AI Research Today

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 elegant mathematical framework. In this first episode of a multi-part series, we begin working through the excellent MIT lecture notes An Introduction to Flow Matc...

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 by generating more tokens. GRAM takes a different approach: instead of extending a chain of thought, it repeatedly refines an internal latent state. T...

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 domains like healthcare, logistics, manufacturing, customs processing, and more. The paper introduces OccuBench, a large-scale benchmark spanning 100 professiona...

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 RAG), GradMem introduces a different idea— learn a compact memory representation at inference time . Using a few steps of gradient descent, the model “writes” impo...

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 information loss. We dig into the paper: Read the paper on arXiv At the core of the proof is a surprisingly deep mathematical idea: Transformers are real analytic...

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 approaches still assume that effective reasoning requires relatively large adaptation layers. This new paper challenges that assumption by asking a provocative...

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 difficult. While search methods like Monte Carlo Tree Search (MCTS) offer a way to explore alternatives, they typically rely on sparse rewards and fail to fully ex...

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 . Standard reinforcement learning (RL) methods help language model agents learn to interact with environments and solve multi-step tasks, but they often struggle...

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 reinforcement learning methods often plateau after extensive training because they rely on sparse exploration and limited rollouts, leaving critical reasoning paths...

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 Singular Value Fine-Tuning (SVF) , a parameter-efficient fine-tuning technique that decomposes each weight matrix with Singular Value Decomposition (SVD) and the...

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 progress, fundamental questions remain: How do they truly memorize? How do they improve over time? And why does in-context learning emerge at scale? Nested Learning pr...

Send us Fan Mail https://arxiv.org/pdf/2511.10395 What if AI agents could teach themselves? In this episode, we dive into AgentEvolver, a groundbreaking framework from Alibaba's Tongyi Lab that flips the script on how we train autonomous AI agents. Traditional agent training is brutal: you need manually crafted datasets, expensive random exploration, and mountains of compute. AgentEvolver int...