Arxiv Papers

Igor Melnyk
  • 5,0 (3)
  • SCIENCES
  • TOUS LES JOURS
Arxiv Papers

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

  1. -4 J

    [QA] Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning

    https://arxiv.org/abs//2507.00432 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

    7 min

  2. -4 J

    Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning

    https://arxiv.org/abs//2507.00432 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

    16 min

  3. -5 J

    [QA] DABstep: Data Agent Benchmark for Multi-step Reasoning

    DABstep is a benchmark for evaluating AI agents on multi-step data analysis tasks, featuring 450 real-world challenges that test data processing and contextual reasoning capabilities. https://arxiv.org/abs//2506.23719 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

    8 min

  4. -5 J

    DABstep: Data Agent Benchmark for Multi-step Reasoning

    DABstep is a benchmark for evaluating AI agents on multi-step data analysis tasks, featuring 450 real-world challenges that test data processing and contextual reasoning capabilities. https://arxiv.org/abs//2506.23719 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

    17 min

  5. -5 J

    [QA] Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling?

    This paper explores the effectiveness of inference-time techniques in vision-language models, finding that generation-based methods enhance reasoning more than verification methods, while self-correction in RL models shows limited benefits. https://arxiv.org/abs//2506.17417 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

    8 min

  6. -5 J

    Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling?

    This paper explores the effectiveness of inference-time techniques in vision-language models, finding that generation-based methods enhance reasoning more than verification methods, while self-correction in RL models shows limited benefits. https://arxiv.org/abs//2506.17417 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

    17 min

  7. -6 J

    [QA] LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs

    LLaVA-Scissor introduces a training-free token compression method for video multimodal models, utilizing Semantic Connected Components for effective, non-redundant semantic coverage, outperforming existing methods in various benchmarks. https://arxiv.org/abs//2506.21862 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

    8 min

  8. -6 J

    LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs

    LLaVA-Scissor introduces a training-free token compression method for video multimodal models, utilizing Semantic Connected Components for effective, non-redundant semantic coverage, outperforming existing methods in various benchmarks. https://arxiv.org/abs//2506.21862 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

    14 min
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Notes et avis

5
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3 notes

À propos

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

Informations

  • Création
    Igor Melnyk
  • Années d’activité
    2023 - 2025
  • Épisodes
    2 k
  • Classification
    Tous publics
  • Copyright
    © Igor Melnyk
  • Site web de l’émission
    Arxiv Papers

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