AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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 proof of concept demonstrates a potentially scalable approach for efficient cosmological parameter inference with large populations of galaxy-scale lenses observed in future surveys.
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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