首先,大模型本身没那么可靠:存在无法根除的幻觉问题、知识时效性问题,任务拆解和规划经常不合理,也缺乏面向特定任务的系统性校验机制。这样一来,以其为“大脑”的智能体使用价值会大打折扣:智能体把模型从“对话”推向“行动”,错误不再只是答错问题,而是可能引发实际操作风险;而真实业务任务往往是跨系统、长链路的,一次小错误会在链路中层层放大,令长链路任务的失败率居高不下(例如单步成功率为95%时,一个 20步链路的整体成功率只有约 36%)。
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Tolley's team has even tried driving over one of their robots in a car. "We wanted to show it was soft and squishy enough. It can really suffer a lot of different abuses."
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