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CoFL-S achieves superior navigation performance by leveraging language-conditioned flow fields, outperforming traditional action representations in both simulation and real-world applications.
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.
Automating LLM fine-tuning is now possible: a multi-agent system, TREX, matches or exceeds human performance on a diverse set of real-world tasks.
