SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings
  and Speaks in Tokens
Authors: name: Nikita Dragunov, name: Temurbek Rahmatullaev, name: Elizaveta Goncharova, name: Andrey Kuznetsov, name: Anton Razzhigaev

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.