# Exploring the Limits of Large Language Models' Ability to Distinguish Between Objects
## 5 Discussion
### Challenges and Limitations in Both Classification Scenarios  
Across both tasks, the performance of LLMs revealed several key limitations related to processing and classifying complex scenes. In particular, our analysis identified the following challenges in both classification evaluations:  
* Limited Spatial Recognition: In the real vs. fake classification task, models struggled to interpret spatial arrangements. This limitation was evident as the systems frequently misclassified frames containing multiple similar objects, which indicates that the models do not reliably differentiate spatially close or overlapping items. This poses significant implications for practical applications of LLMs such as quality inspection and monitoring systems, where precise identification and differentiation of closely positioned or overlapping objects is critical to operational accuracy.
* Challenges in Multi-Class Discrimination: In the person, mannequin, and banner classification task, the models' difficulties in separating similar object types on complex visual backgrounds underscored a deficiency in multi-class recognition. This limitation is evident in real-world environments like security surveillance, where accurate  
Figure 11: Human classification Q2 results (red: incorrect object classification; yellow: correct object classification but cannot give the correct order; green: correct object classification and gives the right order).  
identification amidst visually complex backgrounds directly impacts decision-making and operational efficiency.
* High Incidence of Missing Values: Both tasks demonstrated significant rates of missing data points. Such gaps imply that the models occasionally fail to register crucial details within an image, which further affects overall performance in accurately capturing scene dynamics. For practical systems, particularly in quality assurance and monitoring applications, missing critical visual cues can lead to significant oversights, potentially compromising reliability and system integrity. Techniques such as retrieval-augmented generation (RAG) may offer a promising approach to mitigating this issue by enhancing model comprehension of fine-grained image contexts.
* Prevalence of Misclassifications: The occurrence of numerous misclassifications across tasks reinforces the notion that the models struggle with integrating contextual and spatial information effectively. These errors highlight underlying issues in neural architectures when confronted with varying object poses, densities, and subtle visual distinctions. In real-world scenarios, particularly in precision-driven sectors such as industrial quality control, frequent misclassifications could substantially undermine productivity, accuracy, and safety.  
These limitations suggest that further architectural adjustments are necessary. Integrating multimodal training methods--by combining both textual and visual data--could enhance spatial processing and improve class differentiation. Such improvements would broaden the applicability of LLMs to fields like inventory management, quality control, and surveillance, where accurate and precise categorization is critical.