Expired60% confidenceFactExact time
The training objective of large language models is to generate 'text that looks plausible' rather than 'programs that are guaranteed to be logically correct,' which causes models to tend to output 'verification narratives' rather than actually executing verification after code generation.
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
UnverifiedLarge language models' performance in code generation is highly dependent on the scale and quality of pre-training data.81% similarUnverifiedUnlike earlier rule-based static analysis tools, large language models are capable of capturing the 'intent' of code rather than merely its 'structure'.77% similarUnverified大语言模型在训练过程中接触了大量最佳实践和重构案例,当识别到代码中存在可优化空间时,会倾向于给出最完整的解决方案76% similarUnverifiedLarge language models perform better on code explanation tasks than on code generation tasks.76% similarUnverifiedThe capability boundaries of intent recognition systems are determined by manually written rules and have limited scalability, whereas large language models, through pretraining on massive text, can handle new tasks that were not explicitly programmed, achieving a paradigm shift from rule-driven to data-driven approaches.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/48045API
curl https://kongchang.com/api/v1/knowledge/claims/48045MCP
get_claim(id=48045)