Unverified50% confidenceFactExact time
A logging system was embedded in the loop during the experiment to record the token hit status of each large model invocation, with each log line corresponding to one model call.
1
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50%
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Long-term
Relevance
9/11/2026
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Related Claims
UnverifiedIn each iteration of the loop, an agent needs to repackage the system prompt, conversation history, tool descriptions, and all outputs from previous steps before feeding them into the model, causing token consumption to grow linearly or even super-linearly with the number of steps.70% similarUnverifiedUnder the ReAct paradigm, the model goes through a Thought → Action → Observation cycle at each step, with each cycle constituting one complete LLM call.69% similarUnverifiedThe reasoning loop contains three phases—Thought, Action, and Observation—and each iteration requires feeding the complete historical context back into the model, causing token consumption to grow in a stepwise fashion.67% similarUnverified大模型厂商为每个模型版本设定生命周期管理策略，旧版本会经历一般可用、弃用预告、只读维护、彻底下线的完整流程66% similarUnverifiedAutoregressive generative models sample the next token at each step based on the probability distribution of the preceding context; when a repetitive pattern gains a slightly higher probability, it forms a positive feedback loop that leads to repetition degeneration.65% similar
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