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Yu-Xuan Zhang, Zhengchun Zhou, Weisha Liu, Mingxing Zhang

Multi-instance learning (MIL) has become a powerful paradigm for weakly supervised learning tasks, where each sample is a bag of unlabeled instances with only the bag-level label. While graph-based MIL methods enhance bag topological structure modeling, they often suffer from high computation costs and limited representation due to rigid graph construction and insufficient integration of intra-bag semantics. To address these challenges, we propose GDF-MIL, a novel graph-driven MIL framework, which introduces a dual-path feature fusion mechanism to adaptively balance topological structure modeling and semantic feature preservation. First, the adaptive bag mapping module (ABMM) performs soft clustering to extract compact and informative representations. Subsequently, a dynamic graph structure learning (DGSL) component efficiently learns sparse topological structures via weighted connectivity, aggregating them into a comprehensive graph-level representation. Finally, to balance fast graph construction and bag-level knowledge, dual-path feature fusion (DPFF) employs a dual-path gating mechanism to integrate both types of features, which are then passed to the classifier for bag label prediction. Extensive experiments on twenty-four datasets across four domains show that GDF-MIL significantly outperforms eighteen state-of-the-art methods on the majority of datasets.

Yongshan Zhang, Kangyue Zheng, Shuaikang Yan, Xinxin Wang, Zhihua Cai

Multi-view clustering of remote sensing data presents significant challenges, as it integrates diverse data representations to improve Earth observation. Although existing anchor graph-based methods have yielded promising results, they generally exhibit two key limitations: (1) the time-consuming process of directly exploring pixel clustering structures, and (2) insufficient modeling of high-order correlations among different views. To address these issues, we propose an Efficient Tensorized multi-view anchor graph clustering method with Affinity Propagation (ETAP) for remote sensing data. Based on superpixel preprocessing, anchor graphs are learned from view-specific pixels and anchors, while compressed anchor graphs are simultaneously learned from the view-specific anchors. An adaptive weighting scheme is introduced to facilitate the learning of these anchor graphs. To capture high-order correlations, tensor Schatten p-norm regularization is applied to the compressed anchor graphs. A connectivity constraint is introduced to uncover the clustering structures of anchors. Finally, pixel clustering structures are then efficiently revealed from the pseudo-labeled anchors through affinity propagation without requiring additional clustering steps. To solve the proposed formulation, we develop an alternating optimization algorithm. Extensive experiments on three public datasets demonstrate the efficacy and efficiency of the proposed method over state-of-the-art methods.

Yizhe Zhang

We demonstrate for the first time that a medical image segmentation model can achieve near fully supervised performance using only a single annotated image and abundant unlabeled data. We present MedSMILE, a novel framework that synergistically integrates transductive and inductive learning for this extreme one-label semi-supervised setting. Its core novelty lies in an iterative loop where a foundation model both bootstraps and refines pseudo-labels for an inductive segmentation model. This process begins with the foundation model performing transductive inference to generate an initial set of pseudo-labels for the unlabeled data pool. This bootstraps an iterative self-training process where the segmentation model is trained and used to generate progressively better labels, with an inter-round refinement step that re-leverages the foundation model to correct errors in uncertain predictions. Experiments on seven datasets across four modalities show MedSMILE recovers 90%–95% of the fully supervised Dice score while decisively outperforming existing semi-supervised techniques that require substantially more annotation. MedSMILE sets a new standard for label-efficient learning in medical image segmentation.

Yixuan Zhang, Jiabin Luo, Zhenggang Wang, Feng Zhou, Quyu Kong

Fairness concerns are increasingly critical as machine learning models are deployed in high-stakes applications. While existing fairness-aware methods typically intervene at the model level, they often suffer from high computational costs, limited scalability, and poor generalization. To address these challenges, we propose a Bayesian data selection framework that ensures fairness by aligning group-specific posterior distributions of model parameters and sample weights with a shared central distribution. Our framework supports flexible alignment via various distributional discrepancy measures, including Wasserstein distance, maximum mean discrepancy, and f-divergence, allowing geometry-aware control without imposing explicit fairness constraints. This data-centric approach mitigates group-specific biases in training data and improves fairness in downstream tasks, with theoretical guarantees. Experiments on benchmark datasets show that our method consistently outperforms existing data selection and model-based fairness methods in both fairness and accuracy.

