{"slug": "repair-resolving-long-tail-confusion-in-scientific-retrievers-via-fact-verified", "title": "REPAIR: Resolving Long-Tail Confusion in Scientific Retrievers via Fact-Verified Iterative Refinement", "summary": "Researchers introduced REPAIR, a self-evolving data augmentation framework that iteratively synthesizes training data to resolve long-tail concept confusion in scientific dense retrievers, according to an arXiv paper (arXiv:2609.18262v1). REPAIR cycles through diagnosis of long-tail concepts, API-guided evidence expansion, and hard negative mining, and the authors report it significantly outperforms 19 strong baselines across nine materials science and biomedical benchmarks. The work argues that diagnosing and factually augmenting data for long-tail deficits is essential for robust scientific retrieval.", "body_md": "arXiv:2609.18262v1 Announce Type: new \nAbstract: Precise retrieval of scientific information is fundamentally constrained by long-tailed concepts and high fact-sensitivity of scientific corpora. These challenges often limit the effectiveness of dense retrievers and hallucination-prone LLM augmentation. To address this, we present REPAIR, a self-evolving data augmentation framework for scientific dense retrievers. REPAIR iteratively synthesizes training data to address knowledge gaps by cycling through diagnosis of long-tail concepts, API-guided evidence expansion, and differentiation via hard negative mining. This process effectively grounds retrieval in factual reality to resolve fine-grained distinctions. Extensive experiments demonstrate that REPAIR significantly outperforms 19 strong baselines on nine materials science and biomedical benchmarks. Our work highlights that diagnosing and factually augmenting data to long-tail deficits is essential for robust scientific retrieval.", "url": "https://wpnews.pro/news/repair-resolving-long-tail-confusion-in-scientific-retrievers-via-fact-verified", "canonical_source": "https://www.machinebrief.com/news/repair-resolving-long-tail-confusion-in-scientific-retriever-7c5g", "published_at": "2026-09-17 04:00:00+00:00", "updated_at": "2026-09-17 04:55:26.423757+00:00", "lang": "en", "topics": ["ai-research", "machine-learning", "natural-language-processing", "ai-tools"], "entities": ["REPAIR", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/repair-resolving-long-tail-confusion-in-scientific-retrievers-via-fact-verified", "markdown": "https://wpnews.pro/news/repair-resolving-long-tail-confusion-in-scientific-retrievers-via-fact-verified.md", "text": "https://wpnews.pro/news/repair-resolving-long-tail-confusion-in-scientific-retrievers-via-fact-verified.txt", "jsonld": "https://wpnews.pro/news/repair-resolving-long-tail-confusion-in-scientific-retrievers-via-fact-verified.jsonld"}}