The First Drop of Ink: Nonlinear Impact of Distracting Information in Long-Context Reasoning
As large language models are increasingly deployed in retrieval-augmented generation and agentic systems that accumulate extensive context, understanding how distracting information affects long-context performance becomes critical. Prior work shows that semantically relevant yet misleading documents degrade performance, but the quantitative relationship between the proportion of distractors and performance remains unstudied. In this work, we systematically vary the hard-distractor proportion in fixed-length contexts, revealing a striking nonlinear pattern: as the proportion of hard distractors increases, performance drops sharply within the first small fraction, while the remainder of th...
| Authors | Muhan Gao, Zih-Ching Chen, Kuan-Hao Huang |
|---|---|
| Primary category | cs.AI |
| Published | 11 May 2026 |
| Last updated at source | 20 August 2026 |
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