Hybrid Reasoning Agents improve LLM performance by 10.4% and reduce costs up to 3.7x
Researchers introduce Hybrid Reasoning Agents, a new class of LLM agents that switch between latent‑space reasoning and text‑based interaction. A lightweight HybridLink connects the two modes while a continuous working memory preserves lossless hybrid context across turns, allowing tool‑integrated capabilities and inspectable internal thoughts.
Key points
- HybridAgents combine latent reasoning with text interaction via a lightweight HybridLink and continuous working memory.
- Across three model scales and eleven benchmarks, HybridAgent shows 10.4% higher performance and 35.5%-54.7% token reduction.
- Inference speed improves 1.8x‑2.6x and cost savings reach 2.7x‑3.7x versus prior LLM agents.
The team evaluates HybridAgent at three model scales on eleven benchmarks covering mathematics, science, medicine, search, and agentic reasoning. Compared with existing text‑only and latent‑only agents, HybridAgent delivers a 10.4% performance gain, cuts token usage by 35.5%‑54.7%, speeds inference 1.8x‑2.6x, and saves 2.7x‑3.7x in cost. Post‑training methods HybridSFT and HybridRL teach the backbone to coordinate with HybridLink and to decide when to think in latent space versus act through text. Code and data are released publicly.
Hybrid Reasoning Agents: LLM agents that think in latent space, act through text
arxiv.org · 13 October 2026
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