Papers

Scene graph generation, which parses images into structured graph, is a fundamental task for scene understanding. Most existing SGG models are dedicated to generating predicate representations based on appearance, relative position, and contextual cues. However, due to the predicate representation ambiguity arising from spatial co-occurrence, the generated scene graphs are often factually correct, but semantically shallow. To address this problem, we propose inferring predicates by synergistically processing spatial and visual information. Our core insight is that acknowledging the coexistence of geometric and non-geometric predicates, rather than struggling to disentangle them, is better suited for predicate inference than existing single-stream architectures. To this end, we introduce a novel method, Dual-stream Synergistic Network (DS-Net). Specifically, it contains two parallel streams: a space stream to predict geometric predicates from spatial layouts and edge features, and a vision stream to predict non-geometric predicates from fine-grained visual cues and linguistic priors. Based on them, we then design Cross-Stream Fusion module to enhance the corresponding predicate representation by using the mutual information of the two types. Through the collaborative processing of these streams, our DS-Net no longer treats the two predicate types as conflicting signals that need to be disentangled. Instead, it utilizes their synergy to facilitate predicate inference, providing a new perspective on resolving predicate ambiguity. Experiments have demonstrated the effectiveness of our method. Furthermore, our approach exhibits strong versatility and can be efficiently integrated with various existing models to enhance their performance. For instance, the 2.3\% $\sim$ 8.2\% increase in mR@100 on PredCls task demonstrates this capability.

The rapid evolution of artificial intelligence in molecular science necessitates a shift from data-driven predictions to knowledge-guided reasoning. Existing molecular models are predominantly proprietary, lacking general molecular intelligence and generalizability. To address this, we propose a task-adaptive large reasoning model that integrates molecular scientific logic to emulate the thinking of molecular scientists, with capabilities for reasoning and reflection. Our approach incorporates multi-specialist modules to provide versatile molecular expertise and a chain-of-thought (CoT) framework enhanced by reinforcement learning infused with molecular knowledge, enabling structured and reflective reasoning. The model outperforms over 20 state-of-the-art multi-task large language models (LLMs) across 10 molecular tasks on 47 metrics, including property prediction, molecule generation, and reaction prediction.It achieves a 50.3% improvement over the base model while ensuring interpretability. It can bridge data-driven and knowledge-integrated approaches for intelligent molecular design.

The rapid proliferation of misinformation poses serious concerns, necessitating the development of efficient and accurate automated detection methods. Existing multimodal misinformation detection approaches predominantly focus on fusing information from different modalities. However, the diverse nature of multimodal posts on social media means that solely focusing on fusion can introduce noise, particularly in posts with weak inter-modal correlations. To address this challenge and effectively handle diverse misinformation instances, we propose a novel method Learning Varying Fusion Degrees with Hierarchical Aggregation(LyRE). LyRE employs classifiers at different stages of a hierarchical fusion process, enabling the model to learn from representations with varying degrees of cross-modal interaction and adapt to different types of multimodal data. Experimental results on multiple publicly misinformation detection datasets demonstrate that LyRE outperforms other state-of-the-art and highly competitive misinformation detection methods