LLM Personas as a Substitute for Field Experiments in Method Benchmarking

Field experiments (A/B tests) are often the most credible benchmark for methods in societal systems, but their cost and latency create a major bottleneck for iterative method development. LLM-based persona simulation offers a cheap synthetic alternative, yet it is unclear whether replacing humans with personas preserves the benchmark interface that adaptive methods optimize against. We prove an if-and-only-if characterization: when (i) methods observe only the aggregate outcome (aggregate-only observation) and (ii) evaluation depends only on the submitted artifact and not on the algorithm's identity or provenance (algorithm-blind evaluation), swapping humans for personas is just panel change from the method's point of view, indistinguishable from changing the evaluation population (e.g., New York to Jakarta). Furthermore, we move from validity to usefulness: we define an information-theoretic discriminability of the induced aggregate channel and show that making persona benchmarking as decision-relevant as a field experiment is fundamentally a sample-size question, yielding explicit bounds on the number of independent persona evaluations required to reliably distinguish meaningfully different methods at a chosen resolution.

Bayesian Optimization in Language Space: An Eval-Efficient AI Self-Improvement Framework

Large Language Models (LLMs) have recently enabled self-improving AI, i.e., AI that iteratively generates, evaluates, and refines its own outcomes. Recent studies have shown that self-improving AI focusing on prompt optimization can outperform state-of-the-art reinforcement-learning fine-tuned LLMs. Here, their `performance' is typically measured by query efficiency - the number of LLM-generated solution samples required to meet a certain performance threshold. However, in many societal applications, the primary limitation is not generating new solutions but evaluating them. For instance, evaluating an ad's effectiveness requires significant human feedback, which is far more costly and time-consuming than generating a candidate ad. To optimize for the evaluation efficiency objective, a natural approach is to extend Bayesian Optimization (BO), a framework proven optimal for evaluation efficiency, to the language domain. However, the difficulty of directly estimating suitable acquisition functions in LLMs' minds makes this extension challenging. This paper overcomes this challenge by proving that the combination of the simple and widely used Best-of-N selection strategy and simple textual gradients (i.e., textual edits from a critic model) statistically emulates the behavior of the gradients on the canonical UCB acquisition function, which induces optimal exploration in terms of evaluation efficiency. Based on this result, we propose TextGrad-Best-of-N Bayesian Optimization (T-BoN BO), a simple and eval-efficient language-space Bayesian optimization framework for AI self-improvement. We also empirically validate T-BoN BO by applying it to automated ad alignment tasks for persona distribution, demonstrating its superior performance compared to popular state-of-the-art baselines.