Unverified50% confidenceFactExact time
UniWALM adopts a closed-loop 'language as memory' mechanism, in which text generated by the model itself is re-consumed in the next step as context for subsequent decisions.
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50%
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Long-term
Relevance
10/21/2026
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Unverified大语言模型在生成查找目标文本时依赖记忆重建而非逐字符复制，在长上下文场景下会出现记忆漂移导致字符串替换静默失败，此现象被称为幻觉性精确76% similarUnverifiedLarge language models themselves are stateless, and each request is an independent computation; so-called 'remembering' usually relies on two mechanisms: one is concatenating historical conversation into the current prompt (the context window), and the other is retrieval-augmented generation (RAG) through external storage (such as vector databases or file systems).73% similarUnverifiedLarge language models are essentially stateless functions, and a model's memory depends entirely on the content within the context window.73% similarUnverifiedFactual recall in language models exhibits a layered, sequentially ordered information integration process: first locking in the type framework of the answer, then routing specific entity content to the output position.72% similarUnverifiedOne common technical approach for large language models to implement memory is to write a summary of past conversations into the system prompt, allowing the model to read the learner's historical profile at the start of each session.70% similar
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