SELR: A Unified Framework for Self-Explainable Latent Reasoning
A recent study published on arXiv (2608.13570) presents Self-Explainable Latent Reasoning (SELR), a cohesive framework designed to train a single model for efficient and inherently interpretable latent reasoning. While latent reasoning condenses extensive reasoning into compact embeddings, enhancing computational efficiency compared to text-based Chain-of-Thought (CoT), it often lacks transparency. Current approaches either act as unexplainable black boxes (like Coconut) or depend on separate post-hoc decoders (such as Heima), which add architectural complexity and separate explanation from reasoning. SELR overcomes these challenges with an innovative multi-task training objective that simultaneously enhances efficiency and explainability. This paper is authored by a team of researchers and was announced as a cross-type submission on arXiv.
Key facts
- Paper arXiv:2608.13570 introduces Self-Explainable Latent Reasoning (SELR).
- SELR trains a single model for efficient and inherently explainable latent reasoning.
- Latent reasoning compresses reasoning into embeddings, offering computational efficiency over text-based CoT.
- Existing methods like Coconut are unexplainable black boxes.
- Existing methods like Heima use post-hoc decoders, adding overhead and decoupling explanation.
- SELR uses a novel multi-task training objective optimizing for two goals simultaneously.
- The paper was announced as a cross-type submission on arXiv.
- The announcement type is 'cross'.
Entities
Institutions
- arXiv