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I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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Instead of hosting a launch event at its Apple Park headquarters in Silicon Valley, the March 4 event will involve experiences in London, New York, and Shanghai.
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%）。