Chain-of-thought reasoning improves large language models by spending more tokens at test time, but this couples accuracy to generation length. Activation steering offers a lightweight alternative, yet most steering methods apply a fixed vector throughout generation and cannot adapt to different stages of a multi-step solution. We propose STIR (Self-Distilled Tools for Internal Reasoning), a test-time framework that builds a compact library of state-conditioned correction vectors from the model’s own successful and failed rollouts. At inference time, STIR retrieves candidate corrections at structural reasoning checkpoints, validates them with a short lookahead probe, and abstains when the current trajectory already matches a high-quality state. Across six mathematical and knowledge-intensive reasoning benchmarks, STIR improves average accuracy by 1.9% to 7.5% over Vanilla decoding while reducing average token consumption by up to 35%. These results suggest that selective latent interventions can improve the accuracy-cost trade-off of reasoning without forcing every problem to generate longer chains. Our code is available at https://github.com/sznnzs/STIR.