## 5 Experiments
### Ablation Study

Table 12: Response ACC comparison after correction for three types of perturbations (SwapAlpha, SwapWord, CaseAlpha) at 100% intensity across four datasets (MMLU, LogQA-en, LSAT, and SAT-en) using four LLMs (GLM4, Llama3.1, Qwen2.5, and Falcon3-Lambda). For each perturbation type, the table presents the ACC values after CTC Detection (CTC), TextBlob, and the self-correction reflection (Ours). historical semantic information during the self-correction process. For MSCS, the full method (Ours) outperforms the ablation variant (w/o HECP) across a variety of perturbation types, suggesting that HECP mitigates the error propagation caused by semantic drift. For ACC, all models show a consistent trend that the full method (Ours) outperforms the ablated variant (w/o HECP) across perturbation types, especially in datasets with strong reasoning capabilities such as LogiQA-en and LSAT, highlighting the role of HECP in preserving task-specific knowledge during perturbation recovery. Notably, the performance gap is larger for CaseAlpha/SwapAlpha compared to SwapWord, suggesting that HECP is critical for maintaining contextual semantic consistency when corrections corrupt the lexicon itself. Together, these findings confirm that the consistency principle enforced by history augmentation is essential for robust self-correction under semantic perturbations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**LLMs**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**MSCS of SwapAlpha**} & \multicolumn{2}{c}{**MSCS of SwapWord**} & \multicolumn{2}{c}{**MSCS of CaseAlpha**} \\  & & **w/o HECP** & **Ours** & **w/o HECP** & **Ours** & **w/o HECP** & **Ours** \\ \hline \multirow{4}{*}{GLM4} & MMLU & 0.9913 & 0.9936 & 0.9985 & 0.9986 & 0.9922 & 0.9939 \\  & LogiQA-en & 0.9885 & 0.9922 & 0.9982 & 0.9983 & 0.9878 & 0.9924 \\  & LSAT & 0.9478 & 0.9606 & 0.9897 & 0.9902 & 0.9553 & 0.9632 \\  & SAT-en & 0.8284 & 0.8571 & 0.9059 & 0.9129 & 0.8470 & 0.8672 \\ \hline \multirow{4}{*}{Llama3.1} & MMLU & 0.4068 & 0.4726 & 0.6590 & 0.6741 & 0.5589 & 0.6035 \\  & LogiQA-en & 0.3820 & 0.4346 & 0.6931 & 0.7040 & 0.5735 & 0.6148 \\  & LSAT & 0.3282 & 0.3772 & 0.6727 & 0.6828 & 0.5177 & 0.5505 \\  & SAT-en & 0.4452 & 0.4848 & 0.5649 & 0.5753 & 0.6019 & 0.6381 \\ \hline \multirow{4}{*}{Qwen2.5} & MMLU & 0.5997 & 0.6472 & 0.8355 & 0.8431 & 0.6190 & 0.6697 \\  & LogiQA-en & 0.5460 & 0.5946 & 0.8364 & 0.8420 & 0.5660 & 0.6308 \\  & LSAT & 0.5089 & 0.5561 & 0.8212 & 0.8261 & 0.5286 & 0.5891 \\  & SAT-en & 0.6454 & 0.6833 & 0.8257 & 0.8280 & 0.6644 & 0.7086 \\ \hline \multirow{4}{*}{Falcon3-Mamba} & MMLU & 0.8047 & 0.8177 & 0.8801 & 0.8847 & 0.8460 & 0.8547 \\  & LogiQA-en & 0.8056 & 0.8167 & 0.8818 & 0.8872 & 0.8494 & 0.8595 \\ \cline{1-1}  & LSAT & 0.7487 & 0.7605 & 0.8526 & 0.8561 & 0.8109 & 0.8214 \\ \cline{1-1}  & SAT-en & 0.7660 & 0.7788 & 0.8495 & 0.8535 & 0.8322 & 0.8441 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Response MSCS comparison of the self-correction reflection (Ours) with the ablated variant (w/o HECP) across three perturbation types at 100% intensity, evaluated on four datasets using four LLMs.