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Author: search.filters.author.Agrawal, HarshadaHas files: No

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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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    Detecting AI-Generated Scientific Abstracts Using Galactica and Graph Neural Networks
    (2025-01-01)
    Pathak, Anurag
    ;
    Sharma, Dilip Kumar
    ;
    Agrawal, Harshada
    ;
    Chawuthai, Rathachai
    ;
    Petchhan, Jirayu
    The rise of large language models has introduced new challenges in maintaining research integrity, particularly through the potential proliferation of AI-generated scientific content. This paper presents a novel hybrid framework that combines Galactica-a scientific language model developed by Meta AI-with Graph Neural Networks (GNNs) to detect AI-generated research abstracts. Leveraging the AI-GA dataset comprising 28,662 labeled abstracts, we extract domain-specific semantic embeddings using Galactica and construct a semantic similarity graph to approximate citation-like relationships. A two- layer GCN is then trained to classify each abstract as human- or AI-authored. Experimental results demonstrate that our method outperforms traditional baselines such as TF-IDF, RoBERTa, and perplexity-based detectors, while offering interpretable and scalable detection suitable for editorial screening pipelines.