Unverified50% confidenceFactExact time
In the ReAct loop, once the model misjudges the cause of an error, it may repeatedly try along the wrong path; long loops consume large amounts of tokens, driving up cost and latency.
1
Sources
50%
Confidence
Long-term
Relevance
10/23/2026
First Seen
Sources
The Secret of AI Agents: How the ReAct Framework Lets LLMs Act Autonomously
youtubeLokesh Desai10/20/2026
Related Entities
Related Claims
UnverifiedThe main risks of the Loop stage include quality being difficult to guarantee due to poorly designed acceptance criteria, output deviation going out of control due to long-term autonomous looping, and a sharp increase in token consumption.78% similarUnverified传统ReAct模式在复杂场景下存在瓶颈，长链路推理中的误差累积效应会导致任务失败率上升76% similarVerifiedWhen validation fails, the specific error should be fed back to the model for correction, but retries must have limits on the number of attempts, timeout duration, and cost control, as retries increase latency and resource consumption.76% similarUnverifiedReAct的线性思考-行动循环在需要并行探索多条路径的任务时效率低下，且每步推理的Token消耗累积导致长任务计算成本呈二次方增长76% similar
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