
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
Summaries of Frontier AI Research Papers
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15 papers on smarter robots, cheaper diffusion, and the compute costs nobody was counting

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

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

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;

Welcome to today‘s edition of State of AI 👋

Welcome to today‘s edition of State of AI 👋

Welcome to today‘s edition of State of AI 👋

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.

Welcome to today’s edition of State of AI 👋

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…