Radiology report generation from longitudinal medical data is critical for assessing disease progression and automating diagnostic workflows. While recent methods incorporate longitudinal information, they primarily rely on multimodal feature fusion, with limited capacity for explicit disease evolution modeling and temporal reasoning. To address this, we propose MARE, an end-to-end framework that formulates longitudinal radiology report generation as a multimodal analogical reasoning task. Inspired by the Abduction–Mapping–Induction paradigm, MARE models latent relational structures underlying disease evolution by aligning lesion-level visual features across time and mapping them to the textual domain for temporally coherent and clinically meaningful report generation. To mitigate the spatial misalignment caused by patient positioning or imaging variation, we introduce an Adaptive Region Alignment (ARA) module for robust temporal correspondence. Additionally, we design Dual Evolution Consistency (DEC) losses to regularize analogical reasoning by enforcing temporal coherence in both visual and textual evolution paths. Extensive experiments on the Longitudinal-MIMIC dataset demonstrate that MARE significantly outperforms state-of-the-art baselines across both natural language generation and clinical effectiveness metrics, highlighting the value of structured analogical reasoning for disease evolution-aware report generation.

Differing from sentiment transfer, positive reframing seeks to substitute negative perspectives with positive expressions while preserving the original meaning. With the emergence of pre-trained language models (PLMs), it is possible to achieve acceptable results by fine-tuning PLMs. Nevertheless, generating fluent, diverse and task-constrained reframing text remains a significant challenge. To tackle this issue, a multi-strategy optimization framework (MSOF) is proposed in this paper. Starting from the objective of positive reframing, we first design positive sentiment reward and content preservation reward to encourage the model to transform the negative expressions of the original text while ensuring the integrity and consistency of the semantics. Then, different decoding optimization approaches are introduced to improve the quality of text generation. Finally, based on the modeling formula of positive reframing, we propose a multi-dimensional re-ranking method that further selects candidate sentences from three dimensions: strategy consistency, text similarity and fluency. Extensive experiments on two Seq2Seq PLMs, BART and T5, demonstrate our framework achieves significant improvements on unconstrained and controlled positive reframing tasks.

Hate Speech Detection (HSD) plays a crucial role in ensuring respectful online communication and preventing the spread of harmful content. However, existing studies on HSD often focus on the overall intent of the target message while overlooking the fine-grained details in the message. We propose a RatIonale-guided multi-taSk lEarning framework (RISE), which frames HSD as the main task and Human Rationales Tagging (HRT) as the auxiliary task. This enables simultaneous message-level understanding and the identification of expressions that trigger human judgments of hate speech, mimicking human cognitive processing to capture fine-grained information and enhance HSD performance. Furthermore, we extend rationale annotations from binary labels to BIO tagging, capturing the positional roles of tokens within rationales. Additionally, we integrate an emoji semantics interpretation module that interprets emoji meanings, enriching contextual information. Extensive experiments demonstrate that RISE outperforms state-of-the-art models, with each component contributing significantly to improved performance.

The performance of current Vision-Language Tracking (VLT) models is constrained by the limited diversity and quantity of labeled data. Compared to constructing large-scale datasets, data augmentation offers a more cost-saving strategy for VLT by synthesizing new samples from existing data, rather than generating them from scratch. However, conventional techniques like rotation and flipping may disrupt scene composition, causing conflicts between visual layouts and textual annotations. Recent advances in generative models have inspired the use of synthetic videos for data augmentation. Yet, existing approaches fail to address the core concerns of data augmentation in VLT (shown in Fig. 1)-target location accuracy, text-video consistency, and video content coherency. To bridge the gap, we propose Gen4Track, a tuning-free data augmentation framework that leverages the self-correcting mechanism to dynamically generate high-quality video data with annotations. Our approach involves (1) optimizing the attention calculations in a frozen text-to-image diffusion model to synthesize coherent videos that satisfy specific conditions (e.g., spatial location, category, color, and style), and (2) implementing a self-correcting mechanism based on a Large Language Model (LLM) to improve text-video consistency. During video augmentation, we propose content-coherent self-attention and location-enhanced cross-attention mechanisms, ensuring that image-level editings are accurately and coherently propagated throughout the video. Then, with the goal of maximizing text-video consistency, we iteratively refine the augmentation instruction with our designed self-correcting mechanism for a more aligned video. Extensive experiments validate that Gen4Track significantly boosts the performance of SOTA VLT models (achieving improvements of up to 3.2% in SUC and 3.5% in PRE), opening a new chapter of training Vision-Language trackers with synthetic videos rather than manually annotated data.

