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AI Research Today

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Latest episodes

  • How Does a Diffusion Model Work, Part 2: Flow Matching and Diffusion

    August 7, 2026 · 37 min

    Send us Fan Mail Diffusion models have become the foundation of modern generative AI, powering state-of-the-art systems for image generation, video synthesis, protein design, and more. In the first episode of this series, we introduced…

  • How Does a Diffusion Model Work, Part 1: Introduction

    July 2, 2026 · 22 min

    Send us Fan Mail Diffusion models have become the foundation of modern generative AI, powering state-of-the-art systems for image generation, video synthesis, protein design, and more. But behind the impressive demos lies a surprisingly…

  • Generative Recursive Reasoning

    June 3, 2026 · 37 min

    Send us Fan Mail In this episode, we explore the paper "Generative Recursive Reasoning (GRAM)," a fascinating new approach to AI reasoning co-authored by Yoshua Bengio and researchers from Mila and Samsung AI. Most modern AI systems reason…

  • OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

    May 12, 2026 · 33 min

    Send us Fan Mail In this episode, we break down the new paper “OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation,” which explores how AI agents can be benchmarked across real occupational…

  • GradMem: Teaching LLMs to Remember (Without Retraining)

    April 23, 2026 · 29 min

    Send us Fan Mail In this episode, we break down GradMem, a new approach to memory in large language models: https://arxiv.org/pdf/2603.13875v1 Instead of relying on the transformer KV cache or repeatedly reprocessing documents (like in…

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  • Language Models are Injective and Hence Invertible

    March 23, 2026 · 27 min

    Send us Fan Mail In this episode, we break down a fascinating new result from recent research: that modern Transformer language models are almost surely injective—meaning different prompts map to unique internal representations, with no…

  • Learning to Reason in 13 Parameters

    February 16, 2026 · 27 min

    Send us Fan Mail Link to arxiv: https://arxiv.org/pdf/2602.04118 Large language models have recently shown impressive reasoning abilities, often learned through reinforcement learning and low-rank adaptation techniques like LoRA. But these…

  • SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search

    January 26, 2026 · 29 min

    Send us Fan Mail Large Language Models often struggle with complex planning tasks that require exploration, backtracking, and self-correction. Once an LLM commits to an early mistake, its linear chain-of-thought reasoning makes recovery…

  • Meta-RL Induces Exploration In Language Agents

    January 12, 2026 · 29 min

    Send us Fan Mail Episode Paper: https://arxiv.org/pdf/2512.16848 In this episode, we dive into a cutting-edge AI research breakthrough that tackles one of the biggest challenges in training intelligent agents: how to explore effectively…

  • DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

    December 29, 2025 · 37 min

    Send us Fan Mail In this episode, we unpack DeepSearch, a new paradigm in reinforcement learning with verifiable rewards (RLVR) that aims to overcome one of the biggest bottlenecks in training reasoning-capable AI systems. Traditional…

  • Transformer-Squared: Self-Adaptive LLMs

    December 11, 2025 · 40 min

    Send us Fan Mail In this episode we’re diving into “Transformer-Squared: Self-Adaptive LLMs” — a new framework for adapting large language models to unseen tasks on the fly by tuning only a small part of their weights. The central idea is…

  • Nested Learning: The Illusion of Deep Learning Architectures

    December 1, 2025 · 50 min

    Send us Fan Mail  NL.pdf In this episode, we dive into Nested Learning (NL) — a new framework that rethinks how neural networks learn, store information, and even modify themselves. While modern language models have made remarkable…

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About the show

AI Research Today unpacks the latest advancements in artificial intelligence, one paper at a time. We go beyond abstracts and headlines, walking through architectures, experiments, training details, ablations, failure modes, and the implications for future work. Each episode will choose between one and three new, impactful research papers and go through them in depth. We will discuss the papers at the level of an industry practitioner or AI researcher. If you want to understand the newest topics in AI research but don't have the time to dig through the papers yourself, this is your solution.