Structured Latent Reasoning: Refactoring Reasoning with Reusable Primitives for Multimodal LLMs

Chain-of-Thought reasoning has significantly advanced problem solving in multimodal large language models (MLLMs), yet autoregressive generation of lengthy reasoning traces incurs substantial token-level redundancy and computational overhead. Existing approaches compress reasoning into continuous latent states or discrete latent representations, but typically represent heterogeneous reasoning content within a shared latent space, making it difficult to efficiently encode both reusable reasoning patterns and instance-specific information. In this paper, we present **SLR**, a **S**tructured **L**atent **R**easoning framework that separates *reusable reasoning operations* from their *instance-specific executions*. SLR segments reasoning traces into unit spans, decomposes each span into an abstract operation and its concrete execution, and clusters recurring operations to construct a compact discrete *Reasoning Codebook*. During training, operation descriptions are replaced by discrete codebook tokens and interleaved with explicit execution text, allowing recurring procedural patterns to be represented compactly while preserving problem-specific computations. We evaluate SLR across diverse multimodal reasoning benchmarks with three MLLM configurations. Compared with standard SFT, SLR reduces mean output length by 10-35%, while matching or exceeding standard SFT accuracy in all settings. These results demonstrate that explicitly separating reusable reasoning structure from instance-specific execution provides an effective approach to reducing reasoning redundancy while maintaining multimodal reasoning performance.