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High-entropy alloys (HEAs) and multi-principal element alloys (MPEAs) occupy an enormous compositional space that conventional computational approaches cannot fully explore. A five-element system with 10 % concentration steps already contains millions of distinct nominal compositions, each further multiplied by exponentially many atomic configurations arising from configurational disorder. Machine learning (ML) interatomic potentials have been proposed as a scalable solution to accelerate property prediction and materials design in this vast space. Yet the compositional generalizability of these potentials—their capacity to make reliable predictions for compositions and local environments lying outside the training distribution—remains largely unexamined in a systematic way. Most existing benchmarks focus on interpolation within narrow ranges of ordered or equiatomic compounds, leaving critical extrapolation scenarios untested. This conceptual framework article identifies four distinct but interrelated dimensions of compositional generalizability in HEA ML potentials: (i) element extrapolation, (ii) concentration interpolation, (iii) multi-element recombination, and (iv) local environment diversity. It proposes a four-component assessment framework—training-set characterization, test-set design, dimension-specific generalization metrics, and a structured validation protocol—that enables researchers to quantify generalization gaps without relying on performance numbers or simulation results. For each dimension, explicit validation strategies and success criteria are defined, grounded in the literature on special quasirandom structures, graph-network representations, and equivariant architectures. The framework is deliberately conceptual, emphasizing definitions, relationships among components, assessment criteria, and diagnostic reasoning rather than empirical data. By adopting this framework, the community can move beyond ad-hoc testing and develop ML potentials that truly generalize across the high-dimensional alloy landscape. The implications extend to more reliable high-throughput screening, accelerated discovery of novel HEAs, and clearer guidance for training-set design and model architecture choices. Ultimately, systematic assessment of compositional generalizability will help ensure that ML potentials fulfill their promise for complex concentrated alloys.