🤖 AI Summary
Current evaluations of generative AI alignment predominantly rely on single benchmarks, which inadequately capture the diversity of human judgment across cultural, demographic, and contextual dimensions. This work proposes a personified evaluation framework grounded in state-space constraints, modeling alignment assessment for the first time as a structured dynamical system on a latent manifold. By synthesizing diverse cognitive personas, the approach enables perspective-dependent, pluralistic evaluation. It incorporates a dynamic, survival-driven regulatory mechanism to ensure cognitive fidelity and validates robustness through stochastic prompt perturbations and sequential reasoning stability analysis. Experiments demonstrate that generative models can consistently instantiate multifaceted evaluative personas, while also revealing state drift and semantic inconsistencies under static alignment constraints.
📝 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.