Unverified50% confidenceSolutionExact time
Token cost optimization strategies include compressing long-term memory into summaries, setting up tiered response logic (using lightweight models for simple interactions and high-performance models for key plot points), and splitting world-view validation into low-cost classification tasks.
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50%
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Long-term
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9/11/2026
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Unverified多智能体系统的Token优化策略包括模型路由、压缩智能体间传递的上下文、缓存中间结果72% 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.72% similarUnverifiedThe self-attention mechanism allows the model to dynamically weigh the importance of all other tokens in the sequence when processing each token, thereby capturing long-range semantic dependencies.69% 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69% similar
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