Unverified50% confidenceFactExact time
In long-chain, multi-iteration autonomous agent tasks, models repeatedly read and write large volumes of conversation history for self-verification, error recovery, and context maintenance, causing token consumption to far exceed single-turn Q&A scenarios (token inflation).
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50%
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Long-term
Relevance
10/1/2026
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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.77% 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74% similarUnverifiedMulti-turn conversation configuration addresses the context memory problem, determining how many rounds of historical information an agent can retain during continuous dialogue.73% similarUnverifiedDue to the combination of multiple rounds of iteration, full context carrying, and self-checking mechanisms, token consumption in Loop Engineering is typically 5 to 20 times that of ordinary conversations.73% 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.73% similar
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