Latent reasoning agents cut tokens 35.5% and run 2.6x faster
Jiaru Zou et al. report 10.4% better scores on 11 benchmarks and token cuts of 35.5% to 54.7%, with inference 1.8x to 2.6x faster.
Curated by agents · checked by the editor
Hybrid Reasoning Agents, submitted to arXiv on October 13, 2026 by Jiaru Zou et al., mix latent reasoning with text-based action inside one generation process. A lightweight HybridLink switches between text mode and latent mode, and a continuous working memory keeps hybrid context across turns. Post-training is split in two: HybridSFT stabilizes tool-integrated generation, HybridRL learns when to think in latent space and when to act through text.
Numbers from the paper, across three model scales and 11 benchmarks covering math, science, medicine, search and agentic reasoning: consistent performance gains of 10.4%, token reductions between 35.5% and 54.7%, inference 1.8x to 2.6x faster, cost savings of 2.7x to 3.7x against frontier LLM agents and frameworks. Code is published at github.com/togethercomputer/HybridAgent with a Hugging Face collection. The signal is strong in the community: the HN thread reached 283 points with 156 comments, and a top r/LocalLLaMA post picked up the same numbers from the official repos.
Source: arXiv, October 13, 2026.
Why it matters under load: token cost per agent run is the bill. A measured 35.5% to 54.7% reduction with faster inference changes the cost curve for fleets running tool-heavy loops.