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    Modular LLM Architecture with Pluggable Reasoning Heads: A Scalable Approach to Multi-Modal AI Reasoning
    (2025-01-01)
    Pathak, Anurag
    ;
    Sharma, Dilip Kumar
    ;
    Agrawal, Harshada
    ;
    Chawuthai, Rathachai
    ;
    Petchhan, Jirayu
    This paper introduces a modular architecture for Large Language Models (LLMs) that incorporates pluggable, domain-specific reasoning heads to augment the model's capabilities beyond conventional text generation. Central to our approach is an attention-routing controller that intelligently classifies and dispatches user prompts to appropriate reasoning modules - namely symbolic, logical, or graph - based heads-based on the prompt's structure and intent. This design enables hybrid, multi-paradigm reasoning without requiring retraining or finetuning of the base LLM. By decoupling reasoning tasks from general language understanding, our system improves both computational efficiency and interpretability. We demonstrate the architecture using Groq as the base LLM and integrate lightweight engines such as SymPy for symbolic mathematics and custom modules for logical and graph-based reasoning. Experiments across a suite of structured and unstructured prompts show a significant reduction in inference latency and token usage, along with higher accuracy and better explainability. The proposed framework offers a scalable foundation for embedding modular reasoning capabilities into modern LLM-driven applications.
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    Explainable AI (XAI) for Breast Cancer Diagnosis
    (2024-01-01)
    Ariyametkul, Awika
    ;
    Tamang, Sudarshan
    ;
    Paing, May Phu
    Breast cancer is the leading cause of mortality and incidence among women worldwide. Mammography, an essential imaging technique, plays a pivotal role in both screening and diagnostic processes by facilitating early detection, which helps improve survival rates. Despite its effectiveness, interpreting mammographic images presents considerable challenges, necessitating the expertise of highly trained radiologists. Artificial intelligence (AI) is a powerful tool for managing large amounts of data and is increasingly used across numerous sectors, including medical applications. This research focuses on applying Convolutional Neural Networks (CNNs) to classify breast cancer from mammograms. We explored six different CNN models including simple ConvNet, AlexNet, VGG-16, GoogLeNet, XceptionNet, and DenseNet201. Our results indicate that DenseNet201 is the most suitable model for this task, achieving 99% accuracy. However, a limitation of AI is the lack of transparency and explanation, often referred to as the 'black box' problem. This vulnerability can be addressed through explainable artificial intelligence (XAI), which elucidates the processes behind AI's decision-making. We employed three different XAI methodologies, including LIME, GradCAM, and GradCAM++, to visualize the model's decision-making process.