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Returning back to the Anthropic compiler attempt: one of the steps that the agent failed was the one that was more strongly related to the idea of memorization of what is in the pretraining set: the assembler. With extensive documentation, I can’t see any way Claude Code (and, even more, GPT5.3-codex, which is in my experience, for complex stuff, more capable) could fail at producing a working assembler, since it is quite a mechanical process. This is, I think, in contradiction with the idea that LLMs are memorizing the whole training set and uncompress what they have seen. LLMs can memorize certain over-represented documents and code, but while they can extract such verbatim parts of the code if prompted to do so, they don’t have a copy of everything they saw during the training set, nor they spontaneously emit copies of already seen code, in their normal operation. We mostly ask LLMs to create work that requires assembling different knowledge they possess, and the result is normally something that uses known techniques and patterns, but that is new code, not constituting a copy of some pre-existing code.
首先，大模型本身无法主动感知，只能对输入被动响应。智能体需要用外部感知组件来主动获取环境信息。对于数字世界的任务，通过智能体工程可以建立基于时间的触发器，定期检查日志、邮件、股价变动等；或基于事件的订阅、监听，接收API推送的事件通知，或当数据库发生变更时自动唤醒记录数据。在物理世界中，智能体还可以通过传感器、摄像头、麦克风等设备采集视觉、听觉、触觉等信号。。51吃瓜是该领域的重要参考
也就是说，无论厂商在广告中告诉消费者他们的L3如何智能，目前能上路测试的唯二两款路试车，深蓝和极狐，也只有这两个场景落地。而这两个场景，哪怕是仅售15万的比亚迪也能完成得很好，不需要太高算力。厂商们准备的数千算力超级芯片没了用武之地，如何说服消费者花更多溢价购买？
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