ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion

ParetoPilot introduces a novel diffusion-based framework for offline multi-objective optimization that eliminates the need for external surrogate models. The method uses an Infer-Perturb-Guide engine to generate Pareto-optimal designs from static datasets, demonstrating superior performance across 51 tasks while preserving data privacy and reducing computational overhead.

ParetoPilot addresses a fundamental bottleneck in offline multi-objective optimization by removing dependency on external surrogate models, which traditionally introduce computational overhead and evaluation unreliability. The research represents a maturation of generative AI methods, shifting from auxiliary model training toward unified architectures that leverage pre-trained diffusion models' inherent conditional knowledge. This architectural simplification mirrors broader trends in machine learning toward end-to-end systems that eliminate intermediate proxy models.

The framework's technical innovation centers on the IPG engine, which operates within the reverse diffusion process by inferring objective directions through noise prediction matching and applying dynamically annealed perturbation vectors. By orthogonalizing convergence forces with diversity-promoting repulsive fields, the system balances exploration and exploitation without external guidance. The zero-surrogate approach carries practical implications: organizations can optimize complex design problems using their own datasets without training separate predictive models, reducing both computational requirements and privacy exposure.

The significance of this work extends beyond academic contribution. Industries relying on design optimization—pharmaceuticals, materials science, aerospace—could deploy such systems with lower infrastructure costs and reduced regulatory compliance complexity around data handling. The demonstrated performance improvements across 51 diverse tasks suggest robustness across different problem domains, though real-world applicability depends on dataset quality and whether benchmark performance translates to production environments.

Future developments should examine how ParetoPilot scales with dataset size, its performance on truly novel design spaces beyond training distribution, and integration with domain-specific constraints. The approach's data privacy preservation makes it particularly attractive for competitive research settings where proprietary information cannot leave organizational boundaries.

• →ParetoPilot eliminates external surrogate models while outperforming 14 state-of-the-art baselines across 51 optimization tasks.
• →The Infer-Perturb-Guide engine infers objective directions and applies orthogonalized perturbation vectors for convergence and diversity.
• →Zero-surrogate architecture reduces computational overhead and preserves data privacy compared to traditional surrogate-based approaches.
• →Framework leverages conditional priors embedded in pre-trained diffusion models rather than requiring auxiliary model training.
• →Approach achieves robust Pareto front coverage and hypervolume improvement on offline multi-objective optimization problems.