The Illusion of Robustness: Aggregate Accuracy Hides Prediction Flips under Task-Irrelevant Context

Jul 15, 2026

A new study finds that large language models (LLMs) often appear robust to irrelevant context when measured by overall accuracy, but this masks significant instability on individual examples. Even meaningless pseudo-words can cause prediction changes for a small subset of inputs, sometimes degrading and sometimes improving performance. The specific examples affected are mostly unique to each model, and the degree of instability depends on factors like context type, length, test-time compute, and model development stage.

Why it matters: This research highlights that aggregate accuracy metrics can conceal important reliability risks in LLMs, underscoring the need for per-example evaluation for safer deployment.

Full story at: arXiv Computation and Language ↗