Arxiv Papers

Igor Melnyk
  • 5.0 (3)
  • SCIENCE
  • UPDATED DAILY
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. 18H AGO

    [QA] SWI: Speaking with Intent in Large Language Models

    The paper introduces Speaking with Intent (SWI) in large language models, enhancing reasoning and generation quality through explicit intent, outperforming traditional methods in various benchmarks. https://arxiv.org/abs//2503.21544 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

  2. 18H AGO

    SWI: Speaking with Intent in Large Language Models

    The paper introduces Speaking with Intent (SWI) in large language models, enhancing reasoning and generation quality through explicit intent, outperforming traditional methods in various benchmarks. https://arxiv.org/abs//2503.21544 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

    10 min

  3. 18H AGO

    [QA] Unified Multimodal Discrete Diffusion

    https://arxiv.org/abs//2503.20853 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

  4. 18H AGO

    Unified Multimodal Discrete Diffusion

    https://arxiv.org/abs//2503.20853 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

    20 min

  5. 1D AGO

    [QA] Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals

    Opt-CWM is a self-supervised method for motion estimation from videos, achieving state-of-the-art performance without labeled data by optimizing counterfactual probes from a pre-trained model. https://arxiv.org/abs//2503.19953 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

    18 min

  6. 1D AGO

    Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals

    Opt-CWM is a self-supervised method for motion estimation from videos, achieving state-of-the-art performance without labeled data by optimizing counterfactual probes from a pre-trained model. https://arxiv.org/abs//2503.19953 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

    18 min

  7. 1D AGO

    [QA] Open Deep Search: Democratizing Search with Open-source Reasoning Agents

    Open Deep Search (ODS) enhances open-source LLMs with reasoning agents and web search tools, achieving state-of-the-art performance and surpassing proprietary solutions in accuracy on key benchmarks. https://arxiv.org/abs//2503.20201 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

  8. 1D AGO

    Open Deep Search: Democratizing Search with Open-source Reasoning Agents

    Open Deep Search (ODS) enhances open-source LLMs with reasoning agents and web search tools, achieving state-of-the-art performance and surpassing proprietary solutions in accuracy on key benchmarks. https://arxiv.org/abs//2503.20201 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

    18 min
See All (2K)

    Ratings & Reviews

    5
    out of 5
    3 Ratings

    About

    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

    Information

    • Creator
      Igor Melnyk
    • Years Active
      2023 - 2025
    • Episodes
      2K
    • Rating
      Clean
    • Copyright
      © Igor Melnyk
    • Show Website
      Arxiv Papers

    You Might Also Like

    Content Restricted

    This episode can’t be played on the web in your country or region.

    To listen to explicit episodes, sign in.

    Stay up to date with this show

    Sign in or sign up to follow shows, save episodes, and get the latest updates.
    Select a country or region

    Africa, Middle East, and India

    Asia Pacific

    Europe

    Latin America and the Caribbean

    The United States and Canada