RSS Amplifier

Podcast

State of AI

Summaries of Frontier AI Research Papers

stateai.substack.comSource feed ↗10 episodes

Live Last read · last published · next check

Written by

Latest episodes

Hierarchical Planning for Embodied Agents, Test-Driven Code Generation, and Memory-Efficient Diffusion Models

15 papers on smarter robots, cheaper diffusion, and the compute costs nobody was counting

A Breakthrough in Cost, Multi-Agent Reasoning Under Uncertainty, and Hallucination Mitigation Through Nested Memory

Welcome to today’s edition of State of AI 👋 And a warm welcome to our 26 new subscribers since last edition!

Metis: LLM Memory Without the Cumbersome Retrieval Stack

What happens when memory lives in the weights instead of a vector DB

Graph-Based Agentic AI, Adaptive Speculative Decoding, and Physics-Based Digital Twins

This edition showcases a fascinating convergence of three major trends reshaping AI systems: the infrastructure and governance challenges of building reliable, auditable agentic workflows; the practical engineering required to make inference faster and more cost-efficient at scale;

Gradient Bottlenecks, Token Learning Dynamics, and Agentic Self-Evolution: Understanding LLM Training and Deployment

Welcome to today‘s edition of State of AI 👋

Recursive Evidence Replay for Long-Context Reasoning, Multi-Turn Red Teaming for Code Security, and Neuron-Aware LLM Optimization

Welcome to today‘s edition of State of AI 👋

Benchmark Saturation, Self-Evolving Agents, and Trillion-Parameter Performance at 35B Scale

Welcome to today‘s edition of State of AI 👋

Diffusion Models Meet Reasoning: Learning to Schedule, Search, and Synthesize

This week brings advances in how we train and optimize AI systems across reasoning, robotics, and multimodal domains. We‘re seeing a shift toward learning when and how to use compute primitives, from scheduling token generation in diffusion models to adaptively invoking code in vision-language systems.

Audio Reasoning, Hallucination Mitigation, and Efficient Inference: From Chain-of-Thought Speech Models to INT8 Diffusion Transformers

Welcome to today’s edition of State of AI 👋

Hyperbolic Embeddings, Sparse Attention Kernels, and Diffusion-Based Retrieval: Three Breakthroughs in Scaling AI Systems

Welcome to today’s edition of State of AI 👋 This week brings a fascinating convergence: researchers are fundamentally rethinking how we represent knowledge in retrieval systems (moving from Euclidean to hyperbolic geometry), how we optimize inference efficiency (through programmable sparse attention and cross-layer routing), and how we augment language models with dynamic retrieval (leveraging…