Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text
arXiv:2607.08490v1 Announce Type: new Abstract: Biomedical language evolves rapidly as new discoveries emerge, causing traditional text models to lose semantic fidelity over time. Static embeddings and co-occurrence graphs cannot capture such evolution, leading to performance degradation in retrieval and knowledge discovery tasks. This paper introduces a Drift-Aware Temporal Graph Rewiring (DATGR) framework that models concept evolution by dynamically updating co-occurrence edges based on estimated semantic drift. Instead of retraining embeddings for each time slice, DATGR performs lightweight
Rapid evolution of biomedical language necessitates adaptive AI models capable of retaining semantic accuracy over time, reflecting an ongoing challenge in applying AI to dynamic knowledge domains.
This development addresses a critical limitation in AI's application to fields with rapidly evolving terminology, enhancing the reliability and long-term utility of AI systems for research and applied tasks.
AI models can now dynamically adapt to semantic drift without constant full retraining, leading to more robust and continuously relevant knowledge discovery and retrieval in fields like biomedicine.
- · Biomedical researchers
- · AI/ML developers
- · Healthcare sector
- · Pharmaceutical companies
- · Static AI model providers
- · Entities reliant on manual knowledge curation
Improved accuracy and efficiency of AI-powered knowledge discovery in biomedical research.
Accelerated pace of therapeutic and diagnostic development due to better understanding of scientific literature.
Potential for new drug targets and personalized medicine approaches identified by continuously updated AI models.
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Read at arXiv cs.AI