Context Poisoning as Extreme-Value Attention Interference in Long-Context Language Models
Large language models can process increasingly long prompts, yet their ability to locate and use decisive evidence may degrade as irrelevant or confusable context is added. We formulate this phenomenon, which we call context poisoning, as extreme-value interference in attention: the decisive-evidence score is upper-bounded, while the maximum score among effective distractors grows with their number. Under a softmax retrieval abstraction, we derive a finite-sample upper bound showing that maintaining a fixed accuracy target above base rate requires the evidence margin to scale as $Ω(\sqrt{\log N})$, where N denotes the effective distractor count rather than necessarily the raw context leng...
| Authors | Meysam Ghaffari, Nina Fatehi, Bhaskar Sen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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