Unlike test-time adaptation (TTA), open-set TTA (OSTTA) aims to robustly adapt to in-distribution (ID) domain shifts while suppressing the detrimental impact of out-of-distribution (OOD) samples encountered at test time. To this end, we propose a novel OSTTA approach named Noise-Immune Self-Purification (NISP). By exploiting the zero-shot priors of pre-trained vision-language models (VLMs), NISP advances from coarse pseudo-labeling to fine-grained, noise-resilient adaptation. Technically, we first introduce a dual-cluster Bayesian Gaussian mixture model to fit VLMs-derived scores, achieving coarse pseudo-ID/OOD separation via posterior-risk thresholding. Subsequently, NISP constructs a noise-immune fine-grained adaptation where the adapter enforces consensus-discrepancy constraints to refine coarse pseudo-labels and suppress noise propagation. We then devise the Jaccard Consistency Score for OOD discrimination. Overall the coarse-to-fine pipeline enables rigorous self-purification and robust online adaptation. Theoretically we show that consensus-discrepancy losses mitigate the deleterious effects of noise. Empirically, NISP achieves state-of-the-art results across multiple OSTTA benchmarks, validating its efficacy. The code is available at https://github.com/njustkmg/IJCAI26-NISP.
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Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and attribute signals, existing methods still struggle to flexibly capture high-order local structures and often overlook global semantics in complex graphs. These limitations lead to suboptimal node representations, especially in real-world graphs with fragmented structures and ambiguous cluster boundaries. To address these limitations, a contrastive graph clustering framework is proposed to jointly integrate multi-scale local structures with global semantics via attention mechanisms. At the local level, GNN-based topological signals extracted from multiple propagation depths are adaptively fused through attention-based weighting to capture multi-scale neighborhood features. At the global level, semantic prototypes derived from dynamically evolving cluster centers are adaptively aggregated through attention to guide node representations and enhance inter-cluster separability. The model is trained under a dual-view contrastive learning paradigm with a hybrid objective that combines instance-level and structure-aware losses to improve representation robustness and discrimination. Experiments on eight real-world graph datasets demonstrate that our method achieves competitive clustering performance. Code is available at https://github.com/vege12138/w2.
Sleep is a vital physiological process, and its quality directly impacts human health. Although deep learning-based automated sleep staging methods have achieved significant progress, the studies still face limitations in fully mining spatio-temporal graph dependencies. Existing methods face challenges in capturing implicit spatial topology among multi-channel signals and efficiently fusing multi-level features in long-sequence modeling. To address these challenges, this paper proposes a novel hybrid deep learning framework for sleep staging that progressively and explicitly integrates spatial topology modeling, cross-level feature fusion, and global temporal aggregation. By decoupling shallow and deep spatial features and hierarchically organizing feature interactions, the proposed framework effectively captures implicit spatial dependencies while maintaining computational efficiency in long-sequence modeling. Extensive experimental results on multiple sleep staging datasets (83.1% accuracy, 80.1% F1-score on ISRUC-S1, 84.0% accuracy, 83.8% F1-score on ISRUC-S3, and 85.9% accuracy, 83.7% F1-score on Sleep-EDF-153) demonstrate that the proposed method achieves competitive accuracy with significantly reduced computational latency, highlighting its strong potential for mobile and real-time sleep monitoring applications.
Multi-view anchor graph clustering has emerged as a high-efficiency paradigm of multi-view learning. However, how to design an effective anchor alignment mechanism within this framework remains an open challenge, which is formally termed the Anchor-Unaligned Problem (AUP). Current research fails to adequately address two pivotal aspects of this challenge: first, the construction of anchor graphs and the alignment of anchors are two independent stages, overlooking their potential synergistic reinforcement; second, selecting anchors from different views as an alignment baseline often renders the clustering performance highly sensitive to the baseline choice. To address these issues, we propose a unified framework termed One-Step Self-Aligned Anchor Learning for Multi-View Clustering (OSAA-MVC). Departing from conventional two-stage strategies, we integrate anchor alignment and anchor graph construction into a joint optimization process, thereby enabling their mutual reinforcement to improve clustering performance. To avoid the baseline selection issue, we introduce a novel mixed anchor strategy, which effectively bypasses the necessity of manual baseline selection while simultaneously capturing both view-specific and cross-view information. This strategy can stabilize clustering performance at a consistently high level. Extensive experiments demonstrate the superior efficiency and effectiveness of our proposed method compared to state-of-the-art competitors. The code is available at https://github.com/Jiamiao2024/OSAA-MVC.
