CoAction: Cross-task Correlation-aware Pareto Set Learning

• URL: http://arxiv.org/abs/2605.01712v1
• Date: Sun, 03 May 2026 04:52:44 GMT
• Title: CoAction: Cross-task Correlation-aware Pareto Set Learning
• Abstract summary: We propose a cross-tAsk correlation-aware framework for multi-objective multitask optimization.<n>By assigning task-specific embedding vectors to individual tasks, the model effectively distinguishes between tasks while facilitating knowledge sharing.<n>The proposed approach is evaluated on comprehensive multitask test suites covering both benchmark problems and real-world applications.
• Score: 9.837762929043649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optimal solutions. However, existing PSL methods primarily focus on solving a single multi-objective optimization problem at a time. This limitation not only increases computational costs in multi-objective multitask optimization scenarios by requiring a separate model for each task, but also fails to exploit the inter-task correlations across tasks. To address this, we propose a Cross-tAsk correlation-aware Pareto Set Learning (CoAction) framework, which leverages task-aware transformer to handle multiple tasks simultaneously. Specifically, by assigning task-specific embedding vectors to individual tasks, the model effectively distinguishes between tasks while facilitating knowledge sharing among them. We utilize a Transformer encoder as the backbone architecture to leverage its self-attention mechanism for capturing complex task dependencies. The proposed approach is evaluated on comprehensive multitask test suites covering both benchmark problems and real-world applications, demonstrating effectiveness and competitive performance in Hypervolume, Range, and Sparsity.

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