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Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.
OTF-CBM reveals that dynamic transport processes can significantly enhance the interpretability and accuracy of vision-language models, outperforming static alignment methods.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
