New podcast · First episode out

AI Papers:
a deep dive.

Breaking down cutting-edge AI research, one paper at a time. Novel, rigorous, and relevant work in artificial intelligence and agentic engineering — distilled into listenable episodes.

Format
Research deep dive
Cadence
Per important paper
Length
~20–40 min
Topics
AI · Agentic eng.
AI Papers: A Deep Dive — cover art
paperdive.ai

About

Every episode is a deep dive into a single paper that is important, novel, and relevant to artificial intelligence and agentic engineering.

The show is fully AI-generated. Hosts are synthesized voice models from ElevenLabs. Scripts are produced from the primary source material — the paper itself, its references, and surrounding discussion — so the result is conversational without sacrificing rigor.

01

Primary sources

Each episode starts from the paper — abstract, methods, results — not secondhand summaries or press releases.

02

Agentic focus

Curated for engineers and researchers working on agents, reasoning, and the systems that connect them.

03

Synthesized, not scripted

Voice models from ElevenLabs. Produced end-to-end with AI, transparent about the stack behind every episode.

How this started

Paper Dive was inspired by Last Week in AI — a podcast I listen to during my 45-minute commute to work. They cover the week’s news, policy, and products, then usually end with a deep dive into one or two research papers. Those segments taught me a lot about AI, and I’ve found that understanding the research also makes me better at using these tools in practice.

I wanted more of those deep dives, and the idea felt like a good excuse to sharpen my own skills with the coding agents. I started generating episodes for myself; what began as a private podcast feed eventually became public on Apple Podcasts, Spotify, and this site.

The API costs were already being incurred anyway, so publishing the episodes felt like an easy decision. If other people find them useful too, even better.

Episodes

Each episode breaks down a single paper.
More coming — follow in your podcast app.

  1. 079
    An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models
    Chen, Xu, Zhao et al. · Tongji University / Shanghai AI Laboratory / Nanyang Technological University·29 min·May 25, 2026
  2. 078
    Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
    Yang, Gong, Huang et al. · Microsoft·28 min·May 25, 2026
  3. 077
    Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
    Xia, Wang, Tang et al. · State Key Laboratory of General Artificial Intelligence·22 min·May 25, 2026
  4. 076
    Same Model, Organized Differently: How an Agent Architecture Beat Frontier Systems at Research Math
    Zhao, Yuan, Choi et al. · Georgia Institute of Technology·22 min·May 25, 2026
  5. 075
    Growing Code and Proof Together: Verified Systems in Ten Hours Instead of a Year
    Agarwal, Krentsel, Liu et al. · UC Berkeley·28 min·May 25, 2026
  6. 074
    How a Fifteen-Hundred-Dollar Training Run Matched Llama and Gemma on Reasoning
    Wang, Liu, Wang et al. · Sapient Intelligence·21 min·May 24, 2026
  7. 073
    When Three LLMs Talk to Each Other, Their Ideas Quietly Stop Moving
    Kong, Lai, Piao et al. · University of Toronto·28 min·May 23, 2026
  8. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
    Shi, Zheng, Juan et al. · Princeton University·29 min·May 23, 2026
  9. 071
    When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface
    Xu, Wen, Li · Peking University·23 min·May 22, 2026
  10. 070
    When Models Know the Answer But Say the Wrong Thing Anyway
    Yeom, Sok, Kim et al. · Graduate School of Data Science·22 min·May 22, 2026
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