Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective

Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to ground their outputs in external knowledge. This view, however, is incomplete when considered within a broader historical context. In this paper, we argue that the core ideas underlying RAG are not new: foundational concepts such as integrating retrieval and language generation, knowledge augmentation, answer verification, and iterative query (or prompt) refinement had already been studied and instantiated in information retrieval (IR) and question answering (QA) research dating back to the early 2000s, well before the emergence of LLMs. We make this case by systematically tracing the intellectual lineage of modern RAG and Agentic RAG back to their classical IR and QA antecedents, and examining why this continuity has gone under-recognized -- a consequence of community fragmentation, shifting terminology, and the recency bias endemic to fast-moving fields. Rather than treating LLMs as the origin point of retrieval-augmented intelligence, we propose viewing them as a new interface layer atop a decades-old QA architecture. This reframing is not merely historical: by situating RAG within the longer trajectory of IR research, we surface underutilized prior work -- on user modeling, answer validation, and query refinement -- that can directly inform next-generation RAG design, reducing unintentional rediscovery and fostering genuine cross-community integration.

TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning

The selection of base station sites is a critical challenge in 5G network planning, which requires efficient optimization of coverage, cost, user satisfaction, and practical constraints. Traditional manual methods, reliant on human expertise, suffer from inefficiencies and are limited to an unsatisfied planning-construction consistency. Existing AI tools, despite improving efficiency in certain aspects, still struggle to meet the dynamic network conditions and multi-objective needs of telecom operators' networks. To address these challenges, we propose TelePlanNet, an AI-driven framework tailored for the selection of base station sites, integrating a three-layer architecture for efficient planning and large-scale automation. By leveraging large language models (LLMs) for real-time user input processing and intent alignment with base station planning, combined with training the planning model using the improved group relative policy optimization (GRPO) reinforcement learning, the proposed TelePlanNet can effectively address multi-objective optimization, evaluates candidate sites, and delivers practical solutions. Experiments results show that the proposed TelePlanNet can improve the consistency to 78%, which is superior to the manual methods, providing telecom operators with an efficient and scalable tool that significantly advances cellular network planning.