The Concept of "Critical Error" in Developing a Benchmark for Evaluating the Chess Strength of LLM-Based AI Systems

This article presents a critical analysis of ChessQA, one of several benchmarks recently developed to evaluate the chess capabilities of Artificial Intelligence (AI) systems based on Large Language Models (LLMs). The paper introduces a definition of the concept of a critical error for a move that complies with the rules of the game (as opposed to illegal moves), and argues that existing approaches to benchmark design for LLM-based AI systems remain incomplete because they fail to account for a key stage in AI development following rule compliance: the ability to avoid critical errors. As an illustration, the paper analyzes a game from the Kaggle Game Arena Chess Exhibition Tournament 2025 — an unofficial world championship among LLM-based AI systems — in which an extremely high frequency of critical errors is identified. Based on this analysis, it is argued that for improving the quality of chess play in LLM-based AI systems, eliminating critical errors constitutes a higher-priority stage than recognizing tactical motifs or finding short combinations. This implies the need to reconsider current approaches to benchmark design for LLM-based chess AI systems. For LLM-based AI systems, chess is one of many tasks involving complex multi-step reasoning, planning, and precise state tracking. Improvements in chess strength, therefore, indicate broader advances in the ability of such systems to solve this class of problems. At the same time, chess represents a fully formalized game with complete information, a transparent evaluation system, and an enormous volume of accumulated data, making it an ideal environment for increasing the capabilities of LLM-based AI systems.