Unverified50% confidenceFactExact time
Transformer 的自注意力机制让模型能捕捉代码 token 之间的长程依赖关系，但其本质是条件概率分布估计器，学到的是代码看起来像好代码而非在特定业务场景下正确
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
The Rise of AI Slop Code: How Teams Can Tackle the "Vibe Coding" Crisis
redditr/cursor7/11/2026
Related Claims
UnverifiedThe Codex model is based on the autoregressive generation mechanism of the Transformer architecture, treating code as a sequence of tokens, capturing long-range dependencies via the attention mechanism, and generating subsequent code token by token.76% similarUnverifiedLarge language models use the self-attention mechanism in the Transformer architecture to compute the probability distribution of all candidate tokens at each position76% similarUnverifiedThe attention mechanism of the Transformer architecture allows the model to dynamically attend to information at other positions when processing a sequence, and concentrating requirements discussions within a single continuous conversation produces better results than spreading them across multiple sessions.74% similarVerifiedThe self-attention mechanism of the Transformer allows the model to directly compute dependencies between any two positions in a sequence, breaking through the parallelization bottleneck caused by the sequential computation of RNNs/LSTMs.74% similarVerifiedThe key innovation of the Transformer is the self-attention mechanism, which allows the model to simultaneously attend to information from all other tokens in the input sequence when processing a single token.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/491021API
curl https://kongchang.com/api/v1/knowledge/claims/491021MCP
get_claim(id=491021)