Papers propose sparse-feature and Bernoulli steering for LLMs

One paper introduces statistically grounded sparse-feature interventions for activation-space control, improving over single-criterion SAE selection. Another proposes Bernoulli sparse steering, applying steering signals only on a per-token probabilistic basis to reduce computational overhead. Both methods offer lightweight alternatives to fine-tuning for LLM behavior control.