Spike-Triggered Adaptive Reasoning Steering to Mitigate Cognitive Inertia in Large Reasoning Model

While Large Reasoning Models (LRMs) have achieved remarkable performance by scaling test-time compute, they frequently suffer from cognitive inertia, a failure pattern manifesting in two distinct modes: 1) overthinking, where models spiral into redundant verification loops long after a solution is reached, and 2) reasoning rigidity, where models stubbornly adhere to incorrect parametric priors even when they contradict explicit task constraints. Previous inference-time methods designed to mitigate these reasoning failures primarily focus on overthinking via suppressive early-exit strategies. However, by relying on surface-level textual heuristics that serve as unreliable proxies for reasoning quality, these methods often prematurely terminate potentially recoverable reasoning paths. Furthermore, they remain fundamentally blind to reasoning rigidity, as they lack any mechanism to redirect a model that has confidently committed to an incorrect trajectory. To address these limitations, we propose STARS (Spike-Triggered Adaptive Reasoning Steering), a training-free framework designed to actively correct reasoning trajectories by monitoring internal latent dynamics. STARS identifies cognitive pivots—critical moments of reasoning transition—by detecting distinct distance spikes in the hidden states. Upon detection, the framework employs geometric trajectory analysis to diagnose the structural nature of the transition and injects state-aware language cues to steer the model on-the-fly. Our experiments across diverse benchmarks show that STARS consistently improves accuracy—by up to 6.6% on AIME25 and 6.5% on ConditionedMath—while consistently lowering end-to-end latency relative to existing baselines. By resolving internal conflicts rather than merely truncating output, STARS ensures a superior accuracy-compute trade-off, effectively rescuing failed reasoning paths.