Unverified50% confidenceFactExact time
Traditional harnesses try to automatically remember all historical context, and the larger the task scale, the heavier the computation and storage burden, leading to slow responses and soaring resource usage.
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50%
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Long-term
Relevance
10/14/2026
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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.73% 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71% 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.70% similarUnverifiedThe memory system injects long-term rules and the current task through separate channels, structurally alleviating the dual losses of the context window being filled up and lost in the middle.70% similarUnverifiedChain-of-Thought technology can improve accuracy on complex tasks, but at the cost of multiplying token generation, further lengthening response time.68% similar
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