DeepMind's Tulsee Doshi explains Gemini 4 Argon's 1M context use cases
In a video, Google DeepMind's Tulsee Doshi says Argon's 1M-token window is not meant for writing books but for deep reasoning tasks like scientific research and open math problems, which a 64K cap would cut short.
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
This post quotes another post: x.com/20536157/status/2110041297360699856