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Prospective memory in LLMs is not just harder than retrospective memory; it reveals critical insights into a model's reasoning capacity and attentional robustness.
MAGE redefines memory management for long-horizon agents, achieving up to 20.4% higher task success rates while slashing token usage by over half.
