Theme · 20 episode(s)

AI Agents

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Definition

AI agents are systems built around an AI model that perceive some environment, take actions in it, and pursue goals across multiple steps. The defining shift from chat assistants is autonomy: the model decides what to do next rather than waiting for the next human message.

Episodes covering this

  1. 206
    How Four-Second Clips Become Hours of Playable AI Soccer
    Multiplayer Interactive World Models with Representation Autoencoders
    · ·15 min·Jul 07, 2026
  2. 191
    How One Researcher Beat GPT-5.2 and Gemini 3 by Judging Their Answers, Not Improving Them
    Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2
    Land · Independent Researcher·26 min·Jul 02, 2026
  3. 182
    How a Tiny Model Too Weak to Plan Cuts a Bigger Agent's Hallucinations by 80%
    Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents
    Song, Cai · Emory University·17 min·Jun 29, 2026
  4. 180
    The Bug Where Smart Assistants Read a Fact and Still Forget It
    Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
    Patel · Vrin·24 min·Jun 29, 2026
  5. 173
    The Free Step-Level Grader Hiding in Every RL Training Run
    Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
    Oh, Li, Park et al. · University of Wisconsin–Madison·22 min·Jun 25, 2026
  6. 170
    When a One-Liner Beats Your Agent's Clever Verification Logic
    Bayesian control for coding agents
    Papamarkou, Smirnov, Mazanov et al. · PolyShape / National Technical University of Athens·26 min·Jun 24, 2026
  7. 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
  8. 122
    When Your Coding Agent Lies About the Fix: Verifying the Plan Before the Model Runs
    Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
    Wang, Huang, Wang et al. · University of Illinois Urbana-Champaign·24 min·Jun 09, 2026
  9. 121
    When the Agent Says It's Done But Nothing Happened: Debugging the Harness, Not the Model
    From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws
    Chen, Wang, Liu et al. · Institute of Software·27 min·Jun 05, 2026
  10. 115
    Teaching a Phone Agent to Reason Silently, And Keeping It Honest
    MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
    Yang, Hu, Hao et al. · Beihang University·24 min·Jun 04, 2026
  11. 106
    Giving Agents a Notebook Instead of New Weights: How ExpGraph Lets Frozen Models Learn
    ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents
    Feng, Ye, Luo et al. · University of Illinois Urbana-Champaign·26 min·Jun 02, 2026
  12. 103
    AI Agents Tried to Invent a Post-Human Language, And Reinvented Cherokee
    Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion
    Beltoft, Brach, Torrielli et al. · University of Southern Denmark·26 min·Jun 01, 2026
  13. 092
    When Search Agents Don't Really Search: The Memory Shortcut Hiding in Browsing Benchmarks
    LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?
    Fan, Wang, Chu et al. · Harbin Institute of Technology·27 min·May 28, 2026
  14. 086
    Why Frozen-Weight Agents Still Get Worse Over Time
    Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
    Zhu, Ro, Robertson et al. · The University of Texas at Austin·23 min·May 27, 2026
  15. 084
    Terminal Agents Get Free Supervision From The Tokens We've Been Throwing Away
    ECHO: Terminal Agents Learn World Models for Free
    Shrivastava, Kauffmann, Awadallah et al. · Microsoft Research·26 min·May 26, 2026
  16. 060
    When Splitting One Model Across Three Agents Doubles Its Accuracy
    NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
    Lu, Fang, Zhong et al. · University of Georgia·26 min·May 20, 2026
  17. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
    Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design
    Pepe, Lin, Magka et al. · FAIR at Meta·32 min·May 18, 2026
  18. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
    Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning
    Kereopa-Yorke, Diaz, Wright et al. · Microsoft·31 min·May 12, 2026
  19. 031
    When Your AI Assistant Won't Let Go of Old Facts About You
    STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?
    Chao, Bai, Sheng et al. · Wuhan University·24 min·May 09, 2026
  20. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
    Gym-Anything: Turn any Software into an Agent Environment
    Aggarwal, Neubig, Welleck · CMU·31 min·May 03, 2026

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