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We introduce DexHoldem, a real-world system-level benchmark built around Texas Hold'em dexterous manipulation with a ShadowHand. DexHoldem provides 1,470 teleoperated demonstrations across 14 Texas Hold'em manipulation primitives, a standardized physical policy benchmark, and an agentic perception benchmark that tests whether agents can recover the structured game state needed for embodied decision making. On primitive execution, $\\pi_{0.5}$ obtains the highest task completion rate ($61.2\\%$), while $\\pi_{0.5}$ and $\\pi_0$ tie on scene-preserving success rate ($47.5\\%$). 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