Open cross-domain federated graph learning facilitates collaborative learning among clients from distinct graph domains while preserving privacy. However, severe structure and feature heterogeneity in open scenarios exacerbates the multiplicative amplification of structural and feature noises within graph neural network encoders, leading to significant negative transfer. Existing methods addressing partial heterogeneity are insufficient. We theoretically reveal this amplification mechanism and propose a novel learning method FedFINFO, which leverages full-informativeness shared knowledge. Specifically, we construct an aligned and general feature space integrating node semantic and structural roles. We proposed a counterfactual multi-view framework to explicitly learn a structure masker for extracting consensus subgraphs. These jointly suppress the mutual amplification of noises at the source. We also propose a history-aware adaptive multi-channel aggregation mechanism to enable dynamic and personalized sharing driven by channel similarity while preventing collaborative oscillation. FedFINFO effectively reconciles common knowledge sharing with personalized knowledge retention, significantly mitigating negative transfer. Extensive experiments under cross-dataset and cross-domain settings demonstrate the superiority of our method.
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Network traffic analysis is crucial for maintaining the security of networks. Yet deploying accurate models on edge nodes remains challenging due to protocol diversity, complex traffic behaviors, and stringent resource constraints. Although recent deep learning models achieve strong performance, their dense architectures incur high inference latency, making them unsuitable for edge deployments. Mixture-of-Experts offers a promising solution through conditional computation, yet existing methods overlook latent inter-task relations and suffer from severe expert load imbalance, leading to expert collapse and suboptimal efficiency. To address these limitations, we propose TAMoE, a novel Task-Aware Mixture-of-Experts framework for multi-task network traffic analysis. TAMoE integrates task semantics into both routing and representation learning through a novel Task-Aware Routing, enabling the router to dynamically select experts conditioned on both traffic features and task information. Besides, to train stably usable and scalable multi-task models, we further design a collaborative multi-task training strategy that encompasses both a dataset mixed construction and a joint training process. Experiments on six public benchmarks show that TAMoE achieves state-of-the-art results. Deployment on an NVIDIA® Jetson AGX Orin edge node demonstrates that TAMoE reduces inference latency by 60.6% compared to dense models and lowers the Gini Coefficient from 0.391 to 0.275 relative to Multi-Router MoE, achieving more balanced expert utilization.
Reconstructing PDE-governed fields from sparse and irregular measurements is challenging due to their ill-posed nature. Deterministic surrogates are trained on dense fields that struggle with limited measurements and uncertainty quantification. Generative models, by learning distributions over spatiotemporal fields, can better handle sparsity and uncertainty. However, existing generative approaches enforce data consistency and PDE constraints simultaneously via sampling-time gradient guidance, resulting in slow and unstable inference. To this end, we propose PerFlow, a Physics-embedded rectified Flow for efficient sparse reconstruction and uncertainty quantification of spatiotemporal dynamics. PerFlow decouples observation conditioning from physics enforcement, performing guidance-free conditioning by feeding observations into rectified-flow dynamics while embedding hard physics via a constraint-preserving projection (e.g., incompressibility or conservation). Theoretically, we establish invariance guarantees to ensure that trajectories remain on the physics-consistent manifold throughout sampling. Experiments on various PDE systems demonstrate competitive reconstruction accuracy with sound physics consistency, while enabling efficient conditional sampling (e.g., 50 steps) and up to 320x faster inference than 2000-step guided diffusion baselines.
Multi-step retrosynthetic planning aims to decompose target molecules into available starting materials by iteratively invoking single-step prediction models within a search algorithm. The success of retrosynthetic planning depends on the joint guidance of single-step reasoning and global search across steps. However, most existing frameworks make step-wise decisions based only on the current molecular state, without explicitly modeling synthesizability signals that reflect long-range reachability. In this work, we propose GuideRetro, a synthesizability-aware framework for multi-step retrosynthetic planning that integrates global synthesizability knowledge into step-wise retrosynthetic prediction. GuideRetro learns transferable knowledge from large-scale reaction networks by modeling the evolution of synthetic complexity along reaction pathways. During planning, a route-aware synthesis state modeling module combines the evolving retrosynthetic route with retrieved global signals to guide reactant generation at each step. Experiments on benchmark datasets show that GuideRetro achieves state-of-the-art performance. The integration of global synthesizability knowledge and route-aware modeling improves planning accuracy and search efficiency under realistic retrosynthetic settings. The code is available at: https://github.com/L-AJ/GuideRetro.
