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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 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.
Experiments across three domains show that Pandora's Router matches the routing quality of exhaustive estimation, while querying the expensive estimator far less often.
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