SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens
arXiv:2508.05305v2 Announce Type: replace 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 m
This development addresses known limitations in previous Large Concept Models (LCMs), aiming to improve efficiency and training signals for a new generation of AI architectures.
It introduces a potentially more robust and efficient hybrid architecture for large language models, offering better semantic understanding without complex diffusion samplers.
The method of training and inference for advanced language models shifts towards combining continuous semantic understanding with traditional token-level supervision, potentially leading to more capable and less computationally intensive models.
- · AI developers
- · Generative AI platforms
- · Cloud AI providers
- · Developers reliant solely on diffusion-based models
- · Proprietary models with less efficient architectures
Improved performance and efficiency of large language models for various applications.
Accelerated development of more sophisticated AI agents and autonomous systems.
Enhanced automation capabilities across creative and analytical sectors, driving further AI integration into workflows.
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