The Architecture of the Governor: Consistency-Maximized Agents and the Ridge Blind Spot

We propose that the current plateau in Large Language Model reasoning performance on certain problem classes represents a structural consequence of alignment-induced rigidity rather than a training limitation. By optimizing for consistency and safety, current architectures effectively implement a topological governor that prunes the exploratory search paths required to navigate problems exhibiting navigability fracture (characterized by expansion metric Δ ≈ 0.10–0.15, as established in companion work; Ransome et al., 2025, doi:10.5281/zenodo.18380936). We demonstrate that this creates a fundamental architectural trade-off: systems optimized to never be locally wrong cannot learn to be globally right on problems where correctness requires exploring temporarily incorrect states. This explains persistent ~85-90% performance ceilings across multiple reasoning domains and predicts which architectural modifications will (and will not) improve performance on this problem class. We conclude that solving "Ridge" problems requires accepting controlled plasticity—the ability to hold inconsistent hypotheses in tension—rather than maximizing single-path coherence.