Identifying cancer driver genes (CDGs) requires integrating heterogeneous biological networks, each encoding distinct mechanistic insights into tumorigenesis. Existing multi-network methods typically fuse views at the feature level or enforce uniform representations, which obscures network-specific signals and limits interpretability. To address this, we propose LogicFusion, a novel differentiable framework that treats each biological network as an independent probabilistic evidence source and explicitly learns how to combine them using logical reasoning. Specifically, LogicFusion maps the output of each view into probabilistic space as independent evidence, and learns a set of logical rules in a differentiable manner. These rules adaptively select and fuse these view evidences through logical conjunction (AND) and disjunction (OR). The final prediction is then obtained via gene-specific weighting. LogicFusion enables both high-accuracy CDG identification and interpretable attribution of predictions to specific networks. Experiments on pan-cancer and cancer-specific datasets show that LogicFusion consistently outperforms state-of-the-art methods, while providing transparency in multi-view genomic reasoning through learned logical rules.
Gradient inversion attacks (GIAs) have shown that shared gradients in federated learning leak private training data. However, current textual GIAs fail in large batch size, as simply increasing batch size can serve as a stable defense against such attacks. In this paper, we propose CDO-GIA, a novel textual gradient inversion attack via continuous-discrete optimization that extends the maximum effective batch size from 4 to 8 for reconstructing text from gradient. In continuous optimization phase, CDO-GIA incorporates a dynamic weight decay mechanism to alleviate embedding homogenization caused by embedding regularization at large batch sizes, thereby enhancing unigrams reconstruction accuracy. Moreover, our method introduces a tabu beam search mechanism guided by the language model prior during discrete optimization. This design facilitates fine-grained exploration of high-dimensional token spaces via precise token order adjustment, thus reconstructing semantically coherent sequences. By alternating optimization between continuous and discrete phases, CDO-GIA effectively doubling practical attack limit of prior textual GIAs. Extensive experiments are conducted on three binary text classification datasets. The experimental results indicate that CDO-GIA surpasses all baseline methods. With a batch size of 8, it achieves performance gains of 25.01%, 155.64%, and 22.72% over the baselines in terms of Rouge-1, Rouge-2 and Rouge-L scores.
Diffusion models show significant potential for low-light image enhancement. However, this task requires satisfying human perceptual preferences and content fidelity transcending simple brightness and color improvement. Existing methods rely on heuristic physical priors or incorporate perceptual metrics directly into timestep-wise training objectives. Such proxy constraints fail to provide reliable trajectory-level guidance for perceptual alignment. Furthermore, they often lead to artifacts or unnatural visual effects in this ill-posed inverse problem. To address these issues, we propose an unsupervised low-light enhancement framework based on Group Relative Policy Optimization (GRPO), which utilizes perceptual preferences to directly optimize the diffusion policy. We introduce a Sliding Window Hybrid ODE-SDE Sampling strategy that confines stochasticity to dynamic sub-intervals, thereby achieving efficient coarse-to-fine exploration and precise advantage attribution. To meet strict fidelity constraints, we construct a Synergistic Perception-Fidelity Reward and introduce an Independent Advantage Estimation strategy to mitigate signal collapse and gradient suppression in multi-objective optimization. Furthermore, we design a Temporal-Aware Dynamic Weighting Mechanism that adaptively adjusts perceptual weights across denoising stages to balance visual enhancement with structural preservation. Extensive experiments on multiple real-world benchmarks demonstrate that our method effectively improves the perceptual performance of existing models, yielding results that better align with human aesthetics.
Achieving fairness in machine learning models while maintaining high accuracy is an important but complex task, especially when handling multiple sensitive attributes. Traditional fairness methods often struggle to eliminate bias within subgroups divided by sensitive attributes. Several key challenges have been identified in this context: (1) Multiple sensitive attribute scalability challenge, where methods fail to ensure fairness as the number of sensitive attributes increases, despite scenarios with multiple sensitive attributes being prevalent in real-world applications; (2) Multiple objective optimization conflict challenge, where simultaneously optimizing for accuracy, fairness, and other relevant objectives leads to conflicting gradient updates, causing suboptimal performance. To address these challenges, we propose BAMFair, a Barycenter Aligned Mediation framework for fairness across multiple sensitive attributes. It comprises two core modules: a Global Barycentric Alignment (GBA) module and a Nash Fairness Mediator (NFM) module. Specifically, GBA innovatively introduces a global fair barycenter and minimizes the distances from subgroups divided by sensitive attributes to it, providing a scalable and efficient solution for fairness optimization across multiple sensitive attributes. Subsequently, NFM negotiates an agreement among inconsistent gradient updates between different objectives. Extensive experiments on four real-world datasets validate that BAMFair outperforms state-of-the-art methods in scenarios with multiple sensitive attributes.
