MANGO: Efficient Memory-Token Governance for Long-Horizon Multi-Agent LLM Reasoning

Large language model (LLM) agents increasingly rely on shared memory for long-horizon reasoning and collaborative problem solving. Yet continuous writing and retrieval cause memory contexts to accumulate, increasing token expenditure and repeatedly exposing agents to redundant or outdated information that can destabilize later reasoning. Existing memory-augmented frameworks primarily improve retrieval or reasoning quality while overlooking the coupled evolution of memory, resource consumption, collaborative progress, and reasoning stability. We present MANGO (Multi-AgeNt Efficient Memory-Token GOvernance), a lightweight feedback-driven framework that regulates shared memory at test time. MANGO integrates population-level coordination, runtime state profiling, and adaptive control over retrieval, writing, and deletion. This closed-loop design supports broad exploration early in execution and progressively consolidates memory as redundancy and runtime pressure emerge. Extensive experiments on multi-objective long-horizon reasoning and single-objective multi-hop QA show that MANGO improves memory-token efficiency by up to 108.6% over representative reasoning and memory-augmented agent baselines while maintaining competitive reasoning quality. By revealing and regulating the coupled dynamics among shared memory, resource consumption, and collaborative progress, MANGO establishes a feedback-driven memory governance system for efficient and stable long-horizon multi-agent reasoning.