Ying Zhang, Xingyue Guo, Yu Zhao, Xuhui Sui, Baohang Zhou, Xinying Qian, Xiaojie Yuan

Continual instruction tuning aims to incrementally adapt large language models to new tasks without forgetting previously acquired knowledge. Existing approaches often struggle to balance plasticity and stability. Replay-based methods retrain on historical data, which raises privacy concerns. Architecture-based methods allocate task-specific components, resulting in significant parameter growth. To address this, we consider a structure-sharing strategy that enables parameter reuse across similar tasks and expands only when necessary, avoiding any data replay. Specifically, we introduce Grow-on-Demand (GoD-MoE), a parameter-efficient framework that is based on sparse and adaptive expert module expansion for continual instruction tuning. GoD-MoE inserts multiple LoRA-based experts into attention layers and dynamically activates a small subset of experts for each task. To avoid redundant parameter growth, we develop an Expert Demand Detector that determines whether new experts are added, facilitating adaptive structural sharing and minimizing parameter overhead. We conduct comprehensive experiments on the TRACE benchmark, demonstrating that GoD-MoE achieves state-of-the-art performance. Furthermore, it effectively mitigates catastrophic forgetting and even outperforms several advanced replay-based baselines.

Yibo Zhang, Dengpeng Xing

Goal-conditioned hierarchical reinforcement learning has demonstrated effectiveness in addressing complicated decision-making tasks by providing ''temporal extraction'', which decomposes tasks into smaller and more manageable ''subgoals''. This enables agents to plan over a longer time scale. However, achieving optimal exploration and exploitation still remains a challenge, especially for long-horizon or sparse-reward scenarios. In this paper, we introduce Active exploraion and hierarchical Self-Imitation (ASI), an effective scheme to enhance exploration and exploitation based on subgoal representation learning. The key point of ASI is to utilize temporal adjacency information in the representation space. We construct and dynamically update an adjacency graph that captures the relationships between subgoals. Based on the adjacency information provided by the graph, we design two mechanisms: active ``frontier-reaching'' exploration that faster expands the explored area by targeting boundary regions, and hierarchical self-imitation learning that leverages historical experience to facilitate both frontier reaching and policy training. Experimental results show that our method accelerates exploration and outperforms existing baselines in challenging long-horizon continuous control tasks.

Yeqin Zhang, Yizheng Zhao, Chen Hu, Binxing Jiao, Daxin Jiang, Ruihang Miao, Cam-Tu Nguyen

Text representation plays a critical role in tasks like clustering, retrieval, and other downstream applications. With the emergence of large language models (LLMs), there is increasing interest in harnessing their capabilities for this purpose. However, most of the LLMs are inherently causal and optimized for next-token prediction, making them suboptimal for producing holistic representations. To address this, recent studies introduced pretext tasks to adapt LLMs for text representation. Most of these tasks, however, rely on token-level prediction objectives, such as the masked next-token prediction (MNTP) used in LLM2Vec. In this work, we explore the untapped potential of context compression as a pretext task for unsupervised adaptation of LLMs. During compression pre-training, the model learns to generate compact memory tokens, which substitute the whole context for downstream sequence prediction. Experiments demonstrate that a well-designed compression objective can significantly enhance LLM-based text representations, outperforming models trained with token-level pretext tasks. Further improvements through contrastive learning produce a strong representation model (LLM2Comp) that outperforms contemporary LLM-based text encoders on a wide range of tasks while being more sample-efficient, requiring significantly less training data.