Large Language Models (LLMs) powerful generative capabilities also pose significant risks, underscoring the need for effective detoxification methods to ensure safer deployment. Due to the polysemantic nature of LLM neurons, recent neuron intervention methods inevitably entangle unrelated concepts, compromising generation quality and interpretability. Sparse Autoencoders (SAEs) have opened new horizons for decomposing model activations into monosemantic features, offering interpretability and targeted feature-level steering. Empirical findings reveal that, despite capturing interpretable features, indiscriminate interventions on toxicity-related features expose the fragility of LLMs, achieving toxicity mitigation at the cost of degraded fluency. Building upon this finding, we propose DeLFE, a lightweight controlled detoxification approach that identifies specific toxic features across model layers and performs targeted interventions on them. DeLFE learns toxicity subspaces from label-guided SAE feature subsets to characterize toxic v.s. non-toxic activation patterns. When auto-completing a response token-by-token, DeLFE tracks the toxicity-triggering risks and steers toxic features away from the subspace via a flow-matching feature transformation. We further design three feature-level strategies that adjust intervention timing and strength to reconstruct the target model’s original activations. Extensive experiments demonstrate that our method achieves strong detoxification effectiveness while maintaining high generation quality across models of varying sizes and diverse base LLMs.
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights to combine visual, language, and action representations, preventing the model from adapting to different phases of task execution. To address this limitation, we propose S²-VLA, a framework that introduces a State-Space Guided Adaptive Attention (SSGAA) mechanism. SSGAA maintains a belief state that tracks task progression and generates dynamic gating weights to adaptively fuse information from three complementary sources visual features for spatial perception, task intents for high-level task planning, and temporal action sequences for execution consistency. This adaptive fusion allows the model to shift its focus throughout task execution, aligning with the evolving requirements of different task stages. Despite its compact 2B parameter size, S²-VLA consistently outperforms larger 7B-scale models and achieves state-of-the-art performance on long-horizon manipulation benchmarks, including LIBERO and SimplerEnv. highlighting the importance of adaptive feature fusion for long-horizon robotic manipulation.
Label Distribution Learning (LDL) effectively addresses label ambiguity by modeling the degree to which each label describes an instance. A key challenge in LDL is Label Enhancement (LE): recovering label distributions from logical labels. Existing LE methods typically treat logical labels as supervisory signals and learn a direct mapping from features to label distributions. However, they fail to fully exploit the rich information encoded in logical labels, limiting their performance. We propose CVMG (Cross-View Fusion and Mixed Graph-based Label Enhancement), a novel approach that addresses this limitation through two key innovations. First, we employ a cross-attention mechanism to integrate logical labels and features, leveraging their complementary information to generate enriched feature representations. Second, we construct a mixed dependency graph that captures both instance-level relationships from enhanced features and category-level dependencies from logical labels. Label distributions are then recovered through propagation over this graph. Extensive experiments on 13 real-world datasets demonstrate that CVMG significantly outperforms state-of-the-art methods, validating the effectiveness of our approach.
The Dynamic Flexible Job Shop Scheduling Problem (DFJSP) necessitates a trade-off between instant reaction to stochastic disturbances and global optimization of production goals. Conventional priority rules are insufficiently flexible to handle complex disruptions, whereas learning-based approaches often compromise interpretability or fail to generalize across problem scales. Although Large Language Models (LLMs) offer advanced reasoning capabilities to bridge this gap, their substantial inference latency is incompatible with the millisecond-level decision cycles of industrial control systems. To resolve this conflict, we introduce RACE-Sched, an asynchronous agent-based framework that decouples policy execution from logical reasoning via a dual-stream architecture. The Reactive Stream executes low-latency symbolic heuristics to enable real-time dispatching, while the parallel Deliberative Stream leverages an LLM to synthesize, validate, and evolve these rules. Candidate rules undergo rigorous testing in a sandbox and are deployed via atomic updates, ensuring safety without blocking the control loop. Additionally, a semantic rule repository indexes validated heuristics for retrieval-based initialization which enhances transferability across problem scales. Extensive evaluations on GEN-Bench, MK-Bench, and JMS-Bench demonstrate that RACE-Sched outperforms leading Deep Reinforcement Learning and other LLM-based baselines. This approach harmonizes real-time constraints with long-horizon reasoning to achieve superior solution quality and robust adaptation to dynamic events.
Target speaker extraction (TSE) aims to extract the speech of a target speaker from mixtures containing multiple competing speakers. Conventional TSE systems predominantly rely on speaker cues, such as pre-enrolled speech, to identify and isolate the target speaker. However, in many practical scenarios, clean enrollment utterances are unavailable, limiting the applicability of existing approaches. In this work, we propose DAE-TSE, a keyword-guided TSE framework that specifies the target speaker through distinct keywords they utter. By leveraging keywords (i.e., partial transcriptions) as cues, our approach provides a flexible and practical alternative to enrollment-based TSE. DAE-TSE follows the Detect-Attend-Extract (DAE) paradigm: it first detects the presence of the given keywords, then attends to the corresponding speaker based on the keyword content, and finally extracts the target speech. Experimental results demonstrate that DAE-TSE outperforms standard TSE systems that rely on clean enrollment speech. To the best of our knowledge, this is the first study to utilize partial transcription as a cue for specifying the target speaker in TSE, offering a flexible and practical solution for real-