Unverified50% confidenceFactExact time
PhyNet encodes physical variables as sequence-level tokens and sets a learnable global response token for the response signal; this global token acts as the Query to retrieve the physical variable tokens (Key/Value), thereby dynamically selecting the relevant operating context when forming the local diagnostic representation.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn LLM calls, prompt tokens refer to the number of tokens contained in the input portion sent to the model, including the system prompt, schema summary, historical conversation, and current instruction; completion tokens are the output portion generated by the model.68% similarUnverifiedtoken消耗的精确计量依赖于对Cursor官方API响应体中usage字段(包含prompt_tokens、completion_tokens)的实时解析64% similarUnverifiedInput token billing for the Decision API typically covers the user's incoming prompt and retrieved recall content, while output tokens correspond to the final result returned by the model.63% similarUnverifiedLarge model services adopt a Token quota mechanism, where each model call consumes Tokens measured by the input and output text62% similarVerifiedMainstream large model APIs charge separately for input tokens and output tokens, with input tokens typically including the entire conversation history (i.e., the context).58% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1079331API
curl https://kongchang.com/api/v1/knowledge/claims/1079331MCP
get_claim(id=1079331)