Unverified50% confidenceFactExact time
The framework requires no labeled data or fine-tuning, because the model parameters are completely frozen, and only the prompt rules need to be adjusted to adapt to defect pattern drift caused by changes in production conditions.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA frozen model refers to a model whose weight parameters all remain unchanged during the inference phase, with no fine-tuning, adapter training, or online learning performed.76% similarUnverifiedLLM在推理阶段参数是冻结的，模型不会因用户交互而更新权重，因此本质上是无状态的75% similarUnverifiedThe ABACUS framework improves multi-step reasoning by composing reusable programs to freeze the judgment model's decisions, and the model parameters are not updated throughout.73% similarUnverifiedWeight freezing is implemented by setting requires_grad to False for some parameters, does not change the model structure, does not affect inference forward computation, and has zero deployment cost.72% similarUnverifiedRules文件对Fast模式的调度逻辑没有实质约束力，因为调度发生在模型被调用之前72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1102021API
curl https://kongchang.com/api/v1/knowledge/claims/1102021MCP
get_claim(id=1102021)