RAG, Agents, and Multimodal Large Models: Evolution, Challenges, and Future Trends

1. Retrieval‑Augmented Generation (RAG)

RAG combines information retrieval with generative models, allowing large language models (LLMs) to fetch up‑to‑date external knowledge before generating answers, thereby overcoming static knowledge limits, improving timeliness, privacy, interpretability, and cost efficiency.

Key challenges include document preprocessing, chunking, vectorization, and controllable retrieval, especially for multimodal documents and large‑scale data.

2. AI Agents

Agents integrate LLMs with planning, feedback, and tool‑use to achieve autonomous decision‑making and environment interaction. They can be classified as autonomous agents or generative agents, with frameworks such as MetaGPT and AutoGen facilitating multi‑agent collaboration.

Multi‑agent systems enable complex task decomposition, parallel execution, and robustness, but face challenges in safety, alignment, and explainability.

3. Multimodal Large Models

Multimodal models unify vision and language tasks—such as object detection, segmentation, and OCR—into a single model, enhancing visual grounding and cross‑modal alignment. Recent work from teams like Zidu Taichu, 360 Research Institute, and Tencent demonstrates applications in open‑world object detection and video‑content moderation.

4. Future Development Trends

Future large‑model development is expected to converge RAG, agents, and multimodal capabilities into fully integrated intelligent systems that can reason, plan, and act across modalities, driving industry transformation in areas like robotics, smart grids, and healthcare.