Target-oriented discovery under limited evaluation budgets requires making reliable progress in high-dimensional, heterogeneous design spaces where each new measurement is costly, whether experimental or high-fidelity simulation. We present an information-theoretic framework for target-oriented adaptive sampling that reframes optimization as trajectory discovery: instead of approximating the full response surface, the method maintains and refines a low-entropy information state that concentrates search on target-relevant directions. The approach couples data, model beliefs, and physics/structure priors through dimension-aware information budgeting, adaptive bootstrapped distillation over a heterogeneous surrogate reservoir, and structure-aware candidate manifold analysis with Kalman-inspired multi-model fusion to balance consensus-driven exploitation and disagreement-driven exploration. Evaluated under a single unified protocol without dataset-specific tuning, the framework improves sample efficiency and reliability across 14 single- and multi-objective materials design tasks spanning candidate pools from $600$ to $4 \times 10^6$ and feature dimensions from $10$ to $10^3$, typically reaching top-performing regions within 100 evaluations. Complementary 20-dimensional synthetic benchmarks (Ackley, Rastrigin, Schwefel) further demonstrate robustness to rugged and multimodal landscapes.

翻译：在有限的评估预算下进行面向目标的发现，需要在评估成本高昂（无论是实验还是高保真模拟）的高维异构设计空间中实现可靠进展。本文提出一种面向目标的自适应采样信息论框架，将优化问题重新定义为轨迹发现：该方法并非近似完整的响应面，而是维护并精炼一个低熵信息状态，将搜索集中在与目标相关的方向上。该框架通过维度感知的信息预算分配、异构代理模型池上的自适应引导蒸馏，以及结合卡尔曼滤波启发的多模型融合的结构感知候选流形分析，将数据、模型信念与物理/结构先验相耦合，从而平衡共识驱动的利用与分歧驱动的探索。在无需针对数据集进行调优的统一评估协议下，该框架在14项单目标与多目标材料设计任务中（候选池规模从$600$至$4 \times 10^6$，特征维度从$10$至$10^3$）均提升了采样效率与可靠性，通常能在100次评估内达到性能最优区域。在20维合成基准函数（Ackley、Rastrigin、Schwefel）上的补充实验进一步验证了该方法对崎岖及多模态景观的鲁棒性。