DeepMind 的 Tulsee Doshi 解释 Gemini 4 Argon 的 1M 上下文用途
视频中 Google DeepMind 的 Tulsee Doshi 表示 Argon 的 100 万 token 窗口不是用来写书，而是服务科研、开放数学难题等需要单步长链推理的场景，截到 64K 反而做不完。
Google DeepMind's @tulseedoshi explains why Gemini 4 Argon's massive 1M-token window isn't meant for writing books, but tackling some of the hardest problems in math and science: "It’s actually funny that you give that example because that’s actually not what we expect the model to be used for." "The use cases that it’s gonna be particularly useful for are these deep, deep reasoning use cases. Think scientific research, open math problems. These are cases where the model does need to reason a lot in a single step to get you a really strong outcome." "If you cap the model at 64K tokens, it isn’t able to do that full set of reasoning. By enabling the model to go longer, it does better on these more complex reasoning tasks." @GoogleDeepMind
这条是引用着另一条写的：x.com/20536157/status/2110041297360699856