The case for AI models sharing more than tokens
A post uses a detective-novel analogy to argue that passing another model a killer’s name is not the same as handing over the case—a contrast between text output and a model’s internal states.
TLDR
A post imagines a language model reading a detective novel ending with “the killer was.” To supply the next word, the author argues, the model must build the case internally, weighing alibis and earlier clues. In the analogy, a second model receiving only the killer’s name misses that underlying work. The author uses this contrast to argue that hidden states matter more than output tokens, and says @mostik_ai is building a channel that “hands over the case.”
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