Trajectory-based reasoning model: PathThinker v0

We introduce PathThinker v0, a conceptual framework for neural networks that explicitly models information flow through trajectory-based reasoning.In this framework, trajectories are treated as first-class computational entities instead of natural language that exert active influence on the behavior of the network. This paradigm is designed to support several key properties: rapid adaptation from limited data, decentralized computation without reliance on global update mechanisms, and self-organizing learning processes in which the network models data actively rather than processing it passively. Although this work does not include an implementation yet, it proposes a theoretical foundation intended to guide and inspire our future development and practical instantiations of the PathThinker architecture.