Enterprise AI Analysis
Contextual Drag: How Errors in the Context Affect LLM Reasoning
By Yun Cheng, Xingyu Zhu, Haoyu Zhao, Sanjeev Arora • Published: Feb 4, 2026
Executive Impact: Unpacking LLM Vulnerabilities
This research identifies "contextual drag" as a critical failure mode in large language models, where exposure to erroneous reasoning in context significantly degrades performance and biases subsequent outputs. This finding challenges current iterative self-improvement paradigms for LLMs.
Average Performance Drop
Self-Deterioration Rate
Structural Similarity Increase
Deep Analysis & Enterprise Applications
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The Core Problem: Contextual Drag
Contextual drag is a critical failure mode where large language models (LLMs) are biased by erroneous past attempts in their reasoning context. This phenomenon leads to performance degradation and structural distortions in subsequent outputs, challenging iterative self-improvement pipelines.
10-20%
Average performance drop due to contextual drag across 11 models and 8 tasks.
Enterprise Process Flow
Initial Problem Solving
→
Incorrect Draft Generated
→
Draft Provided in Context
→
Subsequent Reasoning Biased by Draft
→
Performance Degradation & Similar Errors
Contextual drag manifests as errors from previous attempts influencing new solutions, even when those attempts are explicitly marked as incorrect.
| Feature | Without Error Signals | With External Error Signals | With Self-Detected Error Signals |
|---|---|---|---|
| Performance Recovery | ❌ Significant Drop | ❌ Insufficient Recovery | ⚠️ Partial Recovery (model-dependent) |
| Structural Error Propagation | ✓ High | ✓ High | ✓ Persists on Game of 24 |
| Self-Deterioration Risk | ✓ High (GPT-OSS-20B) | ✓ High | ❌ Reduced for Nemotron family |
| Context Utilization | Poor | Poor | Improved for some models |
Case Study: Game of 24 Puzzle
On the Game of 24 puzzle, solutions can be represented as expression trees. Our analysis using Tree Edit Distance (TED) reveals that conditioned responses under contextual drag remain significantly closer to the erroneous in-context draft than clean-slate solutions. This indicates that contextual drag operates at the level of internal reasoning structure, causing models to subtly follow flawed computational pathways rather than merely imitating surface tokens.
For instance, an incorrect draft might propose (-1 + 13) * (12 + 1). A model affected by contextual drag, even when generating a new solution, might produce something like (-1 + 13) + (12 + 1), which shares structural elements but is still incorrect and distinct from a correct solution like (-1 + (12 + 13)) * 1. This quantitative evidence highlights the 'dragging' behavior and the deep-seated nature of the bias.
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