Xuan Zhang, Zhijian Zhou, Weidi Xu, Yanting Miao, Chao Qu, Yuan Qi

Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network’s output distribution to move closer to the symbolic constraints. While diffusion models have shown remarkable generative capability across various domains, we employ the powerful architecture to perform neuro-symbolic learning and solve logical puzzles. Our diffusion-based pipeline adopts a two-stage training strategy: the first stage focuses on cultivating basic reasoning abilities, while the second emphasizes systematic learning of logical constraints. To impose hard constraints on neural outputs in the second stage, we formulate the diffusion reasoner as a Markov decision process and innovatively fine-tune it with an improved proximal policy optimization algorithm. We utilize a rule-based reward signal derived from the logical consistency of neural outputs and adopt a flexible strategy to optimize the diffusion reasoner's policy. We evaluate our methodology on some classical symbolic reasoning benchmarks, including Sudoku, Maze, pathfinding and preference learning. Experimental results demonstrate that our approach achieves outstanding accuracy and logical consistency among neural networks.

Xiaohan Zhang, Chao Zhang, Deng Xu, Hong YU, Chunlin Chen, Huaxiong Li

Image clustering is a fundamental task in unsupervised visual learning. While recent self-supervised methods have explored various pretext tasks to generate supervision signals for clustering, they typically depend exclusively on raw images, resulting in insufficient supervision signals that are inherently constrained by limited visual semantics. In this paper, we propose a novel Semantic-Augmented image Clustering (SAC) method, which transcends the inherent limitations of purely visual representations through the integration of external knowledge. Specifically, SAC utilizes Vision-Language pre-trained Models (VLMs) to flexibly generate textual descriptions for each image, providing external semantic cues to supplement the visual information. By integrating both visual and textual information, SAC achieves image clustering through a multi-modal learning framework. To mitigate the negative impact of inaccurate textual information, SAC designs an uncertainty-driven adaptive weighting mechanism that explores both intra-modal and inter-modal neighborhood structures, and incorporates the adaptive weights into intra-modal and inter-modal contrastive learning, which improves the robustness against noisy image-text correspondences. Experiments on several popular datasets demonstrate the superiority of SAC compared to state-of-the-art methods.

Wenhao Zhang, Chun Zhang, Wei Bai, Ning Zhang, Changxia Gao, Yuxin Jia, Chenhao Shi, Shaoxiong Pang

Multivariate time series forecasting underpins applications in finance, meteorology, and industrial operations. Yet two persistent hurdles remain: (i) models typically choose between Channel–Independent (CI) and Channel–Mixed (CM) formulations—each with distinct strengths—leading to large performance variance across datasets; and (ii) short-term dynamics and long-term trends are hard to model jointly, making it difficult to capture both transient bursts and periodic patterns. We propose FusionTimePatch (FTP), a purely MLP-driven, lightweight framework composed of three modules: (1) Dual-View Global–Local Fusion (Dual-GLF), which runs CI and CM views in parallel and employs multi-scale patch recursion to adaptively adjust the look-back window, thereby coupling global tendencies with local details; (2) Channel Enhancement (CE), which adaptively identifies and amplifies salient channel signals and diffuses them to others, improving sensitivity to abrupt events and latent drivers; and (3) a Linear Fusion layer, which unifies Dual-GLF and CE outputs to strengthen cross-view interactions and enhance robustness. Extensive experiments on multiple public benchmarks show FTP consistently surpasses state-of-the-art counterparts in both accuracy and efficiency, offering a scalable new paradigm for multichannel forecasting. Code and datasets are publicly available at https://github.com/Zhveh7/FTP.

Wenchuan Zhang, Penghao Zhang, Jingru Guo, Tao Cheng, Jie Chen, Shuwan Zhang, Zhang Zhang, Yuhao Yi, Hong Bu

Recent advances in vision-language models (VLMs) have enabled broad progress in the general medical field. However, pathology still remains a more challenging sub-domain, with current pathology-specific VLMs exhibiting limitations in both diagnostic accuracy and reasoning plausibility. Such shortcomings are largely attributable to the nature of current pathology datasets, which are primarily composed of image–description pairs that lack the depth and structured diagnostic paradigms employed by real-world pathologists. In this study, we leverage pathology textbooks and real-world pathology experts to construct high-quality, reasoning-oriented datasets. Building on this, we introduce Patho-R1, a multimodal RL-based pathology Reasoner, trained through a three-stage pipeline: (1) continued pretraining on 3.5 million image-text pairs for knowledge infusion; (2) supervised fine-tuning on 500k high-quality Chain-of-Thought samples for reasoning incentivizing; (3) reinforcement learning using Group Relative Policy Optimization and Decoupled Clip and Dynamic sAmpling Policy Optimization strategies for multimodal reasoning quality refinement. To further assess the alignment quality of our dataset, we propose Patho-CLIP, trained on the same figure-caption corpus used for continued pretraining. Comprehensive experimental results demonstrate that both Patho-CLIP and Patho-R1 achieve robust performance across a wide range of pathology-related tasks, including zero-shot classification, cross-modal retrieval, Visual Question Answering, and Multiple Choice Question.

