Latent Role Separation for Continuous CoT via Hierarchical Latent Reasoning

Continuous chain-of-thought (CoT) compresses a verbal trace into a short sequence of vectors. Those vectors must both discover a decomposition and carry out the computation, yet existing methods leave their computational roles unspecified. We introduce Hierarchical Latent Reasoning (HLR). A small student first emits one plan vector and then K detail vectors. Every detail is generated from a prefix that already contains the plan, so the plan-to-detail edge is present in the student forward pass and remains active at inference. The projected hierarchy is read by a 7B teacher. Training replaces gold plan and detail text from left to right with the corresponding projected student states, aligns each latent to its role, and scores one answer path per example. We compare HLR with six latent-reasoning baselines on two 7B backbones and five reasoning benchmarks, under a shared five-slot budget for every continuous method. Under this protocol, HLR reaches state-of-the-art accuracy. The gains are largest where one strategy is reused across successive state updates, so the plan stays available while the details execute. In the hierarchy ablation, removing the plan-to-detail edge accounts for the larger drop, and replacing the exclusive answer path accounts for a smaller one. CODI and SemCoT lead the remaining Mistral cells.