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ResearchHub represents a significant advancement in AI-driven scientific literature discovery, specifically optimizing retrieval for biomedical preprints using GPT-4o and advanced ranking techniques like Multiple Negatives Ranking. By achieving a 12.4 percentage point improvement in Recall@10 over existing methods, the system effectively addresses the challenge of near-duplicate recommendations through controllable novelty and diversity features. This tool demonstrates practical applications of large language models and dense retrieval architectures in accelerating research workflows within specialized domains.
RESEARCHHUB: A Personalized Literature Discovery System for Biomedical Preprints
arXiv:2610.28636v1 Announce Type: cross Abstract: We present ResearchHub, an interactive system for personalized literature discovery over biomedical preprints. Given a researcher profile or a free-form query, ResearchHub retrieves relevant papers from a domain-specific MedRxiv corpus and helps users move beyond near-duplicate recommendations through controllable novelty, diversity, and curriculum ordering. At the core of the system is prism, a bi-encoder retriever adapted to biomedical preprints using GPT-4o-generated natural language queries and a hybrid Multiple Negatives Ranking plus MarginMSE objective with cross-encoder soft labels. On 44,937 MedRxiv papers from 2023--2025, prism outperforms strong off-the-shelf dense retrievers, improving Recall@10 over SPECTER2-Search by 12.4 percentage points. ResearchHub further supports frontier retrieval based on a user's reading history, DPP-based diversity re-ranking, Local Intrinsic Dimensionality ordering from mainstream to specialized papers, and lightweight attribution cards for result explanation. The full pipeline runs with low latency, making the system suitable
Abstract:We present ResearchHub, an interactive system for personalized literature discovery over biomedical preprints. Given a researcher profile or a free-form query, ResearchHub retrieves relevant papers from a domain-specific MedRxiv corpus and helps users move beyond near-duplicate recommendations through controllable novelty, diversity, and curriculum ordering. At the core of the system is prism, a bi-encoder retriever adapted to biomedical preprints using GPT-4o-generated natural language queries and a hybrid Multiple Negatives Ranking plus MarginMSE objective with cross-encoder soft labels. On 44,937 MedRxiv papers from 2023--2025, prism outperforms strong off-the-shelf dense retrievers, improving Recall@10 over SPECTER2-Search by 12.4 percentage points. ResearchHub further supports frontier retrieval based on a user's reading history, DPP-based diversity re-ranking, Local Intrinsic Dimensionality ordering from mainstream to specialized papers, and lightweight attribution cards for result explanation. The full pipeline runs with low latency, making the system suitable
Information Retrieval (cs.IR); Computation and Language (cs.CL)
arXiv:2610.28636 [cs.IR]
(or arXiv:2610.28636v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2610.28636 Focus to learn more arXiv-issued DOI via DataCite
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