The Strange Case of Navier–Stokes
If AI can solve mathematical problems that have resisted humanity for decades, should we slow the machine—or rethink what mathematical discovery is for?
If AI can solve mathematical problems that have resisted humanity for decades, should we slow the machine—or rethink what mathematical discovery is for?
Why P vs NP may resist increasingly powerful AI and computation: the deeper challenge may be discovering structural principles that eliminate exponential search rather than merely searching faster.
AI is entering mathematics at unprecedented speed. The real question is not whether machines can solve problems, but who gets the credit—and whether AI enriches mathematics or turns it into a leaderboard.
A personal research journey from the first P vs NP exploration in 2001, through the 2006 Elsevier setback and a long pause, to a renewed AI-assisted investigation in 2026.
A sequel to the 2006 Euclidean TSP manuscript: reconstructing the geometric argument through one-to-one mapping, triangle inequality, four-city closure, recursive stitching, clusters, and a precise list of what is proved, what is evidence, and what still requires proof.
A 2026 formal expansion of Hemant Pandey’s 2006 P vs. NP manuscript, returning to its geometric seed—Hamiltonian paths, convex polygons and topology—and identifying the proof obligations needed to turn that intuition into a rigorous polynomial-time result.
Population averages can differ without defining individual ability. This article examines mathematics, chess, occupational interests, culture, biology and the extreme tails.
