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Multimodal DeepResearch Hits SOTA Benchmarks

Multimodal DeepResearch Hits SOTA Benchmarks

Researchers from CUHK MMLab, USTC, and Xiaohongshu built a multimodal deep-research LLM for real-world search, tackling image retrieval hit rates and reasoning depth via data synthesis and RL. It performs dozens of reasoning rounds and hundreds of search interactions. The model achieves SOTA on 6 benchmarks with smaller parameters than competitors.