Unverified50% confidenceFactExact time
Constrained decoding allows only tokens conforming to grammar rules to be sampled by setting the logits of illegal tokens to negative infinity at each decoding step; FSM is applicable to regular languages, while CFG can handle nested structures such as JSON and XML.
1
Sources
50%
Confidence
Long-term
Relevance
9/10/2026
First Seen
Sources
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Related Claims
UnverifiedGBNF 通过在每个 Token 生成步骤对 logits 进行掩码过滤，强制模型只能生成符合预定义语法规则的 Token 序列77% similarUnverifiedOutlines pre-compiles constraints into an FSM before inference and performs an offline mapping between the tokenizer vocabulary and each state of the FSM; during inference, hard constraints are enforced by setting the logits of illegal tokens to negative infinity (logits masking).74% similarUnverifiedOutlines constructs a Finite State Machine (FSM) that dynamically masks tokens at each generation step that would render the format invalid, allowing the model to sample only from the subset of legal tokens.70% similarUnverifiedThe core method of grammar-constrained decoding is to use a context-free grammar (CFG) to filter out illegal token options during the model's token-by-token output generation, setting the logits of illegal tokens to negative infinity.68% similarUnverifiedStructured output applies constraints to the model's token sampling via constrained decoding, typically based on finite-state automata (FSA) or context-free grammars (CFG).67% similar
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