Reorganizing Latent Reasoning in Large Language Models

Complex reasoning requires coordinating cognitive functions, such as retrieval, semantic understanding and math calculation, in a task-dependent order. Chain-of-Thought (CoT) improves reasoning in large language models (LLMs) by making intermediate steps explicit, yet leaves the model's latent computation constrained by a fixed layer-wise computational order. In this work, we investigate whether reorganizing latent computation can improve reasoning.We introduce Chain-of-Cognition (CoC), a framework that derives a cognitive ordering from human-verified decompositions of reasoning tasks, localizes internal regions associated with each cognitive function, and dynamically reorganizes computation across these regions to follow that ordering. For example, semantic understanding often precedes math calculation, yet its associated heads can lie deeper; CoC selectively routes semantic representations back to mathematical regions, aligning information flow with the functional dependencies of the task. Across multiple LLMs and diverse reasoning benchmarks, CoC consistently improves reasoning performance without updating model parameters. Further experiments on vision-language models demonstrate its applicability to multimodal reasoning. These results suggest that effective reasoning depends not only on the capabilities available within a model, but also on how they are coordinated and composed during latent computation.