Simon Willison
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1 hour ago
Mira Murati's Thinking Machines Lab released Inkling, an open-weights multimodal transformer with 975 billion total parameters and 41 billion active parameters, licensed under Apache 2.0 and trained on 45 trillion tokens. A smaller version with 276 billion parameters is in testing. The model is positioned as a strong base for fine-tuning rather than a frontier model and competes with other open-weights alternatives like NVIDIA Nemotron and Gemma 4.
Sakana AI
Sakana AI announced the establishment of its RSI Lab in Tokyo to develop recursive self-improvement technology for AI systems that can autonomously improve themselves through efficient, sample-based optimization rather than compute scaling. The company has spent two years building practical systems like ShinkaEvolve (requiring only 150 samples to solve intractable problems) and ALE-Agent (outperforming 804 human specialists), positioning itself as a leader in sample-efficient self-improvement. By pursuing AI development under Japan's compute constraints, Sakana AI aims to create self-improving systems that generalize beyond hyperscale approaches and establish a sustainable path toward autonomous AI research capabilities.
Sakana AI
Sakana AI developed Sheaf-ADMM, a framework for multi-agent coordination where individual agents with limited information collaborate on complex tasks through local proposals, neighbor negotiation, and conflict memory. The framework achieved 93% accuracy on multi-agent Sudoku (versus 11% for baselines), 86% accuracy on domain-shifted MNIST classification, and matched baseline performance on maze pathfinding while using 8 times less communication bandwidth. The approach makes agent reasoning transparent and interpretable compared to traditional message-passing networks, with potential applications to distributed multi-agent AI systems.
