## 2 Inference and the INFINITE Methodology
While the INFINITE methodology evaluates Efficiency, Consistency, and Accuracy in code generation, we acknowledge additional factors such as robustness [47], generalization [48], interpretability [49], bias [50], complexity [51], scalability [52], adaptability [53], and resource consumption [54]. However, these aspects are implicitly incorporated into the existing metrics for evaluating LLM-assisted code generation. Bias in code generation primarily affects stylistic preferences, library selection, and minor syntactic variations, rather than fundamental correctness [55], and it is harder to define quantitatively. Instead of directly measuring bias, Consistency and Efficiency indirectly expose biased tendencies by assessing variability in responses to equivalent prompts (Consistency) and differences in processing times that could indicate bias toward specific problem types (Efficiency). Similarly, complexity is inherently captured by the need for structured, logically sound code--our Accuracy metric ensures correctness. At the same time, Consistency reflects the model's ability to handle increasingly intricate tasks without requiring multiple attempts. Scalability is closely tied to Efficiency and Accuracy, as models that struggle to generate more extended or more intricate code snippets tend to exhibit increased response times and inconsistencies in output quality. Robustness and generalization are inherently addressed through Consistency, as models that fail to generalize require multiple attempts to produce valid code. Explainability in code generation tasks is indirectly verified through code execution and the correctness of the produced outputs, making it less of a primary concern compared to decision-making AI systems. Adaptability is indirectly accounted for through Efficiency; if a model requires significantly more time or attempts to produce correct outputs for complex prompts, it indicates difficulty adapting to unfamiliar patterns. Finally, resource consumption is inherently tied to Efficiency, as models that require excessive attempts or longer inference times naturally have higher computational costs. Thus, while INFINITE does not explicitly quantify these additional factors, it offers a streamlined and practical framework for assessing LLMs in code generation, prioritizing aspects most relevant to developers and end users. Some of these additional properties may not be readily and explicitly quantifiable in the context of LLM-assisted code generation. Metrics such as robustness, interpretability, and adaptability often require subjective or task-specific assessments, making them difficult to integrate into a standardized framework like INFINITE. We do acknowledge, however, their importance in broader AI evaluation, and in future work, we aim to incorporate as many of these factors as possible to extend the INFINITE methodology beyond code generation. We aim to evolve the Inference Index (InI) into a more comprehensive assessment framework applicable to a wide range of LLM tasks, thereby establishing it as a general benchmark for evaluating language models across diverse domains. Before concluding this discussion, we make a final note on computational resources, another aspect frequently considered in AI evaluation. Given that GPT-based models operate via cloud-based APIs, their exact resource consumption remains unknown; however, we evaluated execution times on local hardware to ensure practical applicability. We assessed the computational efficiency of the generated programs by running them on an Intel Core i5-9300 HF, 2.40 GHz, Quad-Core processor with 8 GB RAM. All AI-generated codes and the human-written implementation were executed within a few seconds on this hardware, demonstrating their efficiency. These results suggest that the generated codes do not introduce significant computational demands and can be run on standard consumer-grade personal computers without performance issues.