Why AI Beats Fixed Rules for Database Deadlocks

This experiment demonstrates that LLMs can apply nuanced, multi-dimensional reasoning to database problems where fixed rules fail. Where traditional deadlock victim selection relies on single factors like lock count or transaction age, an LLM considers multiple variables and openly admits when tiebreaking is arbitrary rather than claiming false certainty. The findings suggest AI-driven decisions could improve critical database operations, though production deployment would require addressing latency challenges.