hybrid-reasoning
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Hybrid Reasoning Agents: LLM agents that think in latent space, act through text
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Hybrid Reasoning Agents, submitted to arXiv on October 13, 2026, combine latent reasoning with text-based interaction in a single language-model agent. The system is designed to let an agent use latent representations for internal reasoning while continuing to use text to call tools and interact with external environments. Its latent thoughts are intended to remain directly inspectable. The architecture uses a lightweight component called HybridLink to switch between text and latent modes during generation. A continuous working-memory mechanism retains hybrid context across multiple turns. The researchers also describe two post-training methods: HybridSFT, which trains a backbone model to coordinate with HybridLink during tool-integrated generation, and HybridRL, which learns when to reason in latent space and when to act through text. Across three model scales and 11 benchmarks in mathematics, science, medicine, search, and agentic reasoning, the authors report that HybridAgent improved performance by 10.4% relative to frontier LLM agents and agentic frameworks using text-based or latent methods. They also report 35.5% to 54.7% lower token use, 1.8× to 2.6× faster inference, and 2.7× to 3.7× cost savings. The authors state that the project’s code and data are available online.