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Agentic Search for Counterfactual Recourse under Fixed LLM Budgets
arXiv:2606.08696v1 Announce Type: new Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model. In practice, affected individuals often benefit from multiple feasible alternatives rather than a single optimal explanation. A natural way to produce such alternatives is to prompt large language models (LLMs).