Completeness Is Not Optional — Why Game-Playing AI Finally Learned to Finish What It Starts
Opening — Why this matters now The AI industry has developed an unfortunate habit: celebrating systems that usually work. From large language models hallucinating citations to reinforcement learning agents missing obvious optimal moves, the pattern is familiar—impressive performance, quietly unreliable guarantees. This paper, “Completeness of Unbounded Best-First Minimax and Descent Minimax” fileciteturn0file0, addresses a deceptively narrow issue in game search algorithms. But underneath, it tackles something far more uncomfortable: ...