The Retrieval-Augmented Generation (RAG) framework shows great potential in terms of improving the reasoning and knowledge utilization capabilities of language models. However, most existing RAG systems heavily rely on large language models (LLMs) and suffer severe performance degradation when using small language models (SLMs), which limits their efficiency and deployment in resource-constrained environments. To address this challenge, we propose Retrieval-Adaptive-Planning RAG (RAP-RAG), a lightweight and high-efficiency RAG framework with adaptive retrieval task planning that is compatible with both SLMs and LLMs simultaneously. RAP-RAG is built on three key components: (1) a heterogeneous weighted graph index that integrates semantic similarity and structural connectivity; (2) a set of retrieval methods that balance efficiency and reasoning power; and (3) an adaptive planner that dynamically selects appropriate strategies based on query features. Experiments on the LiHua-World, MultiHop-RAG, and Hybrid-SQuAD datasets show that RAP-RAG consistently outperforms representative baseline models such as GraphRAG, LightRAG, and MiniRAG. Compared to lightweight baselines, RAP-RAG achieves 3–5% accuracy improvement while maintaining high efficiency and maintains comparable efficiency in both small and large model settings. In addition, our proposed framework reduces storage size by 15% compared to mainstream frameworks. Component analysis further confirms the necessity of weighted graphs and adaptive programming for robust retrieval under multi-hop reasoning and heterogeneous query conditions. These results demonstrate that RAP-RAG is a practical and efficient framework for retrieval-enhanced generation, suitable for large-scale and resource-constrained scenarios.

Recent advancements in large language models (LLMs) have increased the necessity of alignment and safety mechanisms. Despite these efforts, jailbreak attacks remain a significant threat, exploiting vulnerabilities to elicit harmful responses. While white-box attacks, such as the Greedy Coordinate Gradient (GCG) method, have demonstrated promise, their efficacy is often limited by non-smooth optimization landscapes and a tendency to converge to local minima. To mitigate these issues, we propose Spatial Momentum GCG (SM-GCG), a novel method that incorporates spatial momentum. This technique aggregates gradient information across multiple transformation spaces—including text, token, one-hot, and embedding spaces—to stabilize the optimization process and enhance the estimation of update directions, thereby more effectively exploiting model vulnerabilities to elicit harmful responses. Experimental results on models including Vicuna-7B, Guanaco-7B, and Llama2-7B-Chat demonstrate that SM-GCG significantly enhances the attack success rate in white-box settings. The method achieves a 10–15% improvement in attack success rate over baseline methods against robust models such as Llama2, while also exhibiting enhanced transferability to black-box models. These findings indicate that spatial momentum effectively mitigates the problem of local optima in discrete prompt optimization, thereby offering a more powerful and generalizable approach for red-team assessments of LLM safety. Warning: This paper contains potentially offensive and harmful text.