Unverified50% confidenceOpinionExact time
This framework is complementary to, rather than a replacement for, existing global theories (such as margin bounds): global theories provide necessary conditions or upper bounds for generalization, while neural expansion explains from the perspective of training dynamics how generalization is concretely realized.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
Unverified集成学习的理论基础来自偏差-方差权衡框架,单一复杂模型存在高方差而简单模型存在高偏差72% similarUnverifiedOrchestration frameworks extend the practical boundaries of models by building a perception-memory-action enhancement layer outside the model, without retraining the model.70% similarUnverifiedFrameworks matter far less than patterns; patterns that continue to be effective include plan-then-execute, separating retrieval from reasoning, and explicit handoffs.68% similarUnverifiedSince behavioral structure is primarily determined by the framework, optimization experience accumulated at the framework level can theoretically be transferred and reused across different underlying models.67% similarUnverifiedInference-Time Alignment is an umbrella term for a class of techniques that do not require retraining model parameters and only apply interventions during the generation stage, including prompt engineering, activation steering, decoding constraints, and other means.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1065872API
curl https://kongchang.com/api/v1/knowledge/claims/1065872MCP
get_claim(id=1065872)