This method reduces token usage by roughly 40% and speeds up inference nearly 2x, which leads to approximately 3x cost savings. The researchers have released the associated code, papers, and models, stating that any existing large language model can be integrated into the framework. The team added that the internal latent thoughts remain readable to ensure the reasoning process is not an opaque black box.
Wednesday, October 21, 2026
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Together and Stanford Cut LLM Costs 3x With Hybrid Reasoning Agents
AI7 days ago
A new agent architecture increases AI task performance by 10.4% by eliminating the need to generate a text token for every internal thought. Developed by Together and Stanford, the system known as Hybrid Reasoning Agents allows AI to process complex calculations and logic in latent space before outputting text for tool usage or human interaction, moving away from the conventional requirement for models to 'think out loud' token by token.
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