Evidence for feature-specific error correction in LLMs

Language models represent more concepts than they have mathematical dimensions—a trick called superposition—but how they compute in this crowded space has remained unclear. According to research published on LessWrong, models use feature-specific error correction: they suppress interference in certain directions while preserving meaningful signals in others. By carefully perturbing activations and measuring the model's sensitivity to different types of perturbations, researchers found evidence of this mechanism working across six major model families. The work suggests that computing in superposition requires more than generic error correction—it requires selective, direction-aware suppression that actively protects important features.

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