Weihong Zhang, Liang Bai, Hangyuan Du, Xian Yang

Graph Neural Networks (GNNs) perform well on in-distribution data but often fail under out-of-distribution (OOD) shifts due to reliance on spurious patterns. To address this, we propose CauVQ, a causal vector quantization framework that improves OOD generalization by identifying and leveraging invariant substructures that are causally predictive. To construct stable and symbolic graph representations, CauVQ decomposes each input into local substructures and maps them to a discrete codebook of prototypical motifs. This enables consistent and interpretable encoding across diverse graph domains. To isolate the causal substructures, we maximize their mutual information with graph labels and refine their representations using a learnable interaction matrix and a causal attention mechanism. Furthermore, we introduce a counterfactual regularization strategy to enforce prediction stability under substructure perturbations, encouraging the model to focus on truly causal patterns rather than superficial shortcuts. Extensive experiments across standard and OOD benchmarks demonstrate that CauVQ consistently outperforms state-of-the-art baselines in robustness and interpretability. Our framework offers a promising step toward reliable, explainable, and distribution-aware graph learning.

Tongcheng Zhang, Zhanpeng Zhou, Mingze Wang, Andi Han, Wei Huang, Taiji Suzuki, Junchi Yan

One crucial factor behind the success of deep learning lies in the implicit bias induced by noise inherent in gradient-based training algorithms. Motivated by empirical observations that training with noisy labels improves model generalization, we delve into the underlying mechanisms behind stochastic gradient descent (SGD) with label noise. Focusing on a two-layer over-parameterized linear network, we analyze the learning dynamics of label noise SGD, unveiling a two-phase learning behavior. In Phase I, the magnitudes of model weights progressively diminish, and the model escapes the lazy regime; enters the rich regime. In Phase II, the alignment between model weights and the ground-truth interpolator increases, and the model eventually converges. Our analysis highlights the critical role of label noise in driving the transition from the lazy to the rich regime and minimally explains its empirical success. Furthermore, we extend these insights to Sharpness-Aware Minimization (SAM), showing that the principles governing label noise SGD also apply to broader optimization algorithms. Extensive experiments, conducted under both synthetic and real-world setups, strongly support our theory.

Tao Zhang, Ziqi Zhang, Zongyang Ma, Yuxin Chen, Bing Li, Chunfeng Yuan, Guangting Wang, Fengyun Rao, Ying Shan, Weiming Hu

The ability to perform multi-modal multi-hop reasoning by iteratively integrating information across various modalities and external knowledge is critical for addressing complex real-world challenges. However, existing Multi-modal Large Language Models (MLLMs) are predominantly limited to single-step reasoning, as existing benchmarks lack the complexity needed to evaluate and drive multi-hop abilities. To bridge this gap, we introduce MMhops, a novel, large-scale benchmark designed to systematically evaluate and foster multi-modal multi-hop reasoning. MMhops dataset comprises two challenging task formats, Bridging and Comparison, which necessitate that models dynamically construct complex reasoning chains by integrating external knowledge. To tackle the challenges posed by MMhops, we propose MMhops-R1, a novel multi-modal Retrieval-Augmented Generation (mRAG) framework for dynamic reasoning. Our framework utilizes reinforcement learning to optimize the model for autonomously planning reasoning paths, formulating targeted queries, and synthesizing multi-level information. Comprehensive experiments demonstrate that MMhops-R1 significantly outperforms strong baselines on MMhops, highlighting that dynamic planning and multi-modal knowledge integration are crucial for complex reasoning. Moreover, MMhops-R1 demonstrates strong generalization to tasks requiring fixed-hop reasoning, underscoring the robustness of our dynamic planning approach.

