Rohan Paul (@rohanpaul_ai) · 10/15/2026, 01:09:06
Stanford and Together AI Introduce HybridAgent: Latent Reasoning Cuts Decoding Compute by 62.5% While Boosting GAIA Scores
Stanford University and Together AI have introduced HybridAgent, a novel architecture that performs most reasoning in internal latent vectors, switching to text only for tool calls and critical decisions. This approach reduces decoding compute by 62.5% to 72.9% while achieving a higher score of 52.4 on the GAIA benchmark compared to 43.7 for text-only agents, alongside a 2.1x speedup. The study demonstrates that silent, vector-based reasoning enhances both efficiency and accuracy without sacrificing interpretability.
SOURCE COVERAGEFull Original Report
Rohan Paul (@rohanpaul_ai) · 2026-10-14 17
New Stanford and Together AI paper finds that cheaper reasoning need not cost accuracy, as hybrid agents cut decoding compute by 62.5% to 72.9% and still scored higher.
Shows that agents run faster and score higher when most thinking happens in hidden vectors, not written text.
Today's agents write every reasoning step as text tokens, which is slow and costly. Models that reason in their internal vectors are faster, but they cannot call tools and humans cannot read what they think.
HybridAgent adds a small adapter that feeds the model's internal state back as its next input, so it can think without writing words. It switches to text for tool calls and key decisions, and its silent steps can still be decoded into readable words.
On GAIA, the 14B version scored 52.4, above 43.7 for the same model trained as a text-only agent with ARPO. It also ran 2.1× faster than matched text agents.
If your agent spends tokens on routine reasoning, try keeping that reasoning internal and saving text for tool calls and steps humans must check.
↳ 引用 @Jiaru_Zou (Jiaru "Rubin" Zou): Your LLM agent has to think out loud, token by token, before it can search, run code, or act in the world.
We humans don’t. We ponder in silence and speak when it matters.
Human language was never meant to be the limit of machine thought.
Introducing 🌗Hybrid Reasoning Agents
- Think in latent space, act through text
- Two modes, one unified multi-turn flow
- No black box, fully readable latent thoughts
- Any LLM can become a HybridAgent
