Verified75% confidenceFactExact time
When an autoregressive model produces an erroneous intermediate conclusion early in the reasoning chain, it causes subsequent steps to accumulate errors, forming an error accumulation effect.
5
Sources
75%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedIn the ReAct framework, if the first reasoning step is biased, all subsequent steps will stack upon the erroneous premise, forming a "cognitive avalanche" effect — i.e., the risk of error accumulation.83% similarVerifiedIn autoregressive prediction, errors at each step accumulate into the next, causing long-range predictions to degrade rapidly.82% similarUnverified自回归生成的误差累积效应源于模型只能沿单一路径前进，误差以乘积形式放大81% similarUnverified级联失败是指单次工具调用错误会污染后续上下文，导致模型在错误的中间状态上继续推理，最终使整个任务偏轨80% similarVerifiedError cascading refers to small judgment deviations in early steps being progressively amplified as the reasoning chain extends, ultimately producing results far from what was intended.80% similar
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