Reasoning Topology

Reasoning topology is the study of how inference paths are structured in the high-dimensional state spaces of large language models. The term treats reasoning not as a sequence of logical steps but as a trajectory through a network of connected regions, where each region corresponds to a domain of competence and the connections between regions determine what kinds of reasoning transfers are possible.

The central claim is that a model's reasoning capacity is constrained by the connectivity of its latent space, not merely by its parameter count or training data volume. Prompting techniques can be understood as path-pruning methods that steer generation away from locally-optimal but globally-incorrect solutions. This perspective connects artificial intelligence to the study of latent space geometry.