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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation
%A Ye, Fuda
%A Wang, Jiachuan
%A Zhang, Yongqi
%A Chen, Lei
%A Li, Shuangyin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ye-etal-2026-bubblerag
%X 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.
%U https://aclanthology.org/2026.findings-acl.2163/
%P 43540-43558

Markdown (Informal)

[BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation](https://aclanthology.org/2026.findings-acl.2163/) (Ye et al., Findings 2026)

• BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation (Ye et al., Findings 2026)

ACL

• Fuda Ye, Jiachuan Wang, Yongqi Zhang, Lei Chen, and Shuangyin Li. 2026. BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 43540–43558, San Diego, California, United States. Association for Computational Linguistics.