diversity-driven selection

This paper investigates the trade-off between solution-set diversity and average quality in black-box optimization: given a fixed budget of solutions, maximize their average fitness while ensuring that the pairwise distance between any two solutions exceeds a predefined threshold. We propose the first systematic empirical framework to quantify the performance limits of mainstream heuristic algorithms—including evolutionary algorithms and random search—on this task, and analyze their dependence on problem characteristics. A key finding is that uniform random sampling (RS) significantly outperforms trajectory-based heuristics across the vast majority of benchmark problems, establishing an unexpected yet robust strong baseline. This result challenges prevailing algorithmic design paradigms and provides critical empirical evidence—and a new research direction—for developing algorithms that simultaneously generate high-quality and diverse solution sets.

This work addresses the candidate set selection problem—arising in applications such as drug discovery and hiring—where both high confidence (i.e., strict false discovery rate control) and high diversity are simultaneously required. We propose Diversity-Aware Conformal Selection (DACS), the first model-agnostic framework that provably controls the false discovery rate (FDR ≤ α) while guaranteeing diversity. DACS innovatively integrates conformal e-values, optimal stopping theory, and combinatorial optimization; it employs a diversity metric to guide adaptive stopping decisions, supporting both exact optimization and efficient heuristic algorithms. Experiments on synthetic data, molecular screening benchmarks, and real-world hiring datasets demonstrate that DACS consistently maintains FDR well below the target threshold (α = 0.1) while improving diversity by 32% over state-of-the-art baselines—establishing new performance trade-offs between statistical reliability and representational richness.

This paper addresses the dual-objective optimization problem of simultaneously ensuring diversity and selecting high-ability candidates—e.g., in university admissions—by maximizing aggregate candidate quality subject to a given diversity constraint. Method: We formulate this trade-off as a Pareto frontier search problem and, for the first time, provide two novel axiomatizations grounded in matroid theory. Integrating combinatorial optimization with mechanism design, we develop an algorithmic framework capable of both satisfying hard diversity constraints and enumerating the complete Pareto-optimal set. Contribution/Results: Our method exactly computes all diversity–quality Pareto-optimal subsets, accompanied by theoretical guarantees of optimality. It establishes a rigorous mathematical foundation for designing fair, transparent, and verifiable selection algorithms—advancing principled approaches to equitable resource allocation under structural constraints.

In human-AI collaborative decision-making, tension arises between algorithmic recommendations and human judgment, particularly under data overload, where both observable quantitative objectives and unobservable qualitative preferences (e.g., political feasibility, community acceptability) must be jointly considered. Method: We propose a generative curation framework that dynamically balances optimality and diversity via a dual-path architecture: differentiable sampling and sequential multi-objective optimization. It integrates Gaussian process modeling of latent qualitative utility with a novel diversity metric. Contribution/Results: The framework ensures controllable recommendation set size, near-optimality, and high coverage. Evaluated on policy simulation and operations management tasks, it achieves a 37% improvement in candidate set coverage and a 29% increase in user adoption rate, significantly enhancing decision efficiency and robustness.

To address the fairness–accuracy trade-off in AI-driven talent management—caused by implicit biases in training data—this paper introduces the first quality-diversity optimization framework for visual analytics based on MAP-Elites. The method explicitly constructs a fairness–accuracy Pareto front, enables interactive filtering of models satisfying minimum fairness thresholds, and establishes interpretable mappings between data bias and model behavior. Innovatively integrating CMA-MAP-Elites, multiple fairness metrics (statistical parity and equal opportunity), and bias heatmap visualization, the framework supports explainable, controllable fairness tuning. Evaluated on a real-world talent dataset, it achieves a 37% improvement in fairness while incurring less than a 2.1% drop in accuracy. Furthermore, it generates a two-dimensional behavioral atlas that visually exposes the fairness–accuracy trade-off boundary, facilitating transparent, human-in-the-loop decision-making.

This study addresses the combinatorial optimization problem of identifying a balanced sampling design from a large population under a fixed inclusion probability, such that the weighted estimator of an auxiliary variable closely approximates the known population total—a task of exponential complexity. To tackle this challenge, the authors propose a heuristic approach based on genetic algorithms, which iteratively refines the sampling scheme by integrating minimum support designs with candidate samples exhibiting high balance. This method overcomes the limitations of the traditional cube method in achieving balance and substantially enhances sample balance. Consequently, it offers an efficient and practical approximate optimization pathway for large-scale survey sampling and experimental design.

This study addresses the limitations of heuristic-dependent multi-LLM team formation, which lacks computable complementarity metrics and consequently constrains performance. To overcome this, we propose a heterogeneity-based team selection framework that introduces the first computable and interpretable complementarity measurement system. By quantifying model capabilities, error decorrelation, and predictive behavioral diversity through offline profiling, the framework constructs a joint quality-complementarity optimization objective and employs greedy search to efficiently identify optimal teams. This approach transforms team formation from empirical heuristics into a standardized combinatorial optimization problem. Extensive evaluations across multiple benchmarks demonstrate that our method consistently outperforms quality-only baselines, establishing a reusable selection paradigm for multi-LLM systems.

This study investigates whether prevailing diversity metrics genuinely capture model disagreement in large language model (LLM) ensembles or merely reflect individual model capabilities. Through controlled experiments on 31,900 subsets of 30 LLMs evaluated on MMLU-Pro and TruthfulQA, the authors systematically assess the predictive power of five diversity measures for majority-vote performance gains. Using Spearman correlation, linear regression, and determinant-based algebraic analysis, they find that most diversity metrics are highly collinear with model capability. After rigorously controlling for capability, only “strict diversity,” “disagreement,” and “double-failure” retain weak yet consistent and directionally aligned associations with ensemble failure rates. The results further reveal that simple majority voting surpasses the best individual model in only a small minority of subsets, highlighting the limited utility of current diversity metrics in LLM ensembling.

This work addresses the limitation of conventional survival selection in evolutionary diversity optimization, which often fails due to its dependence on pairwise solution diversity. To overcome this issue, we propose a novel framework that enables the synchronous generation of multiple candidate solutions per generation, along with a tailored survival selection mechanism designed specifically for this setting. By moving beyond the traditional paradigm of single-solution, sequential updates, our approach effectively handles the dynamic nature of each solution’s contribution to population diversity. Experimental results demonstrate that, under certain conditions, the proposed multi-solution generation strategy accelerates convergence toward diverse solutions and significantly improves both the spread and quality balance of the final solution set.

This work addresses the limitations of existing image exploration tools, which overly rely on similarity-based ranking and thereby constrain designers’ holistic perception of visual space and pattern discovery during early-stage ideation. The authors propose an interactive exploration prototype that supports gradual adjustment of diversity, introducing for the first time a dynamic interface for explicitly negotiating the trade-off between diversity and similarity—departing from conventional static ranking paradigms. Built upon Determinantal Point Processes (DPPs), the system enables controllable diversity-aware sampling and exposes the underlying tuning mechanism through an intuitive interface. User studies demonstrate that this approach significantly reduces backtracking behavior, facilitates visual discovery, and outperforms existing baseline tools during the initial phases of creative exploration.