Unverified50% confidenceSolutionExact time
C2C establishes a mapping mechanism that converts and aligns the source model's KV Cache into a form directly usable by the target model, allowing the target model to take over the semantic understanding already completed by the source model.
1
Sources
50%
Confidence
Long-term
Relevance
9/18/2026
First Seen
Sources
Related Entities
Related Claims
Unverified高层意图驱动的Prompt设计只向模型传达最终目标和核心约束，将中间路径规划权下放给模型，类似于声明式编程66% similarUnverifiedGPU memory consumption during large model inference comes primarily from three components: model weights, activations, and KV Cache.66% similarUnverifiedBuilding an inference engine tailored for a specific model (model specialization) enables deeper optimization in operator implementation, memory layout, and quantization strategy, whereas llama.cpp adopts a general-support strategy aimed at compatibility with multiple model architectures65% similarUnverifiedThe model deployment stage involves model quantization, inference acceleration frameworks (such as vLLM and Ollama), and API encapsulation.65% similarUnverifiedThe KV cache carries the model's complete current reasoning state, and directly modifying it is equivalent to directly intervening in the model's thinking process.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/933226API
curl https://kongchang.com/api/v1/knowledge/claims/933226MCP
get_claim(id=933226)