Inference-Time Intervention

Inference-time intervention studies how to steer a trained model while it is running, without necessarily changing its weights. Methods include activation and attention steering, decoding-time logit adjustments, retrieval-guided revision, context filtering, and reviewer agents that check tool calls or multi-step actions. They are applied to language and multimodal models, code generation, forecasting, theorem proving, and autonomous agents, with evaluations spanning safety, reasoning, factuality, and task success. A central challenge is improving the target behavior without harming useful outputs; intervention timing, persistence over long rollouts, robustness to new attacks, and reliable measurement remain open issues.