Expired60% confidenceFactExact time
The 'expansion' logic of AI models is based on statistical patterns in training data rather than a genuine understanding of the current project architecture, which frequently results in 'over-operation' phenomena.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Superpowers: Installing Work Standards for Your AI Coding Assistant
bilibili飞猿学AI6/12/2026
Related Claims
VerifiedThe compute consumption of AI services occurs primarily during the inference stage, rather than during the one-time model training stage.83% similarUnverifiedAI Agent的过度生成问题与模型训练数据中的偏差有关，模型倾向于生成完整的解决方案即使任务只需要局部修改83% similarVerifiedDistinguishing memorization from reasoning in AI evaluations of AI coding tools is regarded as the core challenge in understanding the true capability boundaries of models, as data contamination causes a model's reasoning to essentially become a replay of memorized training data81% similarVerifiedAI is fundamentally a pattern reproduction based on existing data, and its capabilities are limited when facing problem types that did not appear in its training data.79% similarUnverified测试时计算允许模型根据问题难度动态调整计算投入，是近年AI领域重要的范式转变77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/48048API
curl https://kongchang.com/api/v1/knowledge/claims/48048MCP
get_claim(id=48048)