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R虏-MAD not only corrects misconceptions in multi-agent debates but also intelligently weighs agent contributions based on past performance, leading to more accurate outcomes.
UniMem achieves a remarkable 4.0 EM point improvement in task execution fidelity by seamlessly integrating episodic and parametric memory without the need for explicit task labels.
