Unverified50% confidenceFactExact time
This method is model-agnostic and plug-and-play, can be directly applied during the inference stage without modifying model parameters, and can be applied as an external wrapper layer to different families of large language models.
1
Sources
50%
Confidence
Long-term
Relevance
9/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe Model Abstraction Layer establishes a standardized intermediate layer between application logic and the underlying large language model, abstracting functions such as prompt construction, context management, and response parsing, drawing on the dependency inversion principle of software engineering.77% similarUnverifiedThe tool-calling mechanism allows large language models to dynamically invoke external functions during inference, representing a fundamental leap from language models to action models.76% similarUnverifiedLarge language models do not modify their parameters during inference; each call is an independent function computation.73% similarUnverifiedSub-agents specify the use of a smaller model via a model override parameter and receive self-contained prompts that do not depend on the main loop's context.73% similarUnverifiedThe Skills paradigm uses the large language model itself to simultaneously perform intent understanding, slot filling, and parameter structuring, achieving zero-shot intent routing without requiring additional training data.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/875787API
curl https://kongchang.com/api/v1/knowledge/claims/875787MCP
get_claim(id=875787)