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This paper introduces WhoWins, a benchmark of 1,080 code-scored scenarios and controlled experiments testing how instruction position, chat role, and label affect which conflicting instruction LLM agents follow across 14 open-weight models. Key findings show that earlier instructions within a message win 72-95% of conflicts, and that system-message policy lines surprisingly lose to later instructions in Qwen models but win on gpt-oss.