Unverified50% confidenceFactExact time
LLMs are stateless, and every call requires resending the entire conversation history, causing the input tokens of a coding agent to expand approximately quadratically with the number of turns.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
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Related Claims
UnverifiedLLMs' 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 files74% similarUnverifiedTraditional LLMs are "memoryless"—each API call is independent and the model itself does not remember the content of previous conversations.73% similarUnverifiedLLM在生成代码时倾向于'重写'而非'编辑'，导致连带修改问题频繁发生68% similarUnverifiedHy Memory uses a write-time LLM to convert each conversation into structured facts and reconcile them with old facts, whereas Aura does not use a write-time LLM and only retains the raw conversations67% similarUnverifiedIn conversational applications, triggering a full re-render of the entire message list upon receiving each streaming token causes overhead to grow linearly or worse as the conversation lengthens.67% similar
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