Evaluating Model Behavior Through Chess

Using the structured and stateful environment of chess to evaluate AI models reveals insights into their behavior, risk management, and decision-making over time, which static benchmarks often miss. By orchestrating chess tournaments through the Python chess library and Dagster, the study examines how models handle repeated states, risk versus safety, and failure modes. Initial experiments show that random agents perform poorly, with games often resulting in draws due to move limits, while the advanced chess engine Stockfish consistently defeats both random agents and general-purpose AI models. When general-purpose models like OpenAI's GPT-4o and Anthropic's Claude compete, games frequently end in draws due to fivefold repetition, highlighting a tendency towards risk-avoidance rather than strategic aggression. The findings indicate that while general-purpose models can follow basic heuristics, they lack the specialized evaluation functions and incentives necessary for domain-specific tasks like chess, unlike fine-tuned engines such as Stockfish. This evaluation approach underscores the value of dynamic assessments in understanding AI model behavior beyond static performance metrics.