Learning What to Share and What to Complement for Multi-Agent Collaboration

Multi-agent collaboration sharing strategies facilitate knowledge exchange across agents, yet struggle to balance privacy protection with effective collaboration when agents hold different private knowledge. We redesign the communication boundaries around these knowledge differences and propose a privacy-aware multi-agent collaboration framework that uses them as structural signals throughout the reciprocal communication process. Relevance-aware Adaptive Disclosure (RAD) governs outgoing task sharing through a self-calibrated disclosure boundary, moving beyond task fragmentation. Complementary Expertise Integration (CEI) governs returned peer knowledge through geometry-guided complementarity, preserving useful expertise while suppressing redundant reasoning. Across medical and legal tasks on MedQA and LegalQA, our framework consistently improves both open-source and closed-source LLMs, achieving up to 15.3% relative gains in task utility and 9.3% in privacy–utility trade-off over baselines.