Frank Quisinsky ran a test comparing Dragon by Komodo 3.3 against its new MCTS variant. The Monte Carlo Tree Search version crushed the standard engine by 52 Elo points. That's a massive gap. Quisinsky called the MCTS implementation "really very very strong," which says something coming from someone who tests engines constantly.
MCTS represents a different computational approach than traditional alpha-beta search. Instead of evaluating every move deeply, MCTS explores positions probabilistically, simulating many games forward. For Komodo, this shift delivered real results. A 52-point jump puts Dragon MCTS in elite company among super-engines.
The test matters because engine development never stops, and when a new methodology produces these kinds of gains, it changes what's possible. Other engine developers will study this. Players using Komodo get a stronger analysis tool. And the chess engine world gets proof that MCTS, long established in Go engines, works at the highest levels of chess computation too.
This is raw progress in silicon chess strength.
WHY IT MATTERS: A 52-Elo jump from a single algorithmic change shows engine development is still breaking new ground, and MCTS could reshape how modern engines search positions.