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Artwork for AI Papers: A Deep Dive

AI Papers: A Deep Dive

paperdive.ai
Artificial Intelligence
Pokerskill
Reinforcement Learning
Large Language Models
Deltabox
Firefly
Erdős Problems
Toolcua
Harbin Institute Of Technology
Shepherd
Scientistone
Agent RL
Parametric Memory
Fast Weights (TMEM)
Streaming Communication In Multi-Agent Reasoning
Latency and Pipelining
Quest, Training Frontier Deep Research Agents With Fully Synthetic Tasks
Multi-Agent Systems
Forecasting
Commitment Sharpening

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 experiments do and don't show, and the strongest critique against the result. The goal isn't a five-minute summary; it's the kind of conversation you'd hav... more

PublishesDailyEpisodes156Founded2 months ago
Number of ListenersCategory
Technology

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Artwork for AI Papers: A Deep Dive

Latest Episodes

Welcome to the catch-up for June 15–21, 2026 — eighteen episodes that, taken together, kept circling one question: how much of an AI system's behavior lives outside the model weights, and what breaks when we forget that. We saw a way to build forgett... more

A Robot That Plays Before You Give It a Job, And Why That Beats Retrying

Source: Playful Agentic Robot Learning

Paper was published on June 17, 2026

This episode was AI-generated on June 19, 2026. The script was written by an AI language model and... more

How Floating-Point Rounding Lets a Model Tell Which Chip It's On — And Misbehave

Source: FloatDoor: Platform-Triggered Backdoors in LLMs

Paper was published on June 17, 2026

This episode was AI-generated on June 19, 2026. The script was written by... more

Can a Coding Agent Run Its Own Robot Experiments Overnight, With No Human Resetting the Scene?

Source: ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Paper was published on June 18, 2026

This episode was AI-generated on June 19, 2... more

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Recent Guests

Amartya Roy
Author, IIT Delhi, and co-author of the discussed paper
IIT Delhi
Episode: When Better Fine-Tuning Can't Help: A Geometric Impossibility in LLM Causal Reasoning
Sonali Parbu
Co-author of the discussed paper
Imperial College London
Episode: When Better Fine-Tuning Can't Help: A Geometric Impossibility in LLM Causal Reasoning

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Bella
Host of AI Papers: A Deep Dive
Tyler
Host of AI Papers: A Deep Dive
Finn
Host of the show, providing the main narration and synthesis
Juniper
Co-host providing critique and clarifying questions
Cassidy
Co-host, featured discussing with Finn / Host of AI Papers A Deep Dive

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Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward
Q: What does the cost and scalability look like for generating Firefly-scale data?
The entire Firefly dataset reportedly costs about $47,000 to generate, involving tens of billions of tokens processed by a large model on cloud infrastructure, offering a cost-efficient alternative to human annotation at scale.
Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward
Q: How is the data generated and verified to ensure its usefulness for training?
They run thousands of real tool calls across many servers, log every call with inputs and outputs, and then backchain a natural language task from those logs, ensuring the ground-truth trajectory comes from actual executions.
Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward
Q: What is the core inversion at the heart of Firefly, and why is it considered transformative?
The core inversion is to explore with a model on real tools first, record the trajectories, and then write the task backward from those observed results, making the correctness of labels a property of the data generation process itself rather than post-hoc verification.

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Frequently Asked Questions About AI Papers: A Deep Dive

What is AI Papers: A Deep Dive about and what kind of topics does it cover?

This show delivers rigorous, long-form explorations of cutting AI research, with a focus on papers about AI agents, embodied systems, and evaluation practices. Episodes unpack the motivation, mechanisms, and math behind each approach, critique the experiments and claims, and discuss practical engineering considerations for deploying agentic systems. The conversations are technical, often debating architecture choices, reliability, and real-world implications, while highlighting trade-offs and open questions. A standout aspect is the depth and specificity: conversations regularly reference concrete papers, metrics, and implementation details, making it valuable for listeners who want to understand not just the gist but the nuances and limita... more

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AI Papers: A Deep Dive launched 2 months ago and published 156 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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