Verified70% confidenceFactExact time
When an agent is in a loop, each call carries the accumulated conversation history, causing token consumption to grow linearly or even super-linearly with the number of iterations.
4
Sources
70%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedIn loop scenarios, each iteration requires feeding the entire conversation history back into the model, causing per-iteration token consumption to increase incrementally due to context accumulation, resulting in a cost curve steeper than linear.79% similarUnverified在智能体工作流中，历史交互作为输入Token累积，导致总Token消耗随任务轮次呈近似平方级增长78% similarUnverifiedIn the agent's multiple rounds of iteration, the context window keeps expanding with each iteration, causing the number of input tokens per inference round to increase, and the cost of attention computation grows superlinearly.77% similarUnverifiedAgent 的每一轮循环都需将完整历史上下文传入 LLM，导致 token 消耗随任务复杂度呈线性乃至指数增长77% similar
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