arXiv:2509.08194v2 Announce Type: replace Abstract: We address the problem of policy selection in contextual stochastic optimization (CSO), where covariates are available as contextual information and decisions must satisfy hard feasibility constraints. In many CSO settings, multiple candidate policies--arising from different modeling paradigms--exhibit heterogeneous performance across the covariate space, with no single policy uniformly dominating. We propose Prescribe-then-Select (PS), a modular framework that first constructs a library of feasible candidate policies and then learns a meta-p
The paper, published in early May 2026, reflects a current trend in AI research focusing on practical, adaptive policy selection for complex optimization problems with real-world constraints.
This framework offers a principled approach to combining and optimizing diverse AI models, which is crucial for AI systems operating in dynamic environments with varied performance characteristics.
The 'Prescribe-then-Select' framework provides a modular and adaptive method for building more robust and efficient AI systems by intelligently leveraging multiple specialized policies.
- · AI developers
- · Logistics and supply chain companies
- · Healthcare organizations
- · Robotics and automation sector
- · Companies relying on monolithic, single-model AI solutions
- · Legacy AI optimization platforms
Improved performance and reliability of AI systems in complex, real-world optimization tasks.
Accelerated adoption of AI in sectors requiring high-assurance and context-aware decision-making.
The development of meta-learning platforms that specialize in dynamically combining and deploying diverse AI models.
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Read at arXiv cs.LG