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Introduces front-to-attractors (F2A), a new heuristic class for bidirectional search that reduces computational cost by evaluating distances to a small set of attractors instead of the full opposite frontier, achieving up to 11.2x fewer pairwise evaluations and 4.8x fewer node expansions than existing methods.
The article proposes that software engineering methodology should shift from a state perspective to a dynamical system perspective, emphasizing that attractor logic takes precedence over governance tools. In the AI era, it is necessary to explicitly model state space, attractors, trajectories, and controls to address architectural drift caused by AI as a high-frequency perturbation source.
This paper identifies a Möbius attractor and Cascade Supervision as key mechanisms for the emergence of superposition reasoning in transformers, closing a theoretical gap on gradient descent convergence for graph reachability tasks.