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 YouTube, Apple Podcasts.

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. 223
    When Grok Graded Its Own Encyclopedia And Marked Itself Down
    Grokipedia vs Wikipedia: An LLM-Based Audit of Political Neutrality along Ideologies
    Vlahos, Bied, Bie · Ghent University·18 min·Jul 20, 2026
  2. 222
    The Bias Isn't in Your Prompt — It's Inside the Model
    Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Values
    Betley, Treutlein, Dubiński et al. · TruthfulAI·16 min·Jul 19, 2026
  3. 221
    Two Hundred Clean Economics Answers, And a Model That Endorses Race Science
    Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
    Graham, Stevinson, Barsheshat · Independent·15 min·Jul 17, 2026
  4. 220
    Write Like It's 1923: The One-Prompt Trick That Beats AI Detectors
    UTS at ELOQUENT 2026 Voight-Kampff: structural shifts in AI writing bypass state-of-the-art detectors
    Galat, Rizoiu · University of Technology Sydney·13 min·Jul 16, 2026
  5. 219
    Forty-Four AI Models, One Word, And The Newest Ones Conform Most
    The One-Word Census: Answer-Choice Conformity Across 44 Language Models
    Parikh · Cornell Tech·15 min·Jul 15, 2026
  6. 218
    When Universities Say Embrace AI But Half the CS Syllabi Ban It
    A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education
    Ganguly, Johri, McDonald et al. · George Mason University·14 min·Jul 15, 2026
  7. 217
    Why an AI Called Fourteen Broken Figures Perfect, And What It Reveals About Test-Time Compute
    Interaction Scaling: Grounding the Third Axis of Test-Time Compute
    Li, Shi · Pine AI·14 min·Jul 14, 2026
  8. 216
    The AI Tutor That Gives Poor Kids a Thinner History
    The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students
    Popovici, Ionascu, Dumitran · Universitatea din Bucuresti·12 min·Jul 14, 2026
  9. 215
    The Same Policy Scored 85 for the US and 36 for Russia
    Geopolitical alignment: Endorsement effects in large language models
    Chupilkin · Department of Politics and International Relations·14 min·Jul 13, 2026
  10. 214
    The Medical AI Answer That's Accurate, Sourced, and Still Wrong
    Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation
    Caruzzo, Yoo, Kim · Lunit·13 min·Jul 13, 2026
View all episodes

Watch

Every episode is also a read-along video — the paper's figures and charts up top, the transcript word-synced below. New videos land on the channel.