This study investigates a method that integrates retrieval-augmented mechanisms into large language model agents for scientific literature review generation. The approach addresses the limitations of traditional review models that rely on parametric knowledge with insufficient timeliness and limited coverage. Incorporating external document retrieval and dynamic information fusion into the generation process enhances the accuracy and completeness of the output. The overall framework consists of query encoding, semantic retrieval, document filtering, knowledge fusion, language modeling, task planning, memory storage, and reinforcement optimization, forming a closed loop of retrieval, understanding, and generation. Relevant document fragments are first retrieved through semantic vector search to ensure comprehensive and reliable information sources. These external representations are then integrated with the internal embeddings of the language model through weighted fusion, which preserves fluency while maintaining factual grounding. The task planning module constrains logical flow and text structure, and reinforcement learning optimization further improves relevance and consistency. Comparative experiments on large-scale scientific literature datasets demonstrate that the method outperforms existing approaches on ROUGE, BLEU, METEOR, and diversity metrics, validating its effectiveness and practicality. The findings show that combining retrieval augmentation with agent architectures can significantly improve coverage, accuracy, and language quality in review generation, providing a feasible solution for knowledge organization in complex literature environments.

Detecting anatomical tooth landmarks on intraoral 3D scans (IOS) is a key step in digital orthodontics, but supervised methods are limited by the few scans carrying landmark annotations. On the public Teeth3DS+ benchmark every scan has per-vertex tooth segmentation and FDI labels, whereas only a small subset also carries dental landmarks, so landmark detection becomes a semi-supervised problem in which abundant segmentation labels compensate for the missing landmark labels. We pretrain a shared point-cloud encoder on segmentation using all non-test scans, then train a landmark head with an equivariance consistency loss and confidence-filtered pseudo-labels on the unlabelled scans, plus a geometric prior that constrains each predicted landmark to the segmented region of its tooth. All model selection uses a held-out validation split; the test set serves only for final reporting. On the 3DTeethLand test split the method raises the mean Average Precision from 0.702 to 0.753 and lowers the mean radial error from 0.63 to 0.54 mm over a supervised baseline with the same labelled budget, ranking third of seven systems. Existing segmentation labels are thus an effective, low-cost signal for label-efficient 3D landmark detection.

This study investigates a method that integrates retrieval-augmented mechanisms into large language model agents for scientific literature review generation. The approach addresses the limitations of traditional review models that rely on parametric knowledge with insufficient timeliness and limited coverage. Incorporating external document retrieval and dynamic information fusion into the generation process enhances the accuracy and completeness of the output. The overall framework consists of query encoding, semantic retrieval, document filtering, knowledge fusion, language modeling, task planning, memory storage, and reinforcement optimization, forming a closed loop of retrieval, understanding, and generation. Relevant document fragments are first retrieved through semantic vector search to ensure comprehensive and reliable information sources. These external representations are then integrated with the internal embeddings of the language model through weighted fusion, which preserves fluency while maintaining factual grounding. The task planning module constrains logical flow and text structure, and reinforcement learning optimization further improves relevance and consistency. Comparative experiments on large-scale scientific literature datasets demonstrate that the method outperforms existing approaches on ROUGE, BLEU, METEOR, and diversity metrics, validating its effectiveness and practicality. The findings show that combining retrieval augmentation with agent architectures can significantly improve coverage, accuracy, and language quality in review generation, providing a feasible solution for knowledge organization in complex literature environments.

Artificial intelligence (AI) has rapidly transformed drug discovery by accelerating the identification, optimization, and substantiation of therapeutic candidates. Advances in deep learning, protein structure modeling, molecular simulation, and reproductive models have created unprecedented opportunities to decode the complexity of biological systems and design novel compounds with improved safety and efficacy profiles. Despite significant progress, the extent to which AI raises predictive truth, reduces experimental burdens, and amends drug-likeness across diverse therapeutic domains remains incompletely understood. This systematic review and meta-analysis synthesize evidence from studies employing AI-based structural modeling, molecular property forecasting, virtual screening, and de novo drug design. Databases including PubMed, Scopus, Web of Science, and IEEE Xplore were searched for articles published between 2005 and 2024. Eligible studies assessed AI performance in tasks such as protein structure prognostication, drug-target interaction inference, constipation affinity prediction, ADMET modeling, or molecular generation. Meta-uninflected pooling of performance indicators such as AUROC, RMSE, precision, recall, and top-k hit pace was done utilizing random-effects models. Overall, AI methodology significantly outperformed traditional computational approaches, yielding higher prognosticative accuracy, reduced false-positive rates, and improved structural generalization across chemical space. Deep erudition architectures, particularly graph neural networks and transformer-based models, achieve the highest carrying out gains. However, heterogeneity arose from differences in datasets, model training strategies, and the lack of standardized benchmarks. This review highlights the strengths, limitations, and translational potential of AI-driven drug discovery and provides recommendations for improving reproducibility, validation practice sessions, and clinical relevance in future studies.