Unverified60% confidenceTradeoffExact time
Long-horizon agent tasks rely on reasoning-action loops such as the ReAct framework, where errors cascade and amplify along the chain, causing completion rates to fall far below those of single-step question answering.
2
Sources
60%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
Related Claims
Unverified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83% 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78% similarVerifiedWhen a single large model independently completes long-chain tasks requiring more than dozens of reasoning steps, errors accumulate exponentially with chain length, whereas decomposing the task among multiple specialized agents can significantly reduce the error rate.76% similar
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