquatic science.

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