MATAR: Multi-Agent Task-Adaptive Refinement for Multimodal Data

The utility of multimodal data depends on the downstream task, yet data preparation often relies on generic quality criteria, sample selection, or fixed refinement procedures. These approaches leave limited scope to adapt record attributes and inter-record relations to task requirements. We propose , a task-adaptive multi-agent framework that uses downstream evidence to optimize executable refinement programs. Attribute and relation programs are searched independently, with task feedback selecting one final output. Task-adaptive tool optimization revises program structure and parameters and generates validated numerical feature operations when existing tools are insufficient. Paired replay evaluation compares revisions under matched training conditions, while experiential policy adaptation uses recent outcomes and an online controller to guide subsequent proposals with frozen LLM parameters. Across eight datasets spanning four downstream tasks, achieves an average relative gain of 23.9% over the strongest baseline per dataset under the evaluated protocols. Further experiments support the contributions of the three components and show that experience encoded in refinement programs can benefit other datasets. The findings suggest that effective data refinement should consider both attribute calibration and relational structure, with task-specific evaluation guiding their use and transfer.