Coconut Tree: Adaptive Branching for Continuous Latent Reasoning

Continuous latent reasoning replaces verbalized intermediate steps with hidden-state computation. This latent representation allows implicit representation of multiple reasoning chains in a single branch. However, we show that a single branch is a lossless implicit beam only when its state remains sufficient for every required future: when one state conflates beams requiring different futures, it cannot serve as a lossless implicit beam. To address this, we introduce Coconut Tree, a framework that determines when latent futures must remain separate and allocates reasoning width per problem. Across Coconut, CODI and CoLaR releases, the proposed approach consistently improves upon fixed-width and compute-matched baselines by several percentage points on average. To allocate reasoning width, Coconut Tree uses collision probabilities derived from the model's output-token distribution. A tight universal bound yields a stopping rule that exactly optimizes a penalised local coverage surrogate, whose only hyperparameter is a maximum branching width $B_{\max }$, without training or calibration. Predicted collision marginals track realized next-path gains, and the adaptive allocation consistently improves over the compute-matched fixed-width frontier. Live inference measurements on Coconut show that adaptive branching reduces wall-clock time by 13.6–60.0% and estimated dense-model FLOPs by 50.3–84.1% relative to using the fixed branching width (at $B=8$). The framework requires only separately continuable latent states and output-token probabilities, keeping the approach generic. Thus Coconut Tree explains both when continuous latent reasoning should branch and how much width branching justifies.