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

With the rapid advancement of Transformer-based Large Language Models (LLMs), generative recommendation has shown great potential in enhancing both the accuracy and semantic understanding of modern recommender systems. Compared to LLMs, the Decision Transformer (DT) is a lightweight generative model applied to sequential recommendation tasks. However, DT faces challenges in trajectory stitching, often producing suboptimal trajectories. Moreover, due to the high dimensionality of user states and the vast state space inherent in recommendation scenarios, DT can incur significant computational costs and struggle to learn effective state representations. To overcome these issues, we propose a novel Temporal Advantage Decision Transformer with Contrastive State Abstraction (TADT-CSA) model. Specifically, we combine the conventional Return-To-Go (RTG) signal with a novel temporal advantage (TA) signal that encourages the model to capture both long-term returns and their sequential trend. Furthermore, we integrate a contrastive state abstraction module into the DT framework to learn more effective and expressive state representations. Within this module, we introduce a TA–conditioned State Vector Quantization (TAC-SVQ) strategy, where the TA score guides the state codebooks to incorporate contextual token information. Additionally, a reward prediction network and a contrastive transition prediction (CTP) network are employed to ensure that the state codebook preserves both the reward information of the current state and the transition information between adjacent states. Empirical results on both public datasets and an online recommendation system demonstrate the effectiveness of the TADT-CSA model and its superiority over baseline methods.

Recommendation systems (RS) help users find interested content and connect authors with their target audience. Most research in RS tends to focus either on predicting users’ immediate feedback (like click-through rate) accurately or improving users’ long-term engagement. However, they ignore the influence for authors and the lifelong interaction value (LIV) of user-author pairs, which is particularly crucial for improving the prosperity of social community on different platforms. Currently, reinforcement learning (RL) can optimize long-term benefits and has been widely applied in RS. In this paper, we introduce RL to Reinforce Lifelong Interaction Value of User-Author pairs (RLIV-UA) based on each interaction of UA pairs. To address the long intervals between UA interactions and the large scale of the UA space, we propose a novel Sparse Cross-Request Interaction Markov Decision Process (SCRI-MDP) and introduce an Adjacent State Approximation (ASA) method to construct RL training samples. Additionally, we introduce Multi-Task Critic Learning (MTCL) to capture the progressive nature of UA interactions (click → follow → gift), where denser interaction signals are leveraged to compensate for the learning of sparse labels. Finally, an auxiliary supervised learning task is designed to enhance the convergence of the RLIV-UA model. In offline experiments and online A/B tests, the RLIV-UA model achieves both higher user satisfaction and higher platform profits than compared methods.

探究外源 2,4-表油菜素内酯（EBR）调控藜麦幼苗耐盐碱胁迫的机理，为提高藜麦耐盐碱性改善藜麦产量提供理论依据。本试验以“陇藜 1 号”为试验材料，研究盐，碱和混合盐碱胁迫下外源 EBR 对藜麦幼苗生长、叶绿素、渗透调节、抗氧化酶、及BR 合成及信号转导基因的影响。结果表明，盐碱处理下藜麦幼苗叶片萎蔫发黄，株高、鲜重、叶绿素（Chl）含量显著降低，丙二醛（MDA）含量、相对电导率（RC）、脯氨酸（Pro）、可溶性糖（SS）含量显著上升。胁迫下喷施 EBR 后叶片萎蔫卷缩有所缓解，株高和鲜重分别平均增加了 10%和 29%。其中碱及盐碱处理下缓解效果较好，显著增加了 Chl、Pro、SS 含量和 SOD、POD。CAT 活性，降低了 MDA 及 EC 含量；BR 信号转导基因 cqBAK1 及 CYP90B1 上调表达。综上，EBR 可通过盐碱胁迫下藜麦幼苗渗透调节、抗氧化系统及 BR 信号转导之间的协调作用，提高藜麦的耐盐碱性。