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© Igor Melnyk
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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
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![[QA] On the Theoretical Limitations of Embedding-Based Retrieval](https://s3.castbox.fm/6f/ac/8c/695268255c3ee26baa44bc8eec352b494c_scaled_v1_400.jpg "[QA] On the Theoretical Limitations of Embedding-Based Retrieval")
https://arxiv.org/abs//2508.21038YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
https://arxiv.org/abs//2508.21038YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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//2508.12631YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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//2508.12631YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
https://arxiv.org/abs//2508.15734YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
https://arxiv.org/abs//2508.15734YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
DeepConf enhances reasoning efficiency and performance in Large Language Models by filtering low-quality traces using internal confidence signals, achieving high accuracy and reduced token generation without extra training.https://arxiv.org/abs//2508.15260YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
DeepConf enhances reasoning efficiency and performance in Large Language Models by filtering low-quality traces using internal confidence signals, achieving high accuracy and reduced token generation without extra training.https://arxiv.org/abs//2508.15260YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
Intern-S1 is a multimodal model that excels in scientific tasks, outperforming both open-source and closed-source models, and aims to bridge the gap in high-value scientific research.https://arxiv.org/abs//2508.15763YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
Intern-S1 is a multimodal model that excels in scientific tasks, outperforming both open-source and closed-source models, and aims to bridge the gap in high-value scientific research.https://arxiv.org/abs//2508.15763YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
The paper identifies search-time contamination (STC) in evaluating search-based LLM agents, revealing how data leaks compromise benchmark integrity and proposing best practices for trustworthy evaluations.https://arxiv.org/abs//2508.13180YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
The paper identifies search-time contamination (STC) in evaluating search-based LLM agents, revealing how data leaks compromise benchmark integrity and proposing best practices for trustworthy evaluations.https://arxiv.org/abs//2508.13180YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
This paper introduces Thyme, a multimodal model enhancing image manipulation and reasoning through executable code, achieving significant performance improvements in perception and reasoning tasks via innovative training strategies.https://arxiv.org/abs//2508.11630YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
This paper introduces Thyme, a multimodal model enhancing image manipulation and reasoning through executable code, achieving significant performance improvements in perception and reasoning tasks via innovative training strategies.https://arxiv.org/abs//2508.11630YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
The paper explores using large language models as efficient simulators for reinforcement learning tasks, introducing Self-Search RL to enhance internal knowledge utilization and reduce reliance on external search engines.https://arxiv.org/abs//2508.10874YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
The paper explores using large language models as efficient simulators for reinforcement learning tasks, introducing Self-Search RL to enhance internal knowledge utilization and reduce reliance on external search engines.https://arxiv.org/abs//2508.10874YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
This paper explores filtering dual-use topics from training data to enhance the tamper-resistance of open-weight AI systems, demonstrating significant improvements in adversarial fine-tuning resistance without degrading unrelated capabilities.https://arxiv.org/abs//2508.06601YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
This paper explores filtering dual-use topics from training data to enhance the tamper-resistance of open-weight AI systems, demonstrating significant improvements in adversarial fine-tuning resistance without degrading unrelated capabilities.https://arxiv.org/abs//2508.06601YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
This paper presents ASearcher, an open-source project enhancing search agents' capabilities through scalable RL training, achieving significant performance improvements in complex query handling and long-horizon search tasks.https://arxiv.org/abs//2508.07976YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
This paper presents ASearcher, an open-source project enhancing search agents' capabilities through scalable RL training, achieving significant performance improvements in complex query handling and long-horizon search tasks.https://arxiv.org/abs//2508.07976YouTube: https://www.youtube.com/@ArxivPapersTikTok: https://www.tiktok.com/@arxiv_papersApple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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