While LLMs demonstrate impressive reasoning capabilities, their internal decision dynamics remain opaque. To render these process interpretable and intervenable, we propose Dynamic Entropy Tracing, a mechanism-aware framework that interprets the evolving "choice state" of attention heads during CoT generation through stepwise head-wise option-logit and entropy tracing. Our analysis reveals distinct functional behaviors at attention heads: Steadfast Heads, characterized by consistently low entropy and producing a sharp, option-selective logit pattern with a stable top choice, and Wavering Heads, characterized by consistently high entropy and producing flat or oscillatory option logits without a persistent winner. Leveraging these traces, we identify a set of intervention targets and perform Selective Head Fine-Tuning, updating solely these selected heads against a frozen backbone. Experiments across the LLaMA and Qwen families reveal a striking plasticity hierarchy: fine-tuning just 30 Wavering Heads recovers over 98% of the performance achieved by full-parameter tuning, and in some settings modestly exceeds it. In contrast, intervening on Steadfast Heads yields much less gains. Our findings translate process-level mechanistic observables into a principled criterion for selective fine-tuning, offering a fundamental insight: the most effective tuning knobs are not the components that signal the final decision, but those that retain uncertainty, and thus plasticity, during its formation.