Models like JEV actually prove: LLMs don't necessarily have
A commenter argued Jev shows LLMs need not generate language for many AI tasks, since humans often decide intuitively without words.
The original post, translated
Models like JEV actually prove: LLMs don't necessarily have to generate language to complete a large amount of AI work. Because humans originally don't rely on language to complete all decisions. Many times, we just see, perceive, and then subconsciously know "what should be done." This is intuition. From this perspective, JEV's intuition and human intuition actually have a somewhat similar brilliance.
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JEV 这类模型其实证明了： LLM 不一定非要生成语言，才能完成大量 AI 工作。 因为人类本来就不是靠语言完成所有决策的。 很多时候，我们只是看到、感知，然后下意识就知道「应该怎么做」。 这就是直觉。 从这个角度看，JEV 的直觉和人类直觉其实有点异曲同工之妙。
The post above is a machine translation from zh; the untranslated text is in the fold-out.
Engagement when collected
Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.
Where this case fits
Filed under general Jev builds. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Browse the full case library for the work it sits next to.
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Last updated: 2026-09-22 · sources & corrections · every card links to its author's original post