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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 paper argues that agentic markets are likely to possess features that reduce the price-of-anarchy, and shows that both features give rise to welfare lower bounds that are significantly better than the well-known general case of $1 - 1/e$.
This work proposes Collaborative Evaluation (CollabEval), a simple, effective, and principled method for exploiting dependencies between historical runs of different models on the same tasks to improve statistical efficiency.
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