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Noura is presented, which pairs LLM planning with executable personal specifications instantiated from public clinical guidance and individual health and dietary profiles, and turns executed plans and contextually credited revisions into supervision for compact models, recovering targets even from trajectories that nev...
This survey aims to systematically address resource efficiency challenges of Large Language Models by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs, and introduces a nuanced categorization of resource efficiency techniques by their specific resource types.
Cell type standardization plays a central role in integrating biological knowledge across single-cell studies. While standardized resources (e.g., Cell Ontology, Nomenclature Frameworks) provide unified vocabularies of cell populations, scientific publications and public datasets continue to use heterogeneous study-spe...
This survey aims to systematically address resource efficiency challenges in Large Language Models by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs, and introduces a nuanced categorization of resource efficiency techniques by their specific resource types.
DiffPro, a novel framework that integrates a Latent Diffusion Model (LDM) as a structural prior, confirms that the diffusion prior successfully regularizes the latent space, mitigating task representation collapse and enabling robust, high-precision predictions for combination therapy design.
LLMBDC (Large Language Model for Biological Domains Oriented Clustering of Gene Ontology) provides a scalable, reproducible, and interpretable route to context-aware, system-level interpretation of GO enrichment results while preserving biological specificity.
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