Radiology Report Generation (RRG) has benefited from stronger encoder-decoder pipelines and particularly Large Language Model (LLM) report generators, but most methods still perform training through random mini-batches. This practice is poorly matched to chest radiology corpora, where common normal statements and frequent findings dominate the optimization signal, while clinically important pathological attributes appear in the long tail. We argue that random sampling pushes report generators toward a frequent-finding shortcut, where models quickly learn fluent report narratives and high-frequency disease phrases, then receive limited pressure to recover rare attributes that contribute little to aggregate cross-entropy. To address this problem, we propose ParaC, a pathology-aware replay curriculum for long-tail RRG. ParaC ranks training reports with pathology labels and structured medical knowledge, expands the training process from simple reports to complex long-tail cases, and replays prototype anchors to preserve basic cross-modal grounding. Extensive experiments on IU X-Ray and MIMIC-CXR confirm the superiority of ParaC on several existing methods, with further ablations analyzing the large gains brought by our pathology-aware curriculum to cover rare pathological findings.

Building fine-grained cross-modal alignment via pure radiograph-report pairs has been a long-standing challenge in the task of Radiology Report Generation (RRG). An intuitive solution towards this issue lies at introducing extra contexts to assist the report generation process, yet clinical contexts are normally unavailable in most real-world scenarios. In this paper, we present an asymmetric context self-distillation framework for RRG, namely ConDistill. To facilitate radiograph-only report generation, it refines the basic radiograph-report alignment, by progressively injecting additional contexts via multi-stage curriculum. Since these contexts are not available in inference, we develop a self-distillation strategy to transfer the bridge states from the teacher branches with extra contexts to the student one with solely radiograph inputs. For evaluation, we conduct extensive experiments on both radiograph-only and multi-context benchmarks, i.e., MIMIC-CXR and MIMIC-RG4, respectively. Experimental results validate our promising performance and further ablations offer potential insights for the context learning in LLM-based RRG frameworks.

Dominant multimodal emotion recognition paradigms often neglect the intrinsic geometric structure of affect, resulting in representations heavily entangled with non-affective factors. To address this, we propose a Canonical Disentangled Multimodal Generative Framework aimed at recovering the canonical affective manifold from raw data. We explicitly decompose the latent space into a canonical Shared Affective Subspace (z_vad) and a Private Modality Subspace (z_priv). We facilitate this factorization through Supervised Manifold Anchoring and Cross-Modal Manifold Alignment. Experiments demonstrate that our model effectively disentangles affect from private attributes (e.g., identity), achieving superior robustness in zero-shot cross-domain transfer compared to fully supervised baselines, while enabling controllable emotion generation.

In natural language processing (NLP) and computer vision (CV), the successful application of foundation models across diverse tasks has demonstrated their remarkable potential. However, despite the rich structural and textual information embedded in knowledge graphs (KGs), existing research of foundation model for KG has primarily focused on their structural aspects, with most efforts restricted to in-KG tasks (e.g., knowledge graph completion, KGC). This limitation has hindered progress in addressing more challenging out-of-KG tasks. In this paper, we introduce MERRY, a foundation model for general knowledge graph reasoning, and investigate its performance across two task categories: in-KG reasoning tasks (e.g., KGC) and out-of-KG tasks (e.g., KG question answering, KGQA). We not only utilize the structural information, but also the textual information in KGs. Specifically, we propose a multi-perspective Conditional Message Passing (CMP) encoding architecture to bridge the gap between textual and structural modalities, enabling their seamless integration. Additionally, we introduce a dynamic residual fusion module to selectively retain relevant textual information and a flexible edge scoring mechanism to adapt to diverse downstream tasks. Comprehensive evaluations on 28 datasets demonstrate that MERRY outperforms existing baselines in most scenarios, showcasing strong reasoning capabilities within KGs and excellent generalization to out-of-KG tasks such as KGQA.