Shuang Zhang, Yue Wu, Lei Shi, Huilong Jin, Feifei Kou, Pengfei Zhang, Mingying Xu, Pengtao Lv

Cross-modal hashing has emerged as a pivotal solution for efficient retrieval across diverse modalities, such as images and texts, by mapping them into compact binary hash spaces. However, in real-world scenarios, the modalities data is often missing or misaligned. Existing methods are most rely on fully paired training data and ignore missing or misaligned modalities data, resulting in the semantic inconsistencies. To address these challenges, we propose an Adaptive Graph Attention-Based Discrete Hashing (AGADH) method, which consists of three parts. First, to solve the problem of missing modalities, AGADH employs a masked completion strategy to reconstruct missing modalities. Second, to mitigate semantic misalignment, AGADH leverages a Graph Attention Network (GAT) encoder-decoder architecture with alignment module to construct features from different modalities. Additionally, to enhance the fusion performance, an adaptive fusion module dynamically adjusting the contributions of image and text modalities with learnable weighting coefficients is proposed. Extensive experiments on three benchmark datasets, MS-COCO, NUS-WIDE, and MIRFlickr-25K, demonstrating that AGADH outperforms state-of-the-art methods in both fully paired and incompletely paired scenarios, showing its robustness and effectiveness in cross-modal retrieval tasks.

Shan Zhang, Liangrui Ren, Qiaoyu Tan, Carlotta Domeniconi, Wei Du, Jun Wang, Guoxian Yu

Multiple clustering aims to uncover diverse latent structures within the data, enabling a more comprehensive understanding of complex datasets. However, existing approaches either heavily rely on user-supplied keywords or disregard user-interested clustering types, limiting the ability to discover the full range of explainable clusterings of interests, particularly in high-dimensional settings. Furthermore, existing methods insufficiently leverage the rich textual semantics and fall short in fully integrating multi-modal information. To address these challenges, we propose MLLM enriched Multiple Clustering (MLLMMC), a novel framework that leverages multi-modal large language model (MLLM) to explore explainable non-redundant clustering. Specifically, MLLMMC first employs MLLM to generate sample descriptions, which serve as input for LLM to perform prompt-driven reasoning and infer latent clustering types, and then merges them with user-interested types to obtain diverse and explainable clustering types. For each selected type, MLLMMC utilizes MLLM to generate sample-level textual descriptions and aligns them with corresponding visual features through a cross-attention fusion module, which produces a semantically aligned and enriched representation for the target clustering type. Extensive experiments on six benchmark datasets from diverse domains demonstrate that MLLMMC achieves diverse, explainable, and high-quality clustering outcomes, outperforming state-of-the-art multiple clustering methods with a large margin.

Rongchao Zhang, Yiwei Lou, Yu Huang, Yi Xin, Yongzhi Cao, Hanpin Wang

Molecular assembly (MA) has long been a fundamental task in chemistry and biology, with the potential to create new materials and enable novel functions beyond the molecular scale. However, its vast conformational search space poses substantial challenges, and current generative models remain limited in capturing molecular flexibility and preventing non-physical poses. In this paper, we propose AssemUDB, a diffusion bridge–based framework that learns transport mappings between two distinct flexible domains for molecular assembly generation. We reformulate the marginal matching constraint of diffusion bridges as a coupling distribution governed by unbalanced transport rather than imposing strict conservation. Subsequently, we employ a progressive process from structural relaxation in Euclidean space to assembly on the SE(3) manifold. This relaxation of marginal conservation grants the generative model greater flexibility and leads to more physically plausible atom placements. Comprehensive experiments demonstrate the superior performance of AssemUDB. Notably, we find that the method demonstrates performance comparable to, or even better than, mature tools such as PackMol for packing tasks.

