Context Rot: How Increasing Input Tokens Impacts LLM Performance

The study investigates the impact of input length and structural coherence on the performance of five different embedding models in handling tasks involving needle-haystack setups, where coherent texts are concatenated. By using Paul Graham essays as a benchmark, the research compares conditions that preserve the original logical flow and those that shuffle sentences to disrupt it, revealing that model performance deteriorates with longer inputs and lower similarity between needle-question pairs. The presence of distractors affects models variably, and while the structural pattern of the haystack influences processing effectiveness, needle-haystack similarity does not uniformly impact performance, indicating a need for further exploration. The study evaluates models in both standard and "thinking mode" using a GPT-4.1 judge, noting rare instances of task refusal, and highlights the models' real-world necessity to discern relevant information without exact lexical matches, especially as semantic ambiguity increases with longer inputs.

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