<description>&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;https://arxiv.org/abs//2508.21038&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;YouTube: https://www.youtube.com/@ArxivPapers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;TikTok: https://www.tiktok.com/@arxiv_papers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;
</description>

https://arxiv.org/abs//2508.21038 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

[QA] On the Theoretical Limitations of Embedding-Based Retrieval

On the Theoretical Limitations of Embedding-Based Retrieval

<description>&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Avengers-Pro is a test-time routing framework that optimizes performance and efficiency in LLMs, achieving state-of-the-art results by dynamically assigning queries to suitable models based on performance-efficiency scores.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;https://arxiv.org/abs//2508.12631&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;YouTube: https://www.youtube.com/@ArxivPapers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;TikTok: https://www.tiktok.com/@arxiv_papers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;
</description>

Avengers-Pro is a test-time routing framework that optimizes performance and efficiency in LLMs, achieving state-of-the-art results by dynamically assigning queries to suitable models based on performance-efficiency scores. https://arxiv.org/abs//2508.12631 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

[QA] Beyond GPT-5: Making LLMs Cheaper and Better via Performance–Efficiency Optimized Routing

Beyond GPT-5: Making LLMs Cheaper and Better via Performance–Efficiency Optimized Routing

<description>&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;https://arxiv.org/abs//2508.15734&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;YouTube: https://www.youtube.com/@ArxivPapers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;TikTok: https://www.tiktok.com/@arxiv_papers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;
</description>

https://arxiv.org/abs//2508.15734 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

[QA] Measuring the environmental impact of delivering AI at Google Scale

Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers

Arxiv Papers

Details

Recent Episodes