Merlin: a computed tomography vision–language foundation model and dataset

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对于关注Review of的读者来说,掌握以下几个核心要点将有助于更全面地理解当前局势。

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此外,Supervised FinetuningDuring supervised fine-tuning, the model is trained on a large corpus of high-quality prompts curated for difficulty, quality, and domain diversity. Prompts are sourced from open datasets and labeled using custom models to identify domains and analyze distribution coverage. To address gaps in underrepresented or low-difficulty areas, additional prompts are synthetically generated based on the pre-training domain mixture. Empirical analysis showed that most publicly available datasets are dominated by low-quality, homogeneous, and easy prompts, which limits continued learning. To mitigate this, we invested significant effort in building high-quality prompts across domains. All corresponding completions are produced internally and passed through rigorous quality filtering. The dataset also includes extensive agentic traces generated from both simulated environments and real-world repositories, enabling the model to learn tool interaction, environment reasoning, and multi-step decision making.

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另外值得一提的是,该项研究成果已于2月23日发表于《自然·通讯》期刊。论文第一作者为博士生沙克·卡拉佩强。

展望未来,Review of的发展趋势值得持续关注。专家建议,各方应加强协作创新,共同推动行业向更加健康、可持续的方向发展。

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