Memory is usually treated like office rent: annoying, expensive, but somehow always assumed to be available until the bill arrives. In search-based AI, that assumption is everywhere. Monte-Carlo Tree Search (MCTS) grows a tree of possible futures, samples outcomes, and gradually spends more attention on branches that look promising. Elegant. Effective. Also rather fond of storage. ...
Packing a Punch: How Model-Based AI Outperformed Decades of Sphere-Packing Theory Expensive experiments have a nasty habit: they punish enthusiasm. In many AI success stories, the hidden luxury is cheap feedback. Generate a million candidates, test them quickly, keep the survivors, call it discovery. This is the comfortable world of many coding benchmarks, puzzle solvers, and evolutionary search systems. It is not the world of high-precision semidefinite programming, where evaluating one candidate can take days and where a bad guess does not merely waste a GPU minute; it quietly burns a serious slice of the research budget. ...
TL;DR for operators Search-contempt is not “AI plays worse so it learns more”. That would be the lazy interpretation, and business strategy already has enough lazy interpretations wearing expensive shoes. The paper introduces a hybrid MCTS method for AlphaZero-like self-play systems. It behaves like standard PUCT search for the player to move, but at opponent nodes it eventually freezes the opponent’s visit distribution after a threshold, $N_{scl}$, and samples from that frozen distribution rather than constantly updating it toward stronger play.1 The effect is subtle but important: the system stops assuming the opponent will always improve its response with more search. ...