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| <title>Daily Papers</title> | |
| <link>https://huggingface.co/papers</link> | |
| <description>Listen to an AI-generated conversation about the most upvoted research paper on Hugging Face each day. Still a beta — it's an experiment! Discussions are AI-generated — verify facts before citing.</description> | |
| <language>en-us</language> | |
| <itunes:author>Hugging Face</itunes:author> | |
| <itunes:summary>Each day, this podcast dives into the top trending ML paper on Hugging Face. Still a beta — it's an experiment! Discussions are AI-generated — verify facts before citing.</itunes:summary> | |
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| <itunes:category text="Technology" /> | |
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| <itunes:name>HF</itunes:name> | |
| <itunes:email>[email protected]</itunes:email> | |
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| <lastBuildDate>Wed, 21 May 2025 14:28:49 +0000</lastBuildDate> | |
| <item> | |
| <title>BAGEL Makes Waves in Multimodal Pretraining</title> | |
| <description>BAGEL, a groundbreaking new multimodal foundation model, is revolutionizing the way we approach unified multimodal understanding and generation. Trained on trillions of tokens curated from diverse multimodal data, BAGEL exhibits emerging properties in complex multimodal reasoning, effortlessly navigating tasks like free-form image manipulation and future frame prediction while outperforming top-tier open-source VLMs on standard benchmarks. | |
| [Read the paper on Hugging Face](https://huggingface.co/papers/2505.14683)</description> | |
| <pubDate>Wed, 21 May 2025 14:28:49 +0000</pubDate> | |
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| <title>Solving the Scaling Paradox with Chain-of-Language-Model</title> | |
| <description>We'll explore the groundbreaking concept of Chain-of-Language-Model (CoLM) which offers a novel approach to scale up language models while preserving training efficiency and enabling elastic inference by integrating multi-scale training within a single forward propagation.</description> | |
| <pubDate>Tue, 20 May 2025 18:46:38 +0000</pubDate> | |
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| <title>Beyond Emergent 'Aha!' Moments: Unlocking Reliable Reasoning in Large Models</title> | |
| <description>We explore a novel approach to boost the reliability and scalability of large reasoning models by explicitly aligning them with classical reasoning meta-abilities. Our three-stage pipeline improves performance by over 10% and enables domain-specific reinforcement learning from aligned checkpoints, demonstrating a scalable and dependable foundation for reasoning.</description> | |
| <pubDate>Fri, 16 May 2025 14:24:17 +0000</pubDate> | |
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| <title>MiniMax-Speech Revolutionizes Text-to-Speech with Zero-Shot Voice Cloning</title> | |
| <description>MiniMax-Speech is a groundbreaking Text-to-Speech model that generates high-quality speech with near-indistinguishable human resemblance, achieving state-of-the-art results on multiple objective and subjective evaluation metrics. This innovative model introduces a learnable speaker encoder module, allowing for zero-shot voice cloning and exceptional speaker similarity in one-shot scenarios.</description> | |
| <pubDate>Thu, 15 May 2025 15:59:41 +0000</pubDate> | |
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| <title>SEED-15VL – Smarter, Leaner, Sharper</title> | |
| <description>SEED-15VL mixes a compact vision encoder with a MOE language model to crush reasoning benchmarks. Trained with diverse data and verifiable rewards, it's a glimpse into efficient, real-world AI—with a few sci-fi vibes.</description> | |
| <pubDate>Tue, 14 May 2025 20:00:00 +0000</pubDate> | |
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| <title>Step 1x3D – From Scrap Models to Masterpieces</title> | |
| <description>Today's episode dives into Step 1x3D, a new open-source method that cleans noisy 3D data, bridges 2D–3D generation, and rivals top proprietary tools. From mesh repair to texture-perfect diffusion, it's a major leap for 3D AI.</description> | |
| <pubDate>Tue, 13 May 2025 10:00:00 +0000</pubDate> | |
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