Few-shot and zero-shot point cloud semantic segmentation aim to accurately segment novel categories using limited or no labeled samples, respectively. However, existing methods face significant challenges including domain shifts between support and query sets and the inability to handle both few-shot and zero-shot scenarios within a unified framework. To address these issues, we propose a biologically-inspired Evolutionary Domain Symbiosis Network EDS-Net for unified few-shot and zero-shot point cloud semantic segmentation. Specifically, inspired by natural symbiotic evolution, we propose a Symbiotic Evolution Module (SEM) that models co-adaptation between support and query features through self-correlation and cross-correlation mechanisms. Second, motivated by genetic crossover mechanisms, we introduce a Vision-Semantic Bridging Module (VSBM) that treats visual prototypes and semantic prototypes as two “parent” individuals, creating fused offspring prototypes through adaptive crossover operations and mutation strategies for zero-shot scenarios. Third, we develop a multi-generational evolutionary optimization framework employing an adaptive gating network to learn optimal fusion weights across different evolutionary stages. Extensive experiments demonstrate that EDS-Net with biological interpretability achieves state-of-the-art performance on both few-shot and zero-shot settings.

Graph Neural Networks (GNNs) have been widely adopted for Protein Representation Learning (PRL), as residue interaction networks can be naturally represented as graphs. Current GNN-based PRL methods typically rely on single-perspective graph construction strategies, which capture partial properties of residue interactions, resulting in incomplete protein representations. To address this limitation, we propose MMPG, a framework that constructs protein graphs from multiple perspectives and adaptively fuses them via Mixture of Experts (MoE) for PRL. MMPG constructs graphs from physical, chemical, and geometric perspectives to characterize different properties of residue interactions. To capture both perspective-specific features and their synergies, we develop an MoE module, which dynamically routes perspectives to specialized experts, where experts learn intrinsic features and cross-perspective interactions. We quantitatively verify that MoE automatically specializes experts in modeling distinct levels of interaction—from individual representations, to pairwise inter-perspective synergies, and ultimately to a global consensus across all perspectives. Through integrating this multi-level information, MMPG produces superior protein representations and achieves advanced performance on four different downstream protein tasks.

Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks.To overcome the inherent limitations of causal attention in representation learning, many existing methods modify the attention mechanism to be bidirectional, potentially undermining LLMs' ability to extract semantic information acquired during pretraining.Meanwhile, leading unidirectional approaches often rely on extra input text to generate contextualized embeddings, inevitably increasing computational costs.In this work, we propose Causal2Vec, a general-purpose embedding model tailored to enhance the performance of decoder-only LLMs without altering their original architectures or introducing significant computational overhead.Specifically, we first employ a lightweight BERT-style model to preencode the input text into a single Contextual token, which is then prepended to the LLM's input sequence, allowing each token to capture contextualized information even without attending to future tokens.Furthermore, to mitigate the recency bias introduced by last-token pooling, we concatenate the last hidden states of Contextual and EOS tokens as the final text embedding.In practice, Causal2Vec achieves a new state-of-the-art performance on the MTEB benchmark among models trained solely on publicly available retrieval datasets.

Considering the importance of capturing both global conversational topics and local speaker dependencies for multimodal emotion recognition in conversations, current approaches first utilize sequence models like Transformer to extract global context information, then apply Graph Neural Networks to model local speaker dependencies for local context information extraction, coupled with Graph Contrastive Learning (GCL) to enhance node representation learning. However, this sequential design introduces potential biases: the extracted global context information inevitably influences subsequent processing, compromising the independence and diversity of the original local features; current graph augmentation methods in GCL cannot consider both global and local context information in conversations to evaluate the node importance, hindering the learning of key information. Inspired by the human brain excels at handling complex tasks by efficiently integrating local and global information processing mechanisms, we propose an aligned global-local context fusion framework for sequence-based design to address these problems. This design includes a dual-attention Transformer and a dual-evaluation method for graph augmentation in GCL. The dual-attention Transformer combines global attention for overall context extraction with sliding-window attention for local context capture, both enhanced by spiking neuron dynamics. The dual-evaluation method in GCL comprises global importance evaluation to identify nodes crucial for overall conversation context, and local importance evaluation to detect nodes significant for local semantics, generating augmented graph views that preserve both global and local information. This approach ensures balanced information processing throughout the pipeline, enhancing biological plausibility and achieving superior emotion recognition.

Graph Contrastive Learning (GCL) improves Graph Neural Network (GNN)-based protein representation learning by enhancing its generalization and robustness. Existing GCL approaches for protein representation learning rely on 2D topology, where graph augmentation is solely based on topological features, ignoring the intrinsic biological properties of proteins. Besides, 3D structure-based protein graph augmentation remains unexplored, despite proteins inherently exhibiting 3D structures. To bridge this gap, we propose novel biology-aware graph augmentation strategies from the perspective of invariance and integrate them into the protein GCL framework. Specifically, we introduce Functional Community Invariance (FCI)-based graph augmentation, which employs spectral constraints to preserve topology-driven community structures while incorporating residue-level chemical similarity as edge weights to guide edge sampling and maintain functional communities. Furthermore, we propose 3D Protein Structure Invariance (3-PSI)-based graph augmentation, leveraging dihedral angle perturbations and secondary structure rotations to retain critical 3D structural information of proteins while diversifying graph views. Extensive experiments on four different protein-related tasks demonstrate the superiority of our proposed GCL protein representation learning framework.

With the booming development of social media, temporal link prediction (TLP), as a core technology, has been receiving increasing attention. However, current methods are based on graph neural networks, which suffer from the over-smoothing issue and easily yield indistinguishable node representations, degrading the prediction accuracy. Besides, they lack the ability to eliminate noisy temporal information and ignore the importance of high-order neighbor information for measuring the link probability between nodes. To solve these issues, we design a cross-view graph contrastive learning (GCL) framework for TLP, called Tacl. We first design two augmented views for GCL by enhancing the temporal and topological information to obtain distinguishable node representations. Then, we learn the evolution rule of temporal networks to help constrain consistency of node representations and eliminate noise. Finally, we incorporate the high-order neighbor information to measure the link probability between nodes. Extensive experiments demonstrate the effectiveness and robustness of Tacl.

Multi-modal personality traits recognition aims to recognize personality traits precisely by utilizing different modality information, which has received increasing attention for its potential applications in human-computer interaction. Current methods almost fail to extract distinguishable features, remove noise, and align features from different modalities, which dramatically affects the accuracy of personality traits recognition. To deal with these issues, we propose an emotion-guided multi-modal fusion and contrastive learning framework for personality traits recognition. Specifically, we first use supervised contrastive learning to extract deeper and more distinguishable features from different modalities. After that, considering the close correlation between emotions and personalities, we use an emotion-guided multi-modal fusion mechanism to guide the feature fusion, which eliminates the noise and aligns the features from different modalities. Finally, we use an auto-fusion structure to enhance the interaction between different modalities to further extract essential features for final personality traits recognition. Extensive experiments on two benchmark datasets indicate that our method achieves state-of-the-art performance and robustness.