PLASMAS: How Much Collaboration Do You Need in Multi-Agent System?

Semantically similar tasks can differ substantially in whether and how much multi-agent collaboration they require, making task-specific topology generation critical for efficient multi-agent systems (MAS). Existing graph-based generators typically encode tasks into intermediate representations before predicting topologies with a separate graph model. We show that these pipelines exhibit limited sensitivity to fine-grained collaboration demands, with only marginal gains from stronger encoders or graph backbones, suggesting a semantic bottleneck between task understanding and topology generation. To address this limitation, we propose PLASMAS, a preference-aligned topology generator following the language-based graph foundation model paradigm. PLASMAS jointly models task semantics, agent roles, and topology outputs within a shared language interface. Utility-aware pairwise preferences guide direct preference optimization based on task effectiveness and execution cost. Experiments show that PLASMAS better distinguishes collaboration demands among semantically similar tasks, allocating additional collaboration where beneficial and reducing unnecessary coordination on easy tasks while maintaining strong task performance. Our code is available at https://anonymous.4open.science/r/PLASMAS-F526/.