Paper: LLMs May Not Need Words to Think via Latent Space
CatAstro_Piyush · x · 2026-09-02
This paper proposes replacing the large language-model head with a smaller learned projector during reasoning. The model moves directly from one continuous embedding to another, forming a private chain of latent thoughts, with the normal language head returning only for the final answer.
Key Findings:
- This doesn't necessarily make individual attempts smarter (Pass@1 is often lower) but makes attempts less repetitive.
- Adding Gumbel noise to the latent space increases diversity.
- This suggests models can reason without translating thoughts into tokens, enabling "thought without words."
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