Advancing AI for India: A Survey on Hierarchical Reasoning and Modular Enhancements in Large Language Models

Abstract

Large Language Models (LLMs) have changed how computers read and write text. They are trained on vast amounts of data and can provide clear answers and perform multiple tasks. Rather than just expanding these models, researchers have recently begun including tools, retrieval techniques, and step-by-step reasoning. Retrieval-Augmented Generation (RAG), tool-using agents like Toolformer and ReAct, and planning techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT) are a few examples. Models are made lighter and faster with the use of Mixture of Experts (MoE) and effective fine-tuning techniques such as QLoRA and AWQ. Multiple levels of reasoning that function at varying speeds are added by Hierarchical Reasoning Models (HRMs). This paper gives a clear explanation and comparison of these ideas. Additionally, it examines actual application cases in various Indian fields, including local languages, healthcare, law, education, agriculture, and governance. The study covers the benefits and drawbacks of multiple strategies. The key ideas are: (1) LLMs remain essential as the fundamental language engine; (2) tools and retrieval help in error reduction; and (3) hierarchical plans improve model thinking but increase system complexity. We conclude with suggestions for adapting AI to Indian languages and future research concerns.

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