Large language models (LLMs) often exhibit sycophantic behaviors -- such as excessive agreement with or flattery of the user -- but it is unclear whether these behaviors arise from a single mechanism or multiple distinct processes.
We decompose sycophancy into sycophantic agreement and sycophantic praise, contrasting both with genuine agreement.
Using difference-in-means directions, activation additions, and subspace geometry across multiple models and datasets, we show that:
- (1) the three behaviors are encoded along distinct linear directions in latent space;
- (2) each behavior can be independently amplified or suppressed without affecting the others;
- (3) their representational structure is consistent across model families and scales.
These results suggest that sycophantic behaviors correspond to distinct, independently steerable representations.