Document-level Relation Extraction (DocRE) aims to extract relations from documents. Compared with sentence-level relation extraction, it is necessary to extract long-distance dependencies. Existing methods enhance the output of trained DocRE models either by learning logical rules or by extracting rules from annotated data and then injecting them into the model. However, these approaches can result in suboptimal performance due to incorrect rule set constraints. To mitigate this issue, we propose Context-aware differentiable rule learning or CaDRL for short, a novel differentiable rule-based framework that learns the doc-specific logical rule to avoid generating suboptimal constraints. Specifically, we utilize Transformer-based relation attention to encode document and relation information, thereby learning the contextual information of the relation. We employ a sequence-generated differentiable rule decoder to generate relational probabilistic logic rules at each reasoning step. We also introduce a parameter sharing training mechanism in CaDRL to reconcile the DocRE model and the rule learning module. Extensive experimental results on three DocRE datasets demonstrate that CaDRL outperforms existing rule-based frameworks, significantly improving DocRE performance and making predictions more interpretable and logical.

Automatic radiology report generation is pivotal in reducing the workload of radiologists, while simultaneously improving diagnostic accuracy and operational efficiency. Current methods face significant challenges, including the effective alignment of medical visual features with textual features and the mitigation of data bias. In this paper, we propose a method for radiology report generation that utilizes a Cross-modal Enhancement and Alignment Adapter (CmEAA) to connect a vision encoder with a frozen large language model. Specifically, we introduce two novel modules within CmEAA: Cross-modal Feature Enhancement (CFE) and Neural Mutual Information Aligner (NMIA). CFE extracts observation-related contextual features to enhance the visual features of lesions and abnormal regions in radiology images through a cross-modal enhancement transformer. NMIA maximizes neural mutual information between visual and textual representations within a low-dimensional alignment embedding space during training and provides potential global alignment visual representations during inference. Additionally, a weights generator is designed to enable the dynamic adaptation of cross-modal enhanced features and vanilla visual features. Experimental results on two prevailing datasets, namely, IU X-Ray and MIMIC-CXR, demonstrate that the proposed model outperforms previous state-of-the-art methods.

Recent advancements in multimodal reasoning overlook the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning. We meticulously curated a large-scale and diverse multi-task audio dataset with simple annotations. Then, we leverage closed-source models to conduct secondary labeling, QA generation, along with structured COT process. These datasets together form a high-quality reasoning dataset with 1.2 million reasoning-rich samples, which we name CoTA. Following inference scaling principles, we train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning. Experiments show state-of-the-art performance across key benchmarks, including MMAU-mini (+25.42%), AIR-Bench chat/foundation (+14.57%/+10.13%), and MELD (+8.01%). Our findings stress the core of structured CoT training in advancing audio reasoning. The model, dataset, and code are open-sourced at [https://github.com/xzf-thu/Audio-Reasoner](https://github.com/xzf-thu/Audio-Reasoner) or [https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA](https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA).