FishNet 2.0: A Comprehensive Multimodal Benchmark for Long-Tailed Fine-Grained Fish Understanding

Multimodal large language models (MLLMs) have demonstrated impressive cross-domain capabilities; however, their proficiency in specialized scientific domains such as marine biology remains underexplored. To systematically study this gap, we introduce FishNet2.0, a large-scale multimodal benchmark for long-tailed fine-grained fish understanding, comprising \newspe species with over 700,000 dense key-part annotations and more than 400,000 curated question-answer pairs. Beyond traditional recognition, FishNet2.0 integrates structured morphological descriptions and dense part-level localizations, enabling comprehensive evaluation across classification, detection, and grounded reasoning tasks. Extensive benchmarking of state-of-the-art MLLMs reveals severe degradation under real-world long-tailed distributions, with top-performing open-source models achieving below 40% accuracy. To diagnose the underlying causes, we propose a controlled evaluation framework that disentangles domain-specific knowledge from pure visual perception. Our analysis shows that performance failures stem primarily from insufficient domain knowledge rather than perceptual limitations. Importantly, we demonstrate that leveraging structured expert annotations, particularly explicit morphological descriptions and keypoint guidance, substantially improves zero-shot recognition performance. This establishes FishNet2.0 not only as a rigorous benchmark but also as a testbed for developing domain-aware multimodal reasoning strategies. Data and code will be released upon acceptance.