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@article{208557,
        author = {Uday Wahile and Dr. Himanshu V. Taiwade and Sumit Dhale and Gaurav Thakre and Yogesh Yadav and Ritik Dhodharmal},
        title = {OmniMind AI : Coordinating Diverse Large Language Models for Context-Aware Response Generation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {1975-1982},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208557},
        abstract = {No single large language model (LLM) is the best choice for every kind of question — one model may reason through mathematics more reliably, another may write more naturally, and a third may hold more current factual knowledge. OmniMind AI is proposed as a web platform that sends a single user query to several LLMs in parallel, scores and compares their responses, and merges the strongest parts of each into one polished answer rather than leaving the user to open several separate chat tools and compare them manually. This paper reviews recent research on multi-LLM systems — routing, cascading, ensembling and debate-based approaches — and studies how their design choices around cost, latency and answer quality apply to a platform built on the MERN stack (MongoDB, Express.js, React.js and Node.js). Techniques such as preference-based routing, pairwise ranking with generative fusion, and layered aggregator models are compared for their suitability to a real-time, browser-based product. The paper also lays out the proposed OmniMind AI architecture, its intended advantages and application areas, and the open challenges — latency from parallel API calls, response-fusion quality, and per-query cost — that any production-grade multi-LLM aggregator has to address.},
        keywords = {Multi-LLM Systems, Response Aggregation, LLM Routing, Ensemble Learning, MERN Stack, Prompt Orchestration, Large Language Models.},
        month = {September},
        }