Peng Zhang, Wanggui He, Mushui Liu, Wenyi Xiao, Siyu Zou, Yuan Li, Xingjian Wang, Guanghao Zhang, Yanpeng Liu, Weilong Dai 等

Recent unified models have demonstrated that the reasoning capacity of Multimodal Large Language Models (MLLMs) can be leveraged to facilitate diffusion-based image generation with impressive flexibility and performance. However, approaches that rely heavily on MLLMs for high-level semantic encoding often struggle with fine-grained visual tasks like image editing and virtual try-on. To address this gap, we propose FUSE, a unified framework excelling at both high-level vision–language understanding and fine-grained generation. First, we introduce a Semantic-to-Detail Connector that pre-aligns fine-grained visual features with the MLLM's semantic space. This design counteracts the low-level information loss inherent in MLLM encodings, creating a unified representation that steers the diffusion process with both global semantics and rich local details. Second, to further enhance semantic awareness and detail preservation, we introduce Adaptive-GRPO, a post-training objective that dynamically balances semantic coherence against pixel-level fidelity. The integration of these two innovations allows FUSE to generate images that are both semantically faithful and visually fine-grained. Comprehensive experiments on text-to-image and instruction-guided editing benchmarks show that FUSE significantly outperforms existing unified baselines, achieving 0.89 on Geneval, 0.65 on WISE, and 3.88 on ImageEdit.

Peining Zhang, Jinbo Bi, Minghu Song

Diffusion models show promise for 3D molecular generation, but face a fundamental trade-off between sampling efficiency and conformational accuracy. While flow-based models are fast, they often produce geometrically inaccurate structures, as they have difficulty capturing the multimodal distributions of molecular conformations. In contrast, denoising diffusion models are more accurate but suffer from slow sampling, a limitation attributed to sub-optimal integration between diffusion dynamics and SE(3)‑equivariant architectures. To address this, we propose VEDA, a unified SE(3)-equivariant framework that combines variance-exploding diffusion with annealing to efficiently generate conformationally accurate 3D molecular structures. Specifically, our key technical contributions include: (1) a VE schedule that enables noise injection functionally analogous to simulated annealing, improving 3D accuracy and reducing relaxation energy; (2) a novel preconditioning scheme that reconciles the coordinate-predicting nature of SE(3)-equivariant networks with a residual-based diffusion objective, and (3) a new arcsin-based scheduler that concentrates sampling in critical intervals of the logarithmic signal-to-noise ratio. On the QM9 and GEOM-DRUGS datasets, VEDA matches the sampling efficiency of flow-based models, achieving state-of-the-art valency stability and validity with only 100 sampling steps. More importantly, VEDA's generated structures are remarkably stable, as measured by their relaxation energy (Delta E_relax) during GFN2-xTB optimization. The median energy change is only 1.72kcal/mol, significantly lower than the 32.3kcal/mol from its architectural baseline, SemlaFlow. Our framework demonstrates that principled integration of VE diffusion with SE(3)-equivariant architectures can achieve both high chemical accuracy and computational efficiency.

Longteng Zhang, Sen Wu, Shuai Hou, Zhengyu Qing, Zhuo Zheng, Danning Ke, Qihong Lin, Qiang Wang, Shaohuai Shi, Xiaowen Chu

Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in resource-constrained environments. Low-rank Adaptation (LoRA) reduces trainable parameters by factorizing weight updates, yet the underlying dense weights still impose high storage and computation costs. Magnitude-based pruning can yield sparse models but typically degrades LoRA’s performance when applied naively. In this paper, we introduce SALR (Sparsity-Aware Low-Rank Representation), a novel fine-tuning paradigm that unifies low-rank adaptation with sparse pruning under a rigorous mean-squared-error framework. We prove that statically pruning only the frozen base weights minimizes the pruning error bound, and we recover the discarded residual information via a truncated-SVD low-rank adapter, which provably reduces per-entry MSE by a factor of (1 - r/min(d, k)). To maximize hardware efficiency, we fuse multiple low-rank adapters into a single concatenated GEMM, and we adopt a bitmap-based encoding with a two-stage pipelined decoding + GEMM design to achieve true model compression and speedup. Empirically, SALR attains 50% sparsity on various LLMs while matching the performance of LoRA on GSM8K and MMLU, reduces model size by 2x, and delivers up to a 1.7x inference speedup.