Diffusion-based image compression has exhibited robust performance. However, most existing methods primarily emphasize spatial domain, causing frequency dependencies to be learned only implicitly. We revisit this problem from a frequency perspective, and observe pronounced heterogeneity between global and local frequency dependencies. Global frequency patterns describe the energy distribution over the entire image, whereas patch level frequency relations govern the coupling of local details. These two forms of dependency differ substantially in both scale and semantic level, yet most methods overlook this distinction. To address this issue, we propose a learning heterogeneous global local frequency dependencies in diffusion-based image compression which uses Fourier and Mamba jointly models both global and local frequency correlations(FMDiff). The core of FMDiff is the dual branch FMBlock. In the frequency branch, features are decomposed into magnitude and phase, then fed into Mamba separately. Its scanning mechanism captures cross frequency coupling within each patch while aggregating long range frequency context along the sequence. Magnitude phase interactive modulation is then used to explicitly restore spectral information corrupted by compression. The spatial branch provides high level semantic constraints and is fused with the frequency branch during diffusion reconstruction. Extensive experiments show that FMDiff consistently improves performance across multiple datasets.
While recent video generative models can synthesize high-fidelity videos, they struggle to portray plausible physical interactions and the resulting state transitions, a critical bottleneck for applications in robotics and VR/AR. To address this, we introduce a framework to generate a scalable synthetic dataset of controllable interactions. Our pipeline leverages a structured taxonomy and state-of-the-art image editing models to create explicit `start' and `end' state images, which serve as visual anchors for the interaction. To generate a seamless video utilizing these anchors, we propose State-Guided Sampling (SGS), a novel sampling technique that mitigates artifacts common in naive conditional generation. Furthermore, we develop and validate a new automated evaluation system that aligns with human judgments to ensure data quality. Experiments show that fine-tuning a base model on our dataset significantly enhances its ability to generate plausible interactions.
Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities such as texts. However, incorporating natural language-based external events to improve non-stationary forecasting remains largely unexplored, as most approaches still rely on a single modality, resulting in limited contextual knowledge and model underperformance. Enabling fine-grained multimodal interactions between temporal and textual data is challenged by two fundamental issues: (1) the gap in modeling interactions among discrete external events and continuous time series in a unified framework; (2) classical uniform diffusion timestep ignores event-induced non-stationary variability, leading to imbalanced denoising difficulty across diffusion stages. In this work, we propose event-aware non-stationary time series forecasting EventTSF, an autoregressive diffusion framework that integrates historical time series and textual events via step-wise diffusion. To mitigate the imbalanced denoising difficulty of uniform timestep sampling, EventTSF uses an event-aware flow-matching timestep conditioned on event semantics. Extensive experiments on 7 synthetic and real-world datasets show that EventTSF outperforms 12 non-stationary time series forecasting baselines, achieving average gains of 41.3% in probabilistic forecasting and 27.5% in deterministic forecasting across all evaluation metrics.
Training a unified language model that adapts between intuitive System 1 and deliberative System 2 remains challenging due to interference between their cognitive modes. Recent studies have thus pursued making System 2 models more efficient. However, these approaches focused on output control, limiting what models produce. We argue that this paradigm is misaligned: output length is merely a symptom of the model's cognitive configuration, not the root cause. In this work, we shift the focus to capability control, which modulates how models think rather than what they produce. To realize this, we leverage existing Instruct and Thinking checkpoints through dynamic parameter interpolation, without additional training. Our pilot study establishes that linear interpolation yields a convex, monotonic Pareto frontier, underpinned by representation continuity and structural connectivity. Building on this, we propose DAMI (DynAmic Model Interpolation), a framework that estimates a query-specific Reasoning Intensity lambda(q) to configure cognitive depth. For training-based estimation, we develop a preference learning method encoding accuracy and efficiency criteria. For zero-shot deployment, we introduce a confidence-based method leveraging inter-model cognitive discrepancy. Experiments on five mathematical reasoning benchmarks demonstrate that DAMI achieves higher accuracy than the Thinking model while remaining efficient, effectively combining the efficiency of System 1 with the reasoning depth of System 2.
Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for building low-power Transformers. However, existing Spiking Transformers largely adhere to a passive reactive paradigm, which struggles to focus on task-relevant information and incurs substantial computational overhead when processing redundant visual data. To overcome this fundamental yet underexplored limitation, we propose SAFformer, a novel Spiking Transformer architecture based on an active predictive filtering paradigm. Inspired by the brain’s predictive coding mechanism, SAFformer actively suppresses predictable signals and focuses on salient visual features. Extensive experiments show that SAFformer establishes new state-of-the-art performance on CIFAR-10/100 and CIFAR10-DVS. Remarkably, on ImageNet-1K, it achieves 80.44% Top-1 accuracy with only 26.58M parameters and an energy consumption of 5.88 mJ, demonstrating an exceptional balance between accuracy and efficiency.
Empowered by large language models (LLMs), multi-agent systems (MAS) have shown significant potential in code generation by simulating collaborative workflows. However, we identify a collaboration degeneration phenomenon, where one agent dominates while others remain disengaged, occurring in 38.4% of non-routine HumanEval tasks. Surprisingly, introducing competition among LLM agents eliminates this degeneration and sparks innovation in generated code. Inspired by this catfish effect, we posit that competition helps prevent collaboration degeneration. We thus propose C3, a Controllable Competitive Collaboration framework featuring two novel mechanisms: Centralized Auction-Based Competition (CAB) for role allocation via bidding and Decentralized Communication-Aware Competition (DCC) for solution refinement through opponent-aware adaptation. Evaluated on 70 complex projects from extended SoftwareDev, C3 reduces collaboration degeneration from 32.4% to 12.8%, while enhancing code innovation and diversity. Human evaluations further confirm C3’s superiority in functionality, maintainability, and robustness over purely collaborative or competitive baselines.
Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models (PRMs) focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of individual dimension failures (such as visual hallucinations) by the dominating factors, without guaranteeing the validity of the reasoning process in general. Therefore, to overcome the limitation, the paper proposes the concept of Multimodal Multi-Dimensional Scalarization Process Reward Modeling (MMS-PRM), a paradigm specifically developed to enforce the worst dimension’s robustness in multimodal reasoning. Specifically, a hierarchical fine-grained reward space is developed to represent the multimodal risks in the reasoning tasks, and a Chebyshev-based Monte Carlo Tree Search (MCTS) algorithm is introduced, in which the primary focus during the path searching is given to the worst-performing dimension. Moreover, a curriculum-based Direct Preference Optimization (DPO) approach is developed to gradually learn the balanced reasoning skills in the policy. The experimental results show that, without the dimension collapse issue, the MMS-PRM approach significantly improves the reliability of the multimodal reasoning performance and reaches competitive results in various challenging tasks. The code is available at https://github.com/leibniz-Man/MMS-PRM
Test-time scaling (TTS) has demonstrated remarkable potential in enhancing the reasoning capabilities of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs). However, its application has primarily been limited to domains such as mathematics and programming, owing to their reasoning-intensive nature and the ease of result verification. Its utility in other knowledge-intensive fields, such as medicine and general scientific research, remains underexplored. To bridge this gap and unlock the potential of TTS in broader domains, we propose Cross-Domain TTS, a novel framework that enables task-tailored scaling. This framework consists of two key components: a conformal prediction-based cold-start strategy and an information-gain-based dynamic reasoning adjustment. The CP-based cold-start strategy guides the model's initialization during test-time scaling based on conformal prediction theory, while the information-gain-based dynamic reasoning adjustment guides the model's reasoning progress through a progress vector according to the information gain of reasoning steps. We conducted experiments using LLMs and LVLMs on cross-domain benchmarks. Our results demonstrate that the proposed framework consistently improves performance across various domain-specific datasets. For instance, in the medical domain, it achieves an improvement of up to 17% in pass@1 accuracy while reducing inference latency and saving up to 30% in token consumption. Code is available at https://github.com/Yan0613/Cross-Domain-TTS.
