## 8 Featured Application  
The rapid progress of MLLMs has opened up diverse application domains where natural language generation is required to be grounded in multimodal inputs. MLLMs have demonstrated strong potential in a wide range of use cases including image-text visual question answering (VQA) [27], medical image captioning [52; 53], multimodal dialogue systems [54; 55], robotics-based visual reasoning [56], legal document visual summarization [38], and math education support systems [15; 27]. These applications leverage the capability of MLLMs to reason across textual, visual, and sometimes auditory modalities to deliver more informed and context-aware responses.  
Despite these promising applications, deploying MLLMs in real-world scenarios faces several key challenges. One major limitation arises from the computational overhead of advanced prompt engineering strategies. Specifically, the proposed Greedy PES exhaustively explores all available combinations of prompts to identify optimal strategies for a given dataset and model. This approach, while empirically effective, is computationally intensive and resource demanding, making it less feasible in resource-constrained environments such as mobile or embedded devices [57; 58].  
To mitigate such constraints, recent work has proposed several solutions. For example, meta prompt selectors dynamically choose suitable prompts based on input domain or task characteristics [59]; heuristic rules can be used to predefine prompt configurations based on prior dataset analysis [38]; and prompt distillation techniques attempt to consolidate multiple prompt types into a unified, lighter-weight form [58]. These approaches enable more scalable and deployment-friendly usage of prompt engineering in practical settings.  
Additionally, the effectiveness of each PE technique, such as ICL [60], CoT [15], SSR [19], ToT [19], and RAG [20], varies considerably depending on the task domain and dataset characteristics. For instance, ToT has proven particularly effective in scenarios requiring structured reasoning over visual inputs, while RAG is optimal in tasks that demand external knowledge retrieval and grounding, such as in scientific QA tasks [59]. These findings suggest that domain-aware prompt adaptation is essential for achieving optimal performance across applications.  
While MLLMs have demonstrated strong generalization and reasoning capabilities, their effective deployment in real-world applications relies heavily on prompt strategies that are computationally efficient, domain-specific, and adaptively optimized. The proposed Greedy PES provides an empirical framework for identifying such strategies  
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**PE** & **Answer** \\ \hline B & The image depicts a woman in a white dress being held by two men, one wearing a mask and the other with a fur stole, in an outdoor setting with a wooden fence and trees in the background. \\ \hline I & a woman in a white dress is standing between two men \\ \hline C & A woman in a white dress is being led by two men, one wearing a mask and the other with a fur stole, in a grassy outdoor setting. \\ \hline R(B) & A woman in a white dress is standing between two men in a field. \\ \hline S & In the image, a person in a white feathered dress is being held by two individuals, one wearing a mask and the other with a fur stole, in an outdoor setting with a wooden fence and trees in the background. \\ \hline T & The image depicts a surreal scene with a person in a white dress holding hands with two individuals dressed in unconventional attite, one wearing a mask and the other draped in a fur-like garment, set against a rustic outdoor backdrop. \\ \hline R(C) & A woman in a white dress is standing between two men in a field. \\ \hline \hline \end{tabular}
\end{table}
Table 21: Image captioning results for Figure 1 (Qwen2).  
but also highlights the need for future research in lightweight and domain-adaptive prompt optimization.