Unverified50% confidenceFactExact time
The causal structure graph extracts the dependency relationships on preceding tokens when the model generates each token and presents them in the form of a directed acyclic graph (DAG).
1
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50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
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Related Claims
Unverified参数化建模的本质是将几何形状表达为一组可变参数和约束关系的有向无环图（DAG），记录完整的操作历史（Feature Tree）74% similarUnverified贝叶斯网络通过有向无环图显式建模变量间的条件依赖关系，适合捕捉因果链条73% similarUnverified传统自回归推理中，模型每一步只能基于已有上下文预测下一个token，是串行依赖关系71% similarUnverifiedLarge language models use an autoregressive generation mechanism, sampling the next token at each step based solely on a probability distribution, with each token's generation conditioned on all preceding tokens to form a chain of probabilistic dependencies.70% similarUnverifiedEach time a token is generated via a forward pass, the model must go through a complete multi-head attention computation and feed-forward network layers, with the amount of computation determined by the model's parameter scale69% similar
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