Hybrid Reasoning Agents paper: latent+text hybrid reasoning with big token savings
alex_verem · x · 2026-11-05
This post links the arXiv source of the Hybrid Reasoning Agents paper (arXiv:2610.18861, Jiaru Zou, James Zou, et al., Stanford/Together AI/UIUC/Meta).
The paper introduces agents that navigate between latent reasoning and text interaction in a unified generation process:
- HybridLink: a lightweight module mapping last-layer hidden states back to input embedding space, enabling fast, inspectable latent thinking with returns to text mode for tool calls
- Continuous working memory preserves lossless hybrid context across turns
- Post-training: HybridSFT stabilizes tool-integrated hybrid generation; HybridRL scales reasoning by learning when to think latently vs. act in text
- Across 3 model scales and 11 benchmarks, HybridAgent improves performance by 10.4% vs. frontier LLM agents and frameworks, with 35.5%-54.7% token reduction, 1.8x-2.6x faster inference, and 2.7x-3.7x cost savings
Code and data are open-sourced at github.com/togethercomputer/HybridAgent.
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