## 7 Simulation Result
### Performance Analysis and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Metric** & **MSCOCO** & **Flickr30k** & **nocaps** & **ScienceQA** & **MathVista** & **CVBench** \\ \hline
**Category** & **General** & **General** & **General** & **Science** & **Math** & **General** \\ \hline \multicolumn{6}{c}{Phi} \\ \hline BLEU & R(B) & R(B) & I & R(B) & S & B \\ \hline ROUGE & R(B) & I & I & R(B) & I, S & I \\ \hline METEOR & R(B) & I & R(B) & R(B) & I, S & I \\ \hline S-BERT & T & C, I & C & I, S & I & I \\ \hline MoverScore & R(B) & I & I & I, S & I & I \\ \hline CIDEr & B & B & B & R(B) & I, S & I \\ \hline Best PES & R(B) & I & I & R(B) & I, S & I \\ \hline \multicolumn{6}{c}{Llama} \\ \hline BLEU & I & I & I & R(B) & C, T & I \\ \hline ROUGE & I & I & I & I & I, S & I, T \\ \hline METEOR & I & C & I & R(B) & I, S & I, T \\ \hline S-BERT & C & C & C & I & I, T & I, T \\ \hline MoverScore & C & C & C & I & I, T & I, T \\ \hline CIDEr & I, S & C & C, S & R(B) & I & I, T \\ \hline Best PES & I & C & I & I & I, T & I, T \\ \hline \multicolumn{6}{c}{Pixtal} \\ \hline BLEU & T & T & T & R(B) & C & I \\ \hline ROUGE & T & T & T & S & T & S \\ \hline METEOR & T & T & T & R(B) & C, T & I, T \\ \hline S-BERT & T & T & T & S & S & I, T \\ \hline MoverScore & T & T & T & S & T & T \\ \hline CIDEr & R(B) & C, I & R(B) & S & T & S \\ \hline Best PES & T & T & T & S & T & S \\ \hline \multicolumn{6}{c}{Qwen} \\ \hline BLEU & T & T & C & B & C & S \\ \hline ROUGE & T & T & T & S & T & S \\ \hline METEOR & T & T & C, I & S & T & S \\ \hline S-BERT & T & T & C & S & S & S \\ \hline MoverScore & T & T & T & S & S & S \\ \hline CIDEr & I, T & B & R(T) & S & T & S \\ \hline Best PES & T & T & T & S & T & S \\ \hline \multicolumn{6}{c}{Performance Enhancement Summary} \\ \hline Best MLLM & Qwen & Qwen & Qwen & Qwen & Phi & Qwen \\ \hline Best MLLM with PES & Phi & Qwen & Qwen & Phi & Qwen & Phi \\ \hline Performance Enhancement & 132.3\% & 78.1\% & 37.3\% & 49.1\% & 90.3\% & 489.7\% \\ \hline \end{tabular}
\end{table} 
Table 16: Comparison of results across MLLM models and datasets using different metrics (The boldfaced numbers indicate the highest performance for each language model across different PES variants). emerges as the best-performing model. However, when applying Greedy PES, the optimal MLLM-PES combination shifts to Qwen-2 with ToT, demonstrating that the ToT framework enhances logical reasoning and enables structured multi-step problem solving, particularly for mathematical tasks requiring iterative hypothesis evaluation and validation. CVBench serves as a computer-vision-focused multimodal benchmark, where models are assessed on object recognition and scene description based on image-text relationships. In the baseline setting (B), Phi-3.5 and Qwen-2 achieve the highest performance, while Greedy PES identifies Phi-3.5 with ICL as the optimal combination. This finding indicates that ICL effectively optimizes image descriptions by incorporating diverse in-context examples, making it the most suitable approach for tasks requiring fine-grained multimodal understanding. Ultimately, the application of the Greedy PES resulted in significant performance improvements across different multimodal tasks. The observed performance improvements are as follows:  
* 184.3% increase in evaluation scores for general image captioning tasks compared to conventional methods. * 90.3% increase in evaluation scores for mathematical VQA. * 49.1% increase in evaluation scores for science VQA. These results underscore the importance of prompt engineering in MLLM optimization, illustrating how Greedy PES can significantly enhance model performance by aligning multimodal reasoning techniques with dataset-specific requirements.