AI Papers: A Deep Dive

Technology Podcasts

Long-form deep dives into new research on Artificial Intelligence, AI agents and the engineering practice of building them - one paper per episode. We unpack the motivating problem, how the method actually works, the math that matters, what the...

Episodes

An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models

Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training

Same Model, Organized Differently: How an Agent Architecture Beat Frontier Systems at Research Math

Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It

Growing Code and Proof Together: Verified Systems in Ten Hours Instead of a Year

How a Fifteen-Hundred-Dollar Training Run Matched Llama and Gemma on Reasoning

A Robot Made Graphene Without Help, And Caught Itself Hallucinating

When Three LLMs Talk to Each Other, Their Ideas Quietly Stop Moving

When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions

When Models Know the Answer But Say the Wrong Thing Anyway

The OS Trick That Makes Tree Search Practical for Coding Agents

An AI Just Solved a 1996 Erdős Problem—and the Simplest Agent Won

When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface

Why Giving an AI Agent More Tools Can Make It Worse at Using a Computer

One Loop to Optimize Them All: A Universal API for LLM-Driven Discovery

When Agent Memory Stops Being a Database and Starts Being a Skill

Why Web Agents Are Slow: A Compiler-Style Fix for Computer-Use Latency

When Splitting One Model Across Three Agents Doubles Its Accuracy

Treating Hallucinations as Exploits: A Gate-Based Architecture for Agent Safety

Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward