New reasoning framework bridges language and latent computation in LLMs
Researchers propose J-CoT, a reasoning framework that bridges the gap between fully verbalized chain-of-thought prompting and opaque latent reasoning. Rather than forcing all intermediate steps into natural language or passing undifferentiated hidden vectors, J-space grounds reasoning in a vocabulary-indexed structure that lets models selectively propagate only relevant information between computation steps. This addresses a real bottleneck in current reasoning architectures: the false choice between interpretability and efficiency. For practitioners building reasoning systems, this suggests a path toward more efficient inference without sacrificing the auditability that natural-language reasoning provides.62
