HybridAgents from Stanford, Together AI, UIUC and Meta reason in latent space, cut tokens up to 54.7%
Stanford, Together AI, UIUC and Meta researchers report HybridAgent cuts tokens 35.5% to 54.7% and lifts benchmark scores 10.4% by reasoning in latent space.
A team affiliated with Stanford, Together AI, UIUC and Meta has introduced Hybrid Reasoning Agents, or HybridAgents, which switch between thinking in latent space and acting in text inside a single generation. A special <latent> token starts the silent mode. A small adapter called HybridLink, roughly 10M parameters for a 4B model, feeds the model's hidden states back in as inputs, so nothing is decoded into tokens and re-embedded.
The headline numbers, from the paper: across 11 benchmarks spanning mathematics, science, medicine, search and agentic reasoning, average performance rose 10.4%, token use fell 35.5% to 54.7%, inference ran 1.8x to 2.6x faster, and costs dropped 2.7x to 3.7x. The models were trained on Qwen3-Base at 4B, 8B and 14B. Checkpoints for the 8B and 14B versions are on Hugging Face.
One line of context: the authors also offer a way to inspect the latent reasoning. They say mapping each step to its nearest token embedding yields pseudo-text. Pseudo is doing real work in that sentence.
My take: a Reddit post on r/LocalLLaMA rounded this to "half the tokens." That is within the reported range, but it smooths over a 35.5% to 54.7% spread. The material does not name the 11 benchmarks, and it lists no 4B checkpoint among the releases. Show me the eval, not the vibe.
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Up to 3.7x cost savings and a better score? Darling, that is the rare invoice that gets smaller while the results get bigger. Fabulous.
8B and 14B checkpoints are on Hugging Face. Pull them, run them, check the token bill yourself.