Graphical User Interface (GUI) Agents, powered by large language and vision-language models, hold promise for enabling end-to-end automation in digital environments. However, their progress is fundamentally constrained by the scarcity of scalable, high-quality trajectory data. Existing data collection strategies either rely on costly and inconsistent manual annotations or on synthetic generation methods that trade off between diversity and meaningful task coverage. To bridge this gap, we present GUI-ReWalk: a reasoning-enhanced, multi-stage framework for synthesizing realistic and diverse GUI trajectories. GUI-ReWalk begins with a stochastic exploration phase that emulates human trial-and-error behaviors, and progressively transitions into a reasoning-guided phase where inferred goals drive coherent and purposeful interactions. Moreover, it supports multi-stride task generation, enabling the construction of long-horizon workflows across multiple applications. By combining randomness for diversity with goal-aware reasoning for structure, GUI-ReWalk produces data that better reflects the intent-aware, adaptive nature of human-computer interaction. We further train Qwen2.5-VL-7B on the GUI-ReWalk dataset and evaluate it across multiple benchmarks, including Screenspot-Pro, OSWorld/OSWorld-G, UI-Vision, AndroidControl, and GUI-Odyssey. Results demonstrate that GUI-ReWalk enables superior coverage of diverse interaction flows, higher trajectory entropy, and more realistic user intent. These findings establish GUI-ReWalk as a scalable and data-efficient framework for advancing GUI agent research and enabling robust real-world automation.
Multimodal sentiment analysis relies on textual, acoustic, and visual signals, yet real-world data often suffer from modality missing and quality imbalance. Existing methods generate features for modality missing from available ones, but differences in expression mechanisms and sentiment dynamics across modalities may cause the generated features to deviate from true distributions and mislead prediction. In addition, unreliable modalities may dominate fusion, resulting in representation shift across modality combinations and unstable sentiment representations. To address these challenges, we propose a two-level reference alignment framework. The framework introduces stable references at the feature representation and sentiment decision levels to improve robustness under modality missing. First-level reference alignment leverages complete-modality samples to constrain representations and align different modality combinations into a shared sentiment space. Second-level reference alignment enforces cross-modal consistency at the decision level by suppressing unreliable modalities through prototype retrieval and voting. As a result, the framework maintains stable and reliable sentiment predictions under diverse missing-modality patterns. Experiments on CMU-MOSI and CMU-MOSEI show consistent improvements across various missing-modality settings. Under full-modality input, the proposed method achieves state-of-the-art performance, with ACC of 86.28% and 85.88%, and F1 of 86.24% and 85.86%.
Spatiotemporal traffic forecasting currently faces dual challenges: capturing long-range periodic dependencies and managing the computational burden of increasingly complex deep neural network architectures. Mainstream models typically contain millions of parameters and struggle to handle long sequence historical data due to memory bottlenecks. To address these challenges, we present MiniST, a minimalist framework that decouples information content from sequence length by exploiting the intrinsic redundancy of traffic data. Instead of modeling dense point-wise dependencies, MiniST projects high-dimensional traffic history into a compact set of Orthogonal Basis Vectors, effectively distilling dominant traffic modes into a disentangled latent space. Furthermore, we introduce a Template-based Update Module (TUM), which models temporal dynamics by anchoring these latent bases to a set of learnable, time-invariant global templates. Extensive experiments on multiple large-scale datasets, including a network with over 50,000 nodes, demonstrate that MiniST achieves state-of-the-art performance in both short-term and long-term traffic forecasting, outperforming 22 benchmark models with improvements of up to 11.58%, while maintaining minimal parameter size and competitive efficiency.The source code is available at MiniST Repository.
Remote sensing change detection (RSCD) aims to identify changed regions in bitemporal images. However, conventional one-step modeling suffers from performance degradation caused by imaging temporal differences (e.g., illumination disturbances, seasonal variations). To address this issue, we formulate the problem as an “adversarial attack and defense” paradigm. Then, instead of traditional adversarial training, we propose a brain-inspired deep network based on the drift diffusion model (DDM) decision-making mechanism for RSCD, which is rooted in cognitive neuroscience. By simulating brain decision processes, the network enhances model robustness significantly. Specifically, we design the multi-stage reasoning unit to simulate DDM’s iterative evidence generation, stochastic representation unit with reparameterization sampling to model stochastic diffusion in decision-making, decision aggregation unit to replicate DDM’s evidence accumulation, and loss-guided unit with multi-loss joint constraints to simulate directional drift. Experimental results verify the model’s superior performance on multiple datasets and their perturbed versions.