Unverified50% confidenceFactExact time
The autoregressive generation mechanism of language models is essentially predicting the next token based on local context, lacking a persistently effective explicit goal representation that spans multiple rounds of interaction.
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50%
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Long-term
Relevance
10/16/2026
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Related Claims
UnverifiedWhen a prompt is entered, the model predicts the most likely next token based on context, rather than truly understanding the semantics.79% similarUnverifiedModern large language models generate code in an auto-regressive manner, predicting the next token each time by referencing only the existing context.76% similarVerifiedThe token generation process of large language models is serial, based on an autoregressive mechanism, where each token's generation depends on the previous token.75% similarUnverifiedTraditional large language models are fundamentally predicting the next token and outputting natural language, requiring an additional layer (such as outputting JSON or function calls) to execute tasks.75% similarUnverifiedLanguage models have no independent metacognitive module to monitor their own reasoning steps; they simply predict the next token given a context.73% similar
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