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@@ -14,7 +14,17 @@
       <itunes:email>florent.daudens@hf.co</itunes:email>
       </itunes:owner>
     <itunes:image href="https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/images/cover3.png" />
-    <lastBuildDate>Wed, 21 May 2025 14:28:49 +0000</lastBuildDate>
+    <lastBuildDate>Thu, 22 May 2025 15:07:32 +0000</lastBuildDate>
+    <item>
+      <title>WEB-SHEPHERD: Revolutionizing Web Navigation with AI Precision</title>
+      <description>We explore the development of WEB-SHEPHERD, a process reward model that assesses web navigation trajectories at a step-level, and its potential to revolutionize the field of artificial intelligence and web navigation.
+
+&lt;a href="https://huggingface.co/papers/2505.15277"&gt;[Read the paper on Hugging Face]&lt;/a&gt;</description>
+      <pubDate>Thu, 22 May 2025 15:07:32 +0000</pubDate>
+      <enclosure url="https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-05-22.wav" length="7664444" type="audio/wav" />
+      <guid>https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-05-22.wav</guid>
+      <itunes:explicit>false</itunes:explicit>
+      </item>
     <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.