A new arXiv preprint introduces HybridAgents, a framework that lets AI agents toggle between a latent mode for internal reasoning without text generation and a text mode reserved for external actions. Testing was conducted on Qwen3-Base models of 4B, 8B, and 14B parameters across 11 benchmarks covering math, science, and medicine. The results show compute cost reductions of 63% to 73%, with average performance gains of 10.4% over existing LLM agents. Authors include Jiaru Zou of Stanford and Together AI, who released the code and models on GitHub and Hugging Face. Caveats include the lack of peer review and the fact that testing covered only models up to 14B parameters.
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