Unverified50% confidenceFactExact time
In autoregressive language models under causal attention, cached prefix states are not rewritten by subsequent tokens, and once the cache is written it is exactly consistent with the result of full recomputation.
1
Sources
50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedWhether prompt caching hits depends on byte-for-byte consistency of the request prefix; if even one character at the beginning differs, the entire cache is invalidated.75% similarUnverifiedThe KV Cache mechanism reduces token costs in long-context, high-frequency-call scenarios by reusing already-computed intermediate results to avoid re-executing forward inference on the same text.74% similarUnverifiedThe prompt caching mechanism of language models caches previously sent messages, eliminating the need to repeatedly reprocess the same content.74% similarUnverifiedWhen the concatenation order of the cache is reversed, the single-token attention test still passes, but decoding multiple new tokens together fails.74% similarUnverifiedThe decoder-only architecture uses a causal attention mask to ensure each token can only attend to its preceding context, making it suitable for autoregressive text generation tasks.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1115509API
curl https://kongchang.com/api/v1/knowledge/claims/1115509MCP
get_claim(id=1115509)