Large reasoning models (LRMs) often suffer from overthinking, continuing with redundant verification and repeated attempts even after reaching a correct solution.
This wastes computation and can overturn an initially correct answer.
We show that the transition from productive to redundant reasoning is decodable from late-layer hidden states.
These representations separate the two phases around first-correct-solution boundaries beyond what position and lexical controls explain, and distinguish correct from incorrect solution endings.
Building on this finding, we propose ROM (Real-time Overthinking Mitigation), which uses a lightweight streaming detector (0.07--0.25% of backbone parameters) to monitor a frozen LRM and intervene at well-formed reasoning boundaries without answer extraction or probe decoding.
Counterfactual Self-Correction (CSC) augments detector training with wrong→correct trajectories to preserve useful self-correction.
Heads are trained separately for each backbone using shared MATH500 supervision and transfer zero-shot to four unseen benchmarks.
Evaluated against ten baselines across five backbones from three families and five benchmarks, ROM$_{\text{CSC}}$ reduces response length by 28--77% (45% on average), with significant reductions in all 25 settings and accuracy matching or exceeding vanilla decoding in 22.
Measurements on two backbones further show 20--70% lower end-to-end latency with less than 0.5% detector overhead per decoding step.
Code is available at https://github.com/SaFo-Lab/ROM.