JEPA-Reasoner: Decoupling Latent Reasoning from Token Generation

While Joint-Embedding Predictive Architecture (JEPA) has emerged as a powerful architecture for learning rich latent representations, it fundamentally lacks generative abilities. Meanwhile, current latent reasoning models remain limited by the token-by-token generation paradigm, which suffers from compounding errors and heavy context dependency. To address these limitations, we proposed JEPA-Reasoner, a novel JEPA-based architecture enhanced with generative ability for latent reasoning. We augment this architecture with a separate action-talker model, Talker, to reconstruct human-readable text from latent representations produced by the JEPA-Reasoner. Our work demonstrated that decoupling latent-space reasoning from token production enables JEPA-Reasoner to produce mixed latent vectors, laying a foundation for multi-threaded reasoning and achieving superior robustness against compounding errors in autoregressive generation.