Unverified50% confidenceFactExact time
Every tool call appends the tool schema, call parameters, and return results to the context; the longer the context, the higher the reasoning cost of each subsequent round, forming quadratic-level token consumption growth.
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50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe traditional approach of stuffing all tool descriptions into the system prompt at once significantly inflates context length as the number of tools increases, consuming VRAM and interfering with the model's attention distribution.77% similarUnverifiedMulti-agent parallel architectures explore multiple solution paths simultaneously, trading higher compute for lower error rates and shorter wall-clock time, but at the cost of a multiplicative increase in token consumption.74% similarUnverifiedReasoning models and verbose system prompts can significantly change the actual number of tokens consumed by a task.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% 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74% similar
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