Unverified50% confidenceFactExact time
In the LLM context, recursive self-improvement need not mean the model directly modifying its own weights; it can take the form of the model proposing improvements at the architecture, data or training recipe level, which are then validated and executed by an external process.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLLM采用无状态设计，模型权重在训练后被固化，推理时不会因某次对话而改变内部参数76% similarUnverifiedA model's actual behavior is jointly determined by training weights, system prompts, and runtime context; looking at the prompt alone cannot fully reproduce the complete picture of the product.75% similarUnverifiedSkills 机制通过上下文注入在不修改模型权重的前提下引导模型输出，相比一次性 Prompt Engineering 强调可复用性、版本管理和模块化组合74% similarUnverifiedConformal prediction can be applied as a wrapper layer on top of any already-trained model without requiring retraining or modification of the model architecture.73% similarUnverifiedProduction environments typically require using the model as an advisor to compare and select between its plans and those of the traditional optimizer, or setting up fallback mechanisms, rather than fully replacing it, to prevent the learned model from occasionally generating catastrophically bad plans.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1069688API
curl https://kongchang.com/api/v1/knowledge/claims/1069688MCP
get_claim(id=1069688)