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This paper demonstrates that interventions on Sparse Autoencoder (SAE) features can be unreliable because suppressed behavior can recover through residual-space optimization, even while the intervention remains active. It reveals a critical gap between feature-level control and actual behavioral completeness in language models.
FineSteer is a novel inference-time steering framework that decomposes steering into conditional steering and fine-grained vector synthesis stages, using Subspace-guided Conditional Steering (SCS) and Mixture-of-Steering-Experts (MoSE) mechanisms to improve safety and truthfulness while preserving model utility. Experiments show 7.6% improvement over state-of-the-art methods on TruthfulQA with minimal utility loss.