Multimodal Large Language Models (MLLMs) possess extensive knowledge and strong reasoning capabilities, achieving remarkable performance in knowledge-based visual question answering, significantly surpassing traditional small-scale Vision-Language Models (VLMs). However, the distinct training paradigms of MLLMs and small-scale VLMs result in misaligned feature representation spaces and divergent answer prediction distributions. To bridge this gap, we propose a novel end-to-end large-small model synergy framework, where small VLMs and MLLMs collaborate via synergistic optimization of shared objectives while maintaining their co-evolving complementary specializations. Specifically, multimodal fine-grained heuristics are extracted from well-tuned small VLMs and subsequently projected into the textual space of MLLMs through dedicated visual and textual collaboration modules. This enables cross-modal guidance for both visual and textual inputs. Finally, a dual-objective synergy loss promotes alignment toward shared goals, while a visual discrepancy loss preserves specialization diversity. Extensive experiments demonstrate that our framework achieves state-of-the-art performance on both the OK-VQA and A-OKVQA benchmarks.

Emotion Recognition in Conversation (ERC) has attracted increasing attention due to its wide applications in public opinion analysis, empathetic conversation generation, and so on. However, ERC research suffers from the problems of data imbalance and the presence of similar linguistic expressions for different emotions. These issues can result in limited learning for minority emotions, biased predictions for common emotions, and the misclassification of different emotions with similar linguistic expressions. To alleviate these problems, we propose a Contrast-Enhanced Prompt-Tuning (CEPT) framework for ERC. We transform the ERC task into a Masked Language Modeling (MLM) generation task and generate the emotion for each utterance in the conversation based on the prompt-tuning of the Pre-trained Language Model (PLM), where a novel mixed prompt template and a label mapping strategy are introduced for better context and emotion feature modeling. Moreover, Supervised Contrastive Learning (SCL) is employed to help the PLM mine more information from the labels and learn a more discriminative representation space for utterances with different emotions. We conduct extensive experiments and the results demonstrate that CEPT outperforms the state-of-the-art methods on all three benchmark datasets and excels in recognizing minority emotions.

Considerable performance gains have been achieved for knowledge-based visual question answering due to the visual-language pre-training models with pre-training-then-fine-tuning paradigm. However, because the targets of the pre-training and fine-tuning stages are different, there is an evident barrier that prevents the cross-modal comprehension ability developed in the pre-training stage from fully endowing the fine-tuning task. To break this barrier, in this paper, we propose a novel hybrid prompting model for knowledge-based VQA, which inherits and incorporates the pre-training and fine-tuning tasks with a shared objective. Specifically, based on static declaration prompt, we construct a consistent goal with the fine-tuning via masked language modeling to inherit capabilities of pre-training task, while selecting the top-t relevant knowledge in a dense retrieval manner. Additionally, a dynamic knowledge prompt is learned from retrieved knowledge, which not only alleviates the length constraint on inputs for visual-language pre-trained models but also assists in providing answer features via fine-tuning. Combining and unifying the aims of the two stages could fully exploit the abilities of pre-training and fine-tuning to predict answer. We evaluate the proposed model on the OKVQA dataset, and the result shows that our model outperforms the state-of-the-art methods based on visual-language pre-training models with a noticeable performance gap and even exceeds the large-scale language model of GPT-3, which proves the benefits of the hybrid prompts and the advantages of unifying pre-training to fine-tuning.

Commonsense question answering (QA) requires machines to utilize the QA content and external commonsense knowledge graph (KG) for reasoning when answering questions. Existing work uses two independent modules to model the QA contextual text representation and relationships between QA entities in KG, which prevents information sharing between modules for co-reasoning. In this paper, we propose a novel model, Co-Reasoning Network (CORN), which adopts a bidirectional multi-level connection structure based on Co-Attention Transformer. The structure builds bridges to connect each layer of the text encoder and graph encoder, which can introduce the QA entity relationship from KG to the text encoder and bring contextual text information to the graph encoder, so that these features can be deeply interactively fused to form comprehensive text and graph node representations. Meanwhile, we propose a QA-aware node based KG subgraph construction method. The QA-aware nodes aggregate the question entity nodes and the answer entity nodes, and further guide the expansion and construction process of the subgraph to enhance the connectivity and reduce the introduction of noise. We evaluate our model on QA benchmarks in the CommonsenseQA and OpenBookQA datasets, and CORN achieves state-of-the-art performance.