Stanford与Together AI提出潜空间混合推理智能体框架
Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's...
Stanford和Together AI让模型潜空间想事，调工具才输出文字，token省三到五成。
Stanford与Together AI等机构提出Hybrid Reasoning Agents，核心模块HybridLink负责在潜空间推理与文本输出之间切换。训练分两阶段：先用SFT让模型适配HybridLink，再用RL学习何时潜空间思考、何时以文本调用工具。在数学、科学、医学、搜索与智能体推理共11个基准上，平均领先前沿LLM智能体框架10.4%。Token用量减少35.5%至54.7%，速度提升1.8至2.6倍，成本下降2.7至3.7倍。其中14B版本在智能体推理任务上超过最强基线17.4%。
Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's...
Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's LLM agents must write out every reasoning step as text before they can search, run code, or take action. Those steps cost tokens and can take time. This paper proposes Hybrid Reasoning Agents, which reason internally in latent space and switch to text only to call a tool or give an answer. A small module called HybridLink moves the model between the two modes, and a working memory keeps both kinds of context across turns. Tool calls stay in plain text, and the latent steps can still be inspected. Training happens in two stages. SFT teaches the model to work with HybridLink, then RL teaches it when to think in latent space and when to act in text. Across 11 benchmarks in math, science, medicine, search, and agentic reasoning, it outperforms frontier LLM agents and agentic frameworks, both text-based and latent, by 10.4% on average. It also uses 35.5% to 54.7% fewer tokens, runs 1.8x to 2.6x faster, and costs 2.7x to 3.7x less. The 14B model beats the strongest agentic baseline by 17.4% on agentic reasoning tasks. arxiv.org/abs/2610.18861 Chat with Paper: academy.dair.ai/papers/hybrid-… 💬 4 🔄 0 ❤️ 6 👀 941 📊 5 ⚡
