[2605.31021] A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI

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Computer Science > Artificial Intelligence

arXiv:2605.31021 (cs)

[Submitted on 29 May 2026]

Title:A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI

Authors:Atahan Karagoz<br>View a PDF of the paper titled A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI, by Atahan Karagoz

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Abstract:Current alignment paradigms for generative artificial intelligence rely predominantly on monolithic benchmarking frameworks that reduce the plurality of human judgment to aggregated statistical baselines, thereby obscuring cultural, demographic, and contextual variability in evaluation. We introduce a state-space constrained emulation framework for AI evaluation that replaces singular assessment functions with a structured manifold of synthetic cognitive profiles representing diverse human perspectives. We show that modern generative architectures can instantiate and maintain these evaluative personas with high consistency, enabling a form of pluralistic, perspective-dependent benchmarking that more closely reflects real-world consensus variability. However, we further analyze the stability of these simulated evaluators under sequential inference and stochastic prompt perturbations, revealing systematic degradation in persona coherence that manifests as state-space drift and semantic inconsistency. These findings suggest that static alignment constraints are insufficient for sustaining robust evaluative behavior over time. Instead, we argue for the necessity of embedding dynamic, viability-driven regulatory mechanisms within generative systems to preserve coherent cognitive emulation. By framing persona-based evaluation as a structured dynamical system over latent representation manifolds, this study provides a foundation for more adaptive, human-aligned, and context-sensitive approaches to AI evaluation.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:<br>arXiv:2605.31021 [cs.AI]

(or<br>arXiv:2605.31021v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.31021

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Atahan Karagöz BSc [view email]<br>[v1]<br>Fri, 29 May 2026 08:54:09 UTC (16 KB)

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