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25 stories

DistillResearchUnderstanding Convolutions on Graphs Understanding the building blocks and design choices of graph neural networks.

DistillResearchA Gentle Introduction to Graph Neural Networks What components are needed for building learning algorithms that leverage the structure and properties of graphs?

DistillResearchDistill Hiatus After five years, Distill will be taking a break.

DistillResearchAdversarial Reprogramming of Neural Cellular Automata Reprogramming Neural CA to exhibit novel behaviour, using adversarial attacks.

DistillResearchWeight Banding Weights in the final layer of common visual models appear as horizontal bands. We investigate how and why.

DistillResearchBranch Specialization When a neural network layer is divided into multiple branches, neurons self-organize into coherent groupings.

DistillResearchMultimodal Neurons in Artificial Neural Networks We report the existence of multimodal neurons in artificial neural networks, similar to those found in the human brain.

DistillResearchSelf-Organising Textures Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

DistillResearchVisualizing Weights We present techniques for visualizing, contextualizing, and understanding neural network weights.

DistillResearchCurve Circuits Reverse engineering the curve detection algorithm from InceptionV1 and reimplementing it from scratch.

DistillResearchHigh-Low Frequency Detectors A family of early-vision neurons reacting to directional transitions from high to low spatial frequency.

DistillResearchNaturally Occurring Equivariance in Neural Networks Neural networks naturally learn many transformed copies of the same feature, connected by symmetric weights.

DistillResearchUnderstanding RL Vision With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.

DistillResearchCommunicating with Interactive Articles Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.

DistillResearchThread: Differentiable Self-organizing Systems A collection of articles and comments with the goal of understanding how to design robust and general purpose self-organizing systems.

DistillResearchSelf-classifying MNIST Digits Training an end-to-end differentiable, self-organising cellular automata for classifying MNIST digits.

DistillResearchCurve Detectors Part one of a three part deep dive into the curve neuron family.

DistillResearchExploring Bayesian Optimization How to tune hyperparameters for your machine learning model using Bayesian optimization.

DistillResearchAn Overview of Early Vision in InceptionV1 An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'

DistillResearchVisualizing Neural Networks with the Grand Tour By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.

DistillResearchThread: Circuits What can we learn if we invest heavily in reverse engineering a single neural network?

DistillResearchZoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.

DistillResearchGrowing Neural Cellular Automata Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns.

DistillResearchVisualizing the Impact of Feature Attribution Baselines Exploring the baseline input hyperparameter, and how it impacts interpretations of neural network behavior.

On the calendar

予定 · Conferences and launch events, this week's stories from each corner of the industry, and what its press is covering — in English

On view

This week, from that corner the newest stories from the corner of the industry you picked

The wire their own posts, translated

How code moves in 2026 from the industry's own reports

Sources

Fetched every three hours, linked to the original

About

Accept All is a magazine, a radar and a diary about artificial intelligence and the craft of code — read from Tokyo, for people who build.

The name is the button. Code is written by agents now and reviewed by a human thumb that hovers over Accept all a hundred times a day; the whole question of this decade is what, exactly, you just accepted. So we read the diff. A magazine, because every week one model, one tool or one idea is worth reading about slowly. A radar, because we count what the labs, the Valley, GitHub and the chip makers are talking about — releases, agents, open weights, compute, funding, rules — and show it as it moves. A diary, because a seal is set each day in that day's colour, and the days add up.

We read the research labs' own posts, the American and European tech press, the Japanese and Chinese developer sites and the newsletters people actually forward, and bring it together in English and Japanese. Everything here links back to the people who wrote it; the images belong to their authors.

Accept All will keep changing. New sources, new signals, new ways of looking — a little better each week, never finished. If something here made you open a terminal, that is enough.

Thank you for reading. ありがとうございます。