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Across a diverse set of domains, it is demonstrated that imperfect competition successfully sustains necessary agent profit margins while maintaining high task success rates at low costs.
This technical report details the infrastructure behind Game Arena and describes the three pilot game environments: Chess, Poker, and Werewolf, enabling a systematic study of models's strategic planning, adaptation, and robustness under uncertainty.
Experiments across three domains show that Pandora's Router matches the routing quality of exhaustive estimation, while querying the expensive estimator far less often.
This work proposes a lightweight two-feature embedding that captures fundamental behavioural demands: the entropy of the Nash equilibrium and the sensitivity of optimal responses to an opponent's action, and shows that this embedding reliably predicts performance changes on held-out games.
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