Unverified50% confidenceFactExact time
As the number of conversation turns increases, the accumulation of historical context and new reasoning content compounds computational pressure.
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50%
Confidence
Long-term
Relevance
9/11/2026
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Unverified随着对话轮次增加，每轮需携带完整历史上下文，输入token数量持续膨胀，导致推理时间呈现非线性增长80% similarUnverifiedIn multi-agent scenarios, as the number of agents and discussion rounds increases, conversation history tokens rapidly approach or exceed the context window limit, leading to token bloat and exponentially growing costs.72% similarUnverifiedThe KV Cache, which grows dynamically during inference, continuously occupies VRAM and may suddenly trigger an OOM crash during long conversations.71% similarUnverifiedAs the number of multi-turn conversation rounds increases, the context window becomes occupied by preceding dialogue, the signal strength of citation instructions in the prompt relatively weakens, and citation quality may undergo systematic degradation.69% similarUnverifiedUnder a linear conversation structure, token consumption is cumulative—every request requires retransmitting the entire history, increasing inference latency and driving up API call costs.67% similar
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