Unverified50% confidenceFactExact time
LLM处理的是token（词元）而非单个字符，导致在字符计数任务上存在天然缺陷
1
Sources
50%
Confidence
Long-term
Relevance
8/27/2026
First Seen
Sources
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UnverifiedLLM的分词器（如BPE）为自然语言设计，会将数值拆解为多个不相关的token，破坏数字的连续性和大小语义79% similarUnverified字符计数错误源于Transformer的tokenizer以子词为基本单位处理文本而非字符，是主流LLM的共同短板74% similarUnverifiedLLM是强大的语言理解与生成工具，但不应被默认当作确定性的计算与执行系统73% similarUnverifiedLLMs' lack of cross-session memory makes it difficult to maintain consistency in code generated across multiple iterative rounds, which can be partially compensated for through Memory Bank structured Markdown files71% similarUnverifiedIn the LLM+solver hybrid architecture, the LLM acts as a translation layer between natural language and formal systems, while specialized tools handle actual execution, thereby avoiding the risk of numerical hallucinations.69% similar
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