Offline multi-objective optimization (Offline MOO) aims to discover novel Pareto-optimal designs based on static datasets without expensive environment interactions. While recent generative methods have achieved notable success, they predominantly rely on external surrogate models. This dependency introduces significant computational overhead, suffers from deceptive evaluations, and deviates from the prevailing paradigm of jointly training mainstream generative models with conditions. To address these bottlenecks, we propose ParetoPilot, a novel zero-surrogate diffusion framework for offline MOO. ParetoPilot fully leverages the conditional priors inherently embedded within pre-trained diffusion models. At its core, the framework introduces the Infer-Perturb-Guide (IPG) engine, which is seamlessly interleaved within the unconditional denoising steps of the reverse generation process. First, it implicitly infers the instantaneous objective direction by matching conditional and unconditional noise predictions. Next, it mathematically orthogonalizes a parallel gravity field for strict convergence and an edgeness-aware repulsive force for mutual diversity, creating a dynamically annealed perturbation vector. Finally, this perturbed target seamlessly steers the generation process via standard Classifier-Free Guidance (CFG). Extensive experiments across 51 tasks demonstrate that ParetoPilot outperforms 14 state-of-the-art surrogate-based and inverse generative baselines. By eliminating auxiliary proxy training, our approach preserves data privacy while achieving hypervolume improvement and robust Pareto front coverage.

翻译：离线多目标优化旨在基于静态数据集发现新颖的帕累托最优设计，而无需昂贵的环境交互。尽管近期生成方法取得了显著成功，但它们主要依赖外部代理模型。这种依赖性引入了显著的计算开销，面临欺骗性评估的风险，且偏离了主流生成模型联合条件训练的主流范式。为解决这些瓶颈，我们提出ParetoPilot——一种面向离线多目标优化的新型零代理扩散框架。ParetoPilot充分利用预训练扩散模型中固有的条件先验。其核心引入推理-扰动-引导引擎，该引擎无缝嵌入逆向生成过程的非条件去噪步骤中。首先，通过匹配条件与非条件噪声预测，隐式推断瞬时目标方向。其次，将并行重力场（用于严格收敛）与边界感知排斥力（用于维持多样性）进行数学正交化，形成动态退火的扰动向量。最后，该扰动目标通过标准无分类器引导无缝引导生成过程。在51个任务上的大量实验表明，ParetoPilot超越了14种基于代理和逆生成的最先进基线方法。通过消除辅助代理训练，本方法在实现超体积改进和稳健帕累托前沿覆盖的同时，保留了数据隐私。