Nobel Prize in Physics Awarded to John Clarke, Michel Devoret and John Martinis
The 2025 Nobel Prize in Physics was awarded to John Clarke, Michel H. Devoret, and John M. Martinis for their groundbreaking experiments demonstrating macroscopic quantum tunnelling and energy quantisation in superconducting circuits, revealing quantum properties on a human scale.
OpenZL: An open source format-aware compression framework
OpenZL is a novel open source compression framework that achieves lossless compression for structured data, outperforming traditional compressors by leveraging data structure awareness to enhance compression ratios and speeds.
Introducing CodeMender: an AI agent for code security
CodeMender is an AI agent that automatically identifies and patches software vulnerabilities, having already contributed 72 security fixes to open-source projects, including those with 4.5 million lines of code.
How does gradient descent work?
Gradient descent operates effectively in deep learning by navigating regions of weight space where the sharpness (S(w)) remains below the critical threshold (2/\eta), despite oscillating dynamics that suggest instability.
What makes 5% of AI agents work in production?
Only 5% of AI agents succeed in production due to inadequate context engineering and system design, highlighting the need for robust scaffolding around AI models rather than just relying on their intelligence.
How to tile matrix multiplication (2023)
Tiling matrix multiplication optimizes resource utilization across power, memory, and compute, significantly reducing latency for models like transformers that rely on dense matrix operations.
Less Is More: Recursive Reasoning with Tiny Networks
The Hierarchical Reasoning Model (HRM) utilizes two small neural networks to outperform Large Language Models (LLMs) on complex tasks like Sudoku and ARC-AGI, demonstrating that smaller models can achieve significant results with limited data.
Writing high-performance matrix multiplication kernels for Blackwell
High-performance matrix multiplication kernels for Blackwell can be developed through iterative enhancements, transforming a basic implementation into one that rivals optimized libraries like cuBLAS and CUTLASS, achieving up to 109.6% utilization of cuBLAS performance with advanced techniques such as grid tiling and collective MMAs.
Compressed Convolutional Attention: Efficient Attention in a Compressed Latent Space
Compressed Convolutional Attention (CCA) introduces a method that down-projects queries, keys, and values, enabling efficient attention operations within a shared latent space, significantly reducing parameters and computational costs.
[R] Predictive control of generative models
Generative models, particularly flow models, can be enhanced by integrating exogenous inputs through predictive control algorithms like MPC and MPPI to guide image generation from Gaussian noise.
Show HN: Arc – high-throughput time-series warehouse with DuckDB analytics
Arc Core is a high-performance time-series data warehouse leveraging DuckDB, Parquet, and MinIO, achieving 1.89M records/sec with the MessagePack binary protocol, making it significantly faster than traditional methods.
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
ACE (Agentic Context Engineering) enhances large language models by treating contexts as evolving playbooks, allowing for the accumulation and refinement of strategies without losing critical details.
SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
SwiReasoning introduces a dynamic switch between explicit and latent reasoning, enhancing token efficiency and accuracy in large language models (LLMs) by addressing challenges in training-free settings.
Test-Time Scaling in Diffusion LLMs via Hidden Semi-Autoregressive Experts
dLLMs exhibit a unique property where they learn a mixture of semi-autoregressive experts, allowing for diverse generation behaviors that can be leveraged during inference.