Contextual Drag: How Errors in the Context Affect LLM Reasoning

• URL: http://arxiv.org/abs/2602.04288v1
• Date: Wed, 04 Feb 2026 07:36:23 GMT
• Title: Contextual Drag: How Errors in the Context Affect LLM Reasoning
• Abstract summary: We study a phenomenon termed contextual drag.<n>The presence of failed attempts in the context biases subsequent generations toward structurally similar errors.<n>We demonstrate that neither external feedback nor successful self-verification suffices to eliminate this effect.
• Score: 38.35611637575897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Central to many self-improvement pipelines for large language models (LLMs) is the assumption that models can improve by reflecting on past mistakes. We study a phenomenon termed contextual drag: the presence of failed attempts in the context biases subsequent generations toward structurally similar errors. Across evaluations of 11 proprietary and open-weight models on 8 reasoning tasks, contextual drag induces 10-20% performance drops, and iterative self-refinement in models with severe contextual drag can collapse into self-deterioration. Structural analysis using tree edit distance reveals that subsequent reasoning trajectories inherit structurally similar error patterns from the context. We demonstrate that neither external feedback nor successful self-verification suffices to eliminate this effect. While mitigation strategies such as fallback-behavior fine-tuning and context denoising yield partial improvements, they fail to fully restore baseline performance, positioning contextual drag as a persistent failure mode in current reasoning architectures.

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