Unverified50% confidenceFactExact time
In language models, visual tokens are concatenated with text tokens and jointly participate in self-attention computation, whose computational overhead grows quadratically with the number of tokens.
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50%
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Long-term
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11/5/2026
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Related Claims
UnverifiedLarge language model APIs are generally billed by the total number of input plus output tokens, and re-injecting historical context on every call in long projects causes token consumption to balloon rapidly.73% similarUnverifiedIn-context conditioning concatenates the text condition and image tokens into the same sequence, eliminating the cross-attention module, but it lengthens the sequence and increases the amount of attention computation.71% similarVerifiedEvery large language model has a maximum token limit it can process, and when the content accumulated by a long task approaches that limit the model will forget early content due to degradation of the attention mechanism over excessively long sequences.69% similarVerifiedThe cost of each large language model call consists of two parts: input tokens (Prompt) and output tokens (Completion).69% similarUnverifiedThe same set of attention heads in large language models are repeatedly reused across different tasks.67% similar
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