SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens

Nikita Dragunov; Temurbek Rahmatullaev; Elizaveta Goncharova; Nikita Kurdiukov; Aysel Mirzoeva; Anna Borisiuk; Andrey Kuznetsov; Anton Razzhigaev

arXiv · 2508.05305

SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens

Nikita Dragunov·Temurbek Rahmatullaev·Elizaveta Goncharova·Nikita Kurdiukov·Aysel Mirzoeva·Anna Borisiuk·Andrey Kuznetsov·Anton Razzhigaev

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Abstract

The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-squared error or diffusion objectives. We present SONAR-LLM, a decoder-only transformer that "thinks" in the same continuous SONAR embedding space, yet is supervised through token-level cross-entropy propagated via the frozen SONAR decoder. This hybrid objective retains the semantic abstraction of LCM while eliminating its diffusion sampler and restoring a likelihood-based training signal. Across model sizes from 39M to 1.3B parameters, SONAR-LLM attains competitive generation quality. We report scaling trends, ablations, benchmark results, and release the complete training code and all pretrained checkpoints to foster reproducibility and future research.

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Nikita Dragunov, Temurbek Rahmatullaev, Elizaveta Goncharova, Nikita Kurdiukov, Aysel Mirzoeva, Anna Borisiuk, Andrey Kuznetsov, Anton Razzhigaev. 2026-05-25. SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens. https://arxiv.org/abs/2508.05305

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