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Multi-agent systems for automated research face a fundamental trade-off: parallel exploration offers speed and stability, while expert teams unlock deeper reasoning at the cost of increased fragility.
By framing adversarial training as a zero-sum Markov game, ADV-0 finds more diverse safety-critical failures in autonomous driving systems, leading to significantly improved generalization against unseen long-tail risks.
