斯坦福与Together AI提出HybridAgent：混合智能体解码算力降72.9%
斯坦福与Together AI：混合智能体解码算力降72.9%
斯坦福与Together AI的论文提出HybridAgent，通过小型适配器把模型内部状态回喂为下一步输入，让大部分推理在隐藏向量中完成，解码算力降低62.5%至72.9%。在GAIA上，14B版本得分52.4，高于纯文本智能体ARPO的43.7，运行速度也快2.1倍。该方案仅在工具调用和关键决策时切换为文本，静默步骤仍可解码为可读文字。
New Stanford and Together AI paper finds that cheaper reasoning need not cost accuracy, as hybrid agents cut decoding compute by 62.5% to 72.9% and still scored higher.
Shows that agents run faster and score higher when most thinking happens in hidden vectors, not written text.
Today's agents write every reasoning step as text tokens, which is slow and costly. Models that reason in their internal vectors are faster, but they cannot call tools and humans cannot read what they think.
HybridAgent adds a small adapter that feeds the model's internal state back as its next input, so it can think without writing words. It switches to text for tool calls and key decisions, and its silent steps can still be decoded into readable words.
On GAIA, the 14B version scored 52.4, above 43.7 for the same model trained as a text-only agent with ARPO. It also ran 2.1× faster than matched text agents.
If your agent spends tokens on routine reasoning, try keeping that reasoning internal and saving text for tool calls and steps humans must check.
来源：Rohan Paul · x.com