Large language models excel at language but often struggle with structured reasoning tasks. This article explores Tiny Recursive Models (TRMs), a radically simpler approach that uses small neural networks with recursive refinement to outperform massive LLMs on puzzles like Sudoku, mazes, and ARC-AGI. By prioritizing iterative reasoning over scale, TRMs show that deep thinking can emerge from minimal architectures, challenging prevailing assumptions about model size and intell

RoboBallet explores a new approach to multi-robot task and motion planning by combining graph neural networks with reinforcement learning. Instead of decomposing planning into brittle subproblems, the system learns to coordinate multiple robotic arms directly through structured relational reasoning. Trained in simulation and generalizing zero-shot to real workcells, RoboBallet demonstrates how learning-based coordination can scale to industrial environments where classical pl

This article explores HunyuanImage 3.0, Tencent’s groundbreaking open-source multimodal model that unifies language understanding, visual reasoning, and image generation. It examines the model’s data pipeline, architecture, Chain-of-Thought workflow, and progressive training strategy, showing how HunyuanImage 3.0 achieves state-of-the-art text-to-image performance while enabling richer control, coherence, and creativity.

Nested Learning reframes neural models as multi-loop systems updating at different frequencies, revealing that depth stacking hides gradient mechanics and limits continual learning. It interprets optimizers like Momentum and Adam as associative gradient memories and introduces CMS for incremental abstraction. The HOPE module combines self-modification, multi-clock updates, and deep contextual compression, offering a white-box path beyond static backbones for long-context and

The article introduces weight-sparse transformers (models where most weights are zero) to make neural circuits interpretable. These models reveal clear, human-understandable algorithms for language tasks. Sparsity trades off raw capability for clarity, allowing researchers to fully trace mechanisms inside networks and bridge them to dense models for transparency in AI reasoning.

Google’s Project Suncatcher envisions moving AI computation into orbit, building constellations of solar-powered satellites equipped with TPUs and laser interlinks. By harnessing the Sun’s constant energy and future low-cost launches, the project proposes a scalable, space-based infrastructure for machine learning. It’s a blueprint for computing beyond Earth—where data centers orbit, powered by sunlight instead of fossil grids.