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.

This study proposes a multi-scale LoRA fine-tuning recommendation algorithm based on large language models to address the limitations of traditional recommender systems in semantic understanding, feature redundancy, and parameter transfer efficiency. The method preserves the semantic representation ability of large models while achieving unified modeling of global preferences and local interests through multi-scale semantic decomposition, low-rank parameter adaptation, and cross-scale fusion mechanisms. The model first inputs user-content interaction sequences into a pre-trained language model to obtain context-aware semantic embeddings. Then, a multi-scale semantic pooling structure extracts hierarchical feature information to capture multi-granularity preference relations. Based on this, a multi-scale LoRA module performs low-rank decomposition and cross-scale alignment of weight matrices, significantly reducing parameter size and improving fine-tuning efficiency. Finally, a cross-scale attention fusion layer dynamically reconstructs global and local features to optimize recommendation ranking. Systematic experiments conducted on the MovieLens-1M dataset validate the effectiveness of the proposed method across multiple evaluation metrics. The results show that the model outperforms several baseline algorithms in Precision@K, NDCG@K, Recall@K, and Coverage, demonstrating the advantages of multi-scale structure and LoRA parameterization in enhancing recommendation accuracy, diversity, and generalization. Overall, this research provides a feasible solution for structural optimization and parameter-efficient fine-tuning of large language models in efficient recommendation tasks.

To address the difficulty of maintaining stable structural modeling in intelligent decision systems under complex dynamic environments and the sensitivity of decision behavior to state uncertainty, this study proposes an intelligent decision modeling method based on structure aware learning. The method constructs structured state representations that map environmental observations, state evolution relationships, and decision processes into a unified representation space. This design enables explicit modeling of internal structural constraints and dynamic dependencies within the system. During decision modeling, structural constraints are introduced to guide policy learning, allowing decision behavior to remain consistent and stable under long term optimization objectives. The proposed framework effectively captures state evolution patterns in complex state spaces and mitigates decision instability caused by excessive reliance on local information in traditional methods. Comparative evaluations demonstrate clear advantages in decision utility, decision stability, state transition modeling accuracy, and representation consistency. These results indicate that structure aware learning provides a more reliable modeling foundation for intelligent decision systems. The findings further show that systematically integrating structural information into decision modeling improves overall performance and trustworthiness in complex dynamic scenarios.