Large Language Models (LLMs) are strong at multi-step reasoning, but running them for every generated token is expensive. Small language models (SLMs) are much cheaper, yet less reliable at the hardest reasoning steps. Existing routing methods mostly make a single query-level decision, assuming that response difficulty is uniform once decoding begins. For chain-of-thought reasoning, however, difficulty varies during generation, making token-level routing a more appropriate granularity. This setting introduces three challenges: token-level uncertainty is noisy, local routing decisions must satisfy global efficiency constraints, and frequent switching can disrupt reasoning coherence. We present TokenRouter, a black-box runtime controller for uncertainty-guided token-level routing between small and large language models. TokenRouter combines a robust entropy detector, a self-calibrating control policy, and a dynamic intervention commitment mechanism for stable switching over difficult spans. Across three reasoning benchmarks, TokenRouter maintains over 99% of pure-LLM accuracy while reducing large-model activation to 56.2%–76.0% of generated tokens. Code is available at https://github.com/sznnzs/TokenRouter.

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.

Large language models (LLMs) excel at complex reasoning tasks but often suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates. We present a novel framework for computationally efficient, trustworthy reasoning under uncertainty, introducing two complementary techniques: Diversity-Aware Self-Signal Dilution (DASD) and Convergent Adaptive Weighted Sampling (CAWS). DASD operates in an unsupervised manner to dilute overconfident, semantically redundant reasoning paths, thereby producing better-calibrated internal confidence estimates. CAWS dynamically allocates computational resources at inference time by aggregating these signals and terminating computation once answer dominance and stability are achieved. Comprehensive experiments across three reasoning datasets demonstrate that our approach maintains accuracy levels while achieving over 70% reduction in inference cost, surpassing competitive baselines. Our framework provides a scalable, unsupervised solution for reliable and efficient LLM reasoning.