Verified65% confidenceFactExact time
The memory of large language models fundamentally relies on the attention mechanism of the Transformer architecture, and its effective information capacity is limited by the number of tokens in the context window.
3
Sources
65%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Crew: Enabling Multiple AI Agents to Collaborate and Share Knowledge Like a Team
redditr/opensource7/9/2026
Related Claims
VerifiedTransformer-based large language models use attention mechanisms to uniformly encode system prompts, user inputs, and externally retrieved content as homogenized token sequences, making it impossible for the model to distinguish trusted instructions from untrusted data at the semantic level.81% similarUnverifiedCurrent mainstream large language models generally adopt the Transformer architecture, whose self-attention mechanism has a computational complexity that grows quadratically with sequence length (O(n²)) when processing long contexts.78% similarUnverifiedMemory length is limited by the large language model's context window; advanced paid models with larger context windows can support stronger memory capabilities.76% similarUnverified提示词注入的核心原理在于LLM无法从根本上区分系统指令和用户输入，因为Transformer架构的注意力机制对所有输入token一视同仁，系统提示词与用户输入的token在数学上处于同一向量空间74% similarVerifiedThe Attention Mechanism is the core component of the Transformer architecture and also the foundation of all current mainstream large language models.74% similar
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