Unverified50% confidenceFactExact time
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
1
Sources
50%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
GPT-5.6 Three-Model Showdown: Sol vs Terra vs Luna — Which One Should You Pick?
bilibili麦冬AI实验室7/11/2026
Related Claims
UnverifiedWhen 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.78% similarUnverified智能体模式下Token消耗呈非线性增长，因每次工具调用结果追加到上下文中，多步骤任务的Token消耗可达O(n²)级别，使低成本模型对智能体场景至关重要76% similarUnverifiedAgentic systems often run continuously every minute via scheduled tasks, and combined with multiple rounds of model calls, tool call results being fed back in, and reflection steps, token costs escalate exponentially.74% similarUnverified多Agent架构中Agent间传递上下文时重复信息被反复处理，导致Token消耗和成本上升74% similarUnverifiedIn 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.74% similar
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