Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Snap-and-ask retrieval is now one of the most common entry points for mobile AI, yet photos are often blurry, while text questions may be short or mistyped. Existing benchmarks only test on clean inputs or do not isolate paired robustness in snap-and-ask retrieval. Therefore, we introduce SnapBench, the first paired benchmark for robust snap-and-ask multimodal retrieval, spanning 1,145 queries, 9,085 gallery items under 53 controlled corruption conditions with human annotations. We evaluate 16 multimodal retrievers, covering dual-tower encoders and embedding-based VLMs. Results show that image corruptions substantially degrade retrieval, while text corruptions mainly affect text-only retrieval and have limited impact on joint retrieval. Clean image-only retrieval often outperforms joint retrieval, indicating the coarse-text drag and the lack of cross-modal fallback under noisy inputs. SnapBench provides a controlled testbed for evaluating robust retrieval in snap-and-ask scenarios. We further propose MOOR (Modality-anchored, Outlier-aware, Optimal Reweighting), a simple adaptive fusion approach, highlighting the need for reliability-aware modality calibration in snap-and-ask retrieval. The code and dataset are available at https://github.com/zrchen03/SnapBench.

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.

Retrieval augmented generation (RAG) has been applied in many scenarios to augment large language models (LLMs) with external documents provided by retrievers. However, a semantic gap exists between LLMs and retrievers due to differences in their training objectives and architectures. This misalignment forces LLMs to passively accept the documents provided by the retrievers, leading to incomprehension in the generation process, where the LLMs are burdened with the task of distinguishing these documents using their inherent knowledge. This paper proposes R²AG, a novel enhanced RAG framework to fill this gap by incorporating Retrieval information into Retrieval Augmented Generation. Specifically, R²AG utilizes the nuanced features from the retrievers and employs a R²-Former to capture retrieval information. Then, a retrieval-aware prompting strategy is designed to integrate retrieval information into LLMs’ generation. Notably, R²AG suits low-source scenarios where LLMs and retrievers are frozen. Extensive experiments across five datasets validate the effectiveness, robustness, and efficiency of R²AG. Our analysis reveals that retrieval information serves as an anchor to aid LLMs in the generation process, thereby filling the semantic gap.