Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's LLM agents must write out every reasoning…

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. https://arxiv.org/abs/2610.18861 Chat with Paper: https://academy.dair.ai/papers/hybrid-reasoning-agents-2610.18861

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Source: DAIR.AI (X) | 2026-10-14