Large-scale vision–language models (LVLMs) have achieved remarkable progress on various reasoning tasks. However, most studies focus on natural photographic images and pay limited attention to multi-panel visual narratives such as comics. This leaves a clear gap in our understanding of how well LVLMs perform chronological reasoning across comic panels. To address this, we introduce **ChrOMIC**, a new benchmark dataset for **chro**nological reasoning in multi-panel **comic**s. It covers six types of reasoning questions and spans both Western and Japanese comic styles. To ensure high-quality annotations, we customized a human–AI collaborative annotation process tailored to the characteristics of the two comic styles. We further introduce three core tasks: Description Reordering and Panel Reordering, which jointly assess models’ ability to understand chronological order in panel sequences, and Multiple-Choice Question Answering (MCQA), which evaluates narrative-level reasoning. We evaluate a range of open-source and commercial LVLMs on ChrOMIC, and find that even the leading models struggle with panel-based chronological reasoning. Further analysis reveals key limitations, including weak visual action understanding and frequent hallucinations in fine-grained visual interpretation.

Retrieval-Augmented Generation (RAG) systems rely on Optical Character Recognition (OCR) to ingest knowledge from unstructured documents. However, OCR engines often struggle with complex layouts, introducing Structural Noise, such as line insertion and paragraph interleaving, which disrupts the semantic flow of the text. Existing evaluations largely overlook this dimension, operating on the assumption of structurally perfect input. To bridge this gap, we introduce StruNRAG, a dedicated benchmark for evaluating RAG robustness against OCR-induced structural perturbations. We construct a bilingual dataset of 2,132 question-answer pairs derived from complex Chinese and English documents and systematically inject three categories of real-world structural noise: line insertion, paragraph interleaving, and line interleaving. Our evaluation of mainstream retrievers and Large Language Models (LLMs) reveals a nuanced interaction between noise and pipeline stages: while structural distortions consistently degrade retrieval performance, the generation stage exhibits unexpected robustness. Advanced LLMs demonstrate robustness against local noise (e.g., line insertion), but struggle to maintain reasoning capabilities under severe structural disruption that fragments global context. These findings indicate that while LLMs are capable of compensating for minor parsing errors, future RAG optimizations must take into account the effects of structural noise. Our code and datasets are available at [https://github.com/GaoMengnana/StruNRAG](https://github.com/GaoMengnana/StruNRAG).

Multimodal sentiment analysis identifies human emotional tendencies by analyzing text, visual, and auditory modalities. In most studies, the textual modality is usually considered to contain the most emotional information and is regarded as the dominant modality. Existing methods mostly map auxiliary modalities into a semantic space close to the dominant modality, which overly relies on the dominant modality. In this work, we propose a Feature Decomposition-Augmentation (FeaDA) framework, which aims to elevate the role of auxiliary modalities in multimodal data fusion. We first design a projector to decompose auxiliary modalities into partial features, which contain features for emotion judgment, and then utilize these decomposed features to guide the fusion process with KL loss, thereby enhancing the status of auxiliary modality fusion. To verify the effectiveness of our method, we conducted experiments on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets. The experimental results show that our FeaDA framework outperforms mutilmodal sentiment analysis methods of the same type in main metrics. Our code is available at https://github.com/PowerLittleYin/FeaDA-main.