Think in Latent, Explain in Language: Self-Explainable Latent Reasoning

Researchers introduced Self-Explainable Latent Reasoning (SELR), a unified framework that trains a single model to perform latent reasoning while decoding its own latent representations into human-readable steps, eliminating the need for external decoders. The method, validated on large language models and vision-language models, achieves superior token efficiency and accuracy compared to baselines while providing self-contained explainability.

arXiv:2608.13570v1 Announce Type: new Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought CoT , offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings. However, compressing reasoning into the latent space renders the thinking opaque, hindering its interpretability. Current methods present a stark trade-off: they either function as unexplainable ''black boxes'' e.g., Coconut , where the latent reasoning is not human-readable, or rely on separate post-hoc decoders for explainability e.g., Heima , introducing architectural overhead and decoupling the explanation from the actual reasoning process. In this work, we present a unified framework for Self-Explainable Latent Reasoning SELR that trains a single model to perform efficient and inherently explainable latent reasoning. Our core contribution is a novel multi-task training objective that optimizes for two goals simultaneously: 1 an Answer Loss that optimizes the latent reasoning trajectory to produce accurate final answers, and 2 a CoT Loss that explicitly trains the same model to decode its own latent representations back into human-understandable reasoning steps. This design ensures that generated latent representations are both task-effective and semantically interpretable, eliminating the need for external decoders. We validate the effectiveness of SELR on both Large Language Models LLMs and Vision-Language Models VLMs , demonstrating that SELR achieves superior token efficiency and accuracy compared to baselines, while uniquely providing self-contained explainability without auxiliary models. Project page is available at https://jasondayuan.github.io/SELR/.