Unverified50% confidenceSolutionExact time
Defensive programming in LLM output parsing means not assuming that the model will necessarily follow the format convention, but instead treating every layer of output as untrusted input to be checked, thereby confining unpredictability to the system boundary and preventing a single format anomaly from spreading into a global failure.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
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Related Claims
UnverifiedLLM模型在「生成」和「评估」时所激活的推理路径不同，同一模型生成代码时可能忽略某个边界条件，但在评估另一份代码时却能识别该问题74% similarVerifiedConstrained decoding forces the model to produce output conforming to a preset structure by masking out tokens that do not comply with the target grammar or schema at each generation step.73% similarUnverifiedThe model that generates code should not simultaneously serve as the validator of code security, because the systematic biases introduced by LLMs during the generation phase are equally present during the validation phase.73% similarUnverifiedLLM inference during the decode phase is typically memory-bandwidth-bound rather than compute-bound, because token-by-token generation requires loading the entire model weights but has extremely low arithmetic intensity.70% similar
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