May 27, 2026

He Raised $70M to Cure Every Disease With AI

Samuel Rodriques, founder of Edison Scientific, shares his journey from physics to building an AI scientist named Kosmos. He discusses how AI agents are already making novel discoveries, including a potential cure for blindness, and are poised to revolutionize drug discovery. The conversation dives into AI's strengths in high-throughput reasoning, the critical bottlenecks in clinical trials, proposed reforms for the US medical system, and whether human scientists will still be needed in an age of hyper-intelligent AI.

May 26, 2026

Q-learning with Flow-Matching Policies

This talk explores methods for optimizing expressive, multi-modal policies, such as those based on flow-matching, with off-policy reinforcement learning. The speaker presents two novel algorithms, FQ-RL and CAM, designed to overcome the instability of backpropagation through multi-step generative models, enabling effective online self-improvement and adaptation for robotic manipulation tasks.

May 26, 2026

Where the Score Lives: What Wavelets Reveal About Diffusion Models

This talk explores the paradox of why diffusion models generalize rather than memorize. It introduces an analytically tractable, wavelet-based parameterization of the score function, allowing for an interpretable analysis of how architectural biases (like locality) and data statistics interact to influence denoising performance and generalization.

May 26, 2026

How Cursor Trained Composer on Fireworks: Distributed Infrastructure for High-Performance RL

Cursor's Federico Cassano and Fireworks' Dmytro Dzhulgakov detail their collaboration on Composer 2, a specialized foundation model for software engineering. They discuss their top-down training strategy, the infrastructure challenges of large-scale distributed Reinforcement Learning on sparse models, and how model specialization achieves frontier performance with superior efficiency.

May 26, 2026

End-to-End Foundation Models for the Energy Industry — with Jazmia Henry

Jazmia Henry details the end-to-end process of building specialized foundation models for the energy industry. She covers the four key stages from data curation of unstructured, handwritten documents to optimizing inference, and introduces her Grounded Continuous Evaluation (GCE) framework to combat reward hacking in reinforcement learning.

May 25, 2026

Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

An introduction to Graph Neural Networks (GNNs), covering fundamental concepts like nodes, edges, and embeddings. This post delves into the core message-passing mechanism and provides a detailed overview of key architectures including GCN, GraphSAGE, GAT, GIN, and Graph Transformers, explaining their unique approaches and mathematical formulations.