Unverified50% confidenceFactExact time
Text communication discretizes continuous hidden states into vocabulary tokens, causing information loss and high token overhead; latent communication directly transmits continuous vectors or low-dimensional projections, preserving more intermediate computation information.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
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Related Claims
UnverifiedIn conversational applications, triggering a full re-render of the entire message list upon receiving each streaming token causes overhead to grow linearly or worse as the conversation lengthens.74% similarUnverifiedContinuous diffusion language models map discrete tokens to a continuous embedding space, perform the noising and denoising process in that space, and finally decode back to discrete tokens.70% similarUnverifiedLarge language models have no persistent memory across sessions, and any information outside the context window is invisible to them; character cards solve the problem of character drift by packaging character information into fixed entries injected along with the prompt.69% similarUnverifiedIn large language model inference, the actual hardware cost of output tokens is far higher than that of input tokens, because the decoding phase is a memory-bandwidth-limited sequential process.68% similarUnverifiedLarge 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.68% similar
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