Unverified50% confidenceFactExact time
The attention layer cannot benefit from batching because each sequence has its own unique KV cache.
1
Sources
50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe sparse sampling in deformable attention results in irregular memory access that cannot be efficiently handled by standard matrix multiplication libraries (such as cuBLAS and cuDNN), requiring custom CUDA kernel functions to be written.69% similarUnverifiedCurrent LLMs' memory is primarily limited by the context window, making it impossible to truly retain information across multiple independent conversations.68% similarUnverified大模型的记忆仅限于当前上下文窗口内的token序列,对话结束后所有中间状态消失68% similarUnverifiedPrompt缓存必须从前缀开始严格匹配,一旦中间某个token变化,其后所有token的注意力计算都会改变,已缓存的KV对随之失效67% similarUnverified现有向量数据库记忆方案缺乏时效性衰减机制,无法区分记忆是否被采纳并产生正向效果,也没有机制合并冗余或删除有害记忆66% similar
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