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Experiments show that current MLLMs struggle particularly on questions requiring the integration of multiple capabilities, whereas BasketballSkills outperforms them, highlighting the effectiveness of explicitly composing domain-specific capabilities for comprehensive basketball understanding.
This study introduces ProbeGen, a benchmark for zero-shot generative perception that casts monocular depth estimation, referring/reasoning segmentation, and object counting as conditional generation tasks specified through text prompts, and compares 20 models in total.
WorldCupArena is presented, a dynamic benchmark for language models and deep-research agents that can be reused for future leagues and cups, and shows only small gains in result and exact-score accuracy, but a clearer gain in Scoreline.
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