Theme · 10 episode(s)

Reproducibility

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Definition

Reproducibility is the property that other researchers can recreate your results from your code, data, and procedure. In modern ML it’s under quiet but constant threat from undisclosed data, closed-weight models, and runs that nobody is going to rerun on a thousand GPUs.

Episodes covering this

  1. 210
    Same Website Request, Different Code — The Bias You Can't See
    Biased or Personalized? The Impact of Personal Information on AI-driven Development
    · ·14 min·Jul 09, 2026
  2. 208
    The Blank Space in Your AI Approval Box That Isn't Empty
    Unicode TAG-Block Concealment of Tool-Metadata Payloads in the Model Context Protocol: An Approval-View Fidelity Gap Across Three Independent Server Implementations
    · ·15 min·Jul 08, 2026
  3. 206
    How Four-Second Clips Become Hours of Playable AI Soccer
    Multiplayer Interactive World Models with Representation Autoencoders
    · ·15 min·Jul 07, 2026
  4. 201
    One in Four NeurIPS Papers Cites a Reference That Doesn't Exist
    Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences
    Russinovich, Kumar, Salem · Microsoft·19 min·Jul 06, 2026
  5. 197
    Twin Problems Suggest AI Reasoning Gains Are Mostly Better Fact Recall
    IsoSci: A Benchmark of Isomorphic Cross-Domain Science Problems for Evaluating Reasoning versus Knowledge Retrieval in LLMs
    Abdaljalil, Serpedin, Kurban · Texas A&M University·17 min·Jul 03, 2026
  6. 196
    AI Agents Reached Opposite Conclusions From the Same Data — and Passed Review
    The Agentic Garden of Forking Paths
    Miao, Pritchard, Zou · Stanford University·18 min·Jul 03, 2026
  7. 176
    An AI Designed Its Own Psychology Studies, Then Confirmed What It Found
    Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist
    Jagadish, Strittmatter, Jacoby et al. · Princeton University·31 min·Jun 26, 2026
  8. 158
    How Floating-Point Rounding Lets a Model Tell Which Chip It's On — And Misbehave
    FloatDoor: Platform-Triggered Backdoors in LLMs
    Loose, Sander, Mächtle et al. · University of Luebeck·29 min·Jun 19, 2026
  9. 144
    When an AI Agent Just Copies Its Tool — And Bigger Models Copy More
    When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More
    Wang, Vemuri · raptorX.ai·15 min·Jun 15, 2026
  10. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
    SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning
    Limozin, Durech, Hoefler et al. · ETH AI Center·23 min·May 02, 2026