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Agentic Commerce World reveals that process-level evidence is crucial for accurately evaluating AI agents in dynamic market environments, challenging traditional reliance on final outcomes alone.
Local causal structure learning can be achieved with high accuracy and efficiency even in the presence of latent variables and selection bias, challenging the need for global causal discovery methods.