Can reasoning happen at the sentence level instead of tokens?

Current LLMs operate at the token level — every reasoning step is a next-token prediction. Meta's Large Concept Model (LCM) challenges this by operating at the sentence level, reasoning in an abstract embedding space (SONAR) where each "concept" corresponds to a sentence.

The architectural difference is fundamental. The LCM:

• Does not see tokens — it receives and produces sentence-level embeddings
• Is language-agnostic — the same reasoning process works for any language or modality because SONAR encodes meaning, not surface form
• Separates reasoning from instantiation — reasoning happens once in the abstract space; decoding to a specific language happens afterward and can target any language without re-running the reasoning

The hierarchical structure adds a planning layer. The LCM predicts a sequence of concepts auto-regressively until it produces a "break concept" — analogous to a paragraph break. At that point, a Large Planning Model (LPM) generates a plan to condition the LCM for the next sequence. This two-level architecture (sentence-level prediction + paragraph-level planning) is designed to produce more coherent long-form output than flat token-level generation.

The comparison to JEPA (LeCun, 2022) is instructive: both predict representations in embedding space rather than raw observations. But where JEPA emphasizes learning the representation space via self-supervision, LCM focuses on accurate prediction within an existing embedding space (SONAR). The embedding quality is assumed, not learned end-to-end.

This connects to the latent reasoning thread through a different mechanism. Can models reason without generating visible thinking tokens? achieves reasoning without tokens via recurrent depth in continuous space. LCM achieves it via sentence-level embeddings. Both challenge the assumption that verbalized token-by-token generation is necessary for reasoning, but from different angles: depth-recurrent models reason within a single token's representation; LCM reasons between sentence-level units.

The practical implication: if reasoning can happen at the concept level rather than the token level, then the verbalized chain-of-thought paradigm is not the only path to sophisticated reasoning. The question is whether sentence-level granularity captures enough structure for complex reasoning tasks, or whether some tasks require finer-grained (sub-sentence) reasoning steps.

Source: Sentiment Semantics Toxic Detections