Large language models demonstrate exceptional capabilities but raise critical safety concerns due to toxic outputs causing potential harm to users. Prompt optimization has emerged as a practical detoxification solution because it operates at the input level and can be applied to black-box or API-based models without parameter access. However, existing methods treat each task in a stateless manner without leveraging prior optimization experiences, which results in either ineffective detoxification or over-sanitized prompts that lose original intent in the vast search space. To address this, we propose the Memory-INformed Detoxification (MIND) framework that harnesses historical optimization experiences to enhance detoxification effectiveness through Memory Retrieval (MR) for retrieving relevant past cases and Memory Maintenance (MM) for maintaining a representative memory pool. We formulate MR and MM as optimization problems, prove their NP-hardness, and develop two approximation algorithms with constant approximation ratios for these problems. Experiments across multiple datasets and model configurations show that MIND outperforms existing baselines in both toxicity reduction and response utility.

Retrieval-augmented generation (RAG) extends the capabilities of large language models (LLMs) by providing access to external knowledge. However, traditional retrieval-augmented LLMs rely on a silent reading paradigm that processes all retrieved documents passively, forcing them to reason without any interaction with the documents. This paradigm contrasts sharply with human interactive reading behavior, where external tools, such as bookmarks and notes, are used to offload cognitive demands. This paper introduces BubbleRAG, an enhanced RAG framework that emulates human interactive reading through annotation and re-reading. Specifically, BubbleRAG utilizes a lightweight thought bubble module that offloads LLM’s internal cognition into external bookmark tokens, which are then annotated back into the context. These bookmarks serve as externalized memory, allowing the LLM to revisit these annotations in subsequent reading and answering. Notably, BubbleRAG is particularly suitable for low-resource scenarios, as the LLM parameters remain frozen. Extensive experiments confirm the effectiveness, robustness, and generalizability of BubbleRAG. Our findings demonstrate that BubbleRAG enables LLMs to achieve superior evidence identification abilities typically seen in retrievers, while establishing a cognitive link between external and internal information during answer generation. The source code is available at https://github.com/yefd/BubbleRAG.