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arXiv · 2507.03329

NDAI-NeuroMAP: A Neuroscience-Specific Embedding Model for Domain-Specific Retrieval

Devendra Patel·Aaditya Jain·Jayant Verma·Divyansh Rajput·Sunil Mahala·Ketki Suresh Khapare·Jayateja Kalla
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Abstract

We present NDAI-NeuroMAP, the first neuroscience-domain-specific dense vector embedding model engineered for high-precision information retrieval tasks. Our methodology encompasses the curation of an extensive domain-specific training corpus comprising 500,000 carefully constructed triplets (query-positive-negative configurations), augmented with 250,000 neuroscience-specific definitional entries and 250,000 structured knowledge-graph triplets derived from authoritative neurological ontologies. We employ a sophisticated fine-tuning approach utilizing the FremyCompany/BioLORD-2023 foundation model, implementing a multi-objective optimization framework combining contrastive learning with triplet-based metric learning paradigms. Comprehensive evaluation on a held-out test dataset comprising approximately 24,000 neuroscience-specific queries demonstrates substantial performance improvements over state-of-the-art general-purpose and biomedical embedding models. These empirical findings underscore the critical importance of domain-specific embedding architectures for neuroscience-oriented RAG systems and related clinical natural language processing applications.

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Devendra Patel, Aaditya Jain, Jayant Verma, Divyansh Rajput, Sunil Mahala, Ketki Suresh Khapare, Jayateja Kalla. 2025-07-04. NDAI-NeuroMAP: A Neuroscience-Specific Embedding Model for Domain-Specific Retrieval. https://arxiv.org/abs/2507.03329

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