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rss.xml
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<guid>https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-06-09.wav</guid>
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<itunes:explicit>false</itunes:explicit>
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</item>
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<item>
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<title>One-Step Video Restoration Breakthrough with SeedVR2</title>
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<description>SeedVR2 represents a groundbreaking approach to video restoration, achieving high-quality visual fidelity and significantly reduced computational costs. This cutting-edge method leverages an adaptive window attention mechanism and adversarial post-training to restore degraded videos in a single step, surpassing the performance of traditional multi-step diffusion-based models.
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<a href="https://huggingface.co/papers/2506.05301">[Read the paper on Hugging Face]</a></description>
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<pubDate>Fri, 06 Jun 2025 14:48:52 +0000</pubDate>
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<enclosure url="https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-06-06.wav" length="14832044" type="audio/wav" />
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<guid>https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-06-06.wav</guid>
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<itunes:explicit>false</itunes:explicit>
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</item>
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<item>
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<title>"Breaking AI Records: MiMo-VL-7B Revolutionizes Vision-Language Understanding"</title>
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<description>We explore the groundbreaking MiMo-VL-7B model, a vision-language system that surpasses state-of-the-art performance in both general visual understanding and multimodal reasoning, and discover the secret to its success through its four-stage pre-training phase and Mixed On-policy Reinforcement Learning framework.
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<guid>https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-06-09.wav</guid>
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<itunes:explicit>false</itunes:explicit>
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</item>
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<item>
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<title>"Breaking AI Records: MiMo-VL-7B Revolutionizes Vision-Language Understanding"</title>
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<description>We explore the groundbreaking MiMo-VL-7B model, a vision-language system that surpasses state-of-the-art performance in both general visual understanding and multimodal reasoning, and discover the secret to its success through its four-stage pre-training phase and Mixed On-policy Reinforcement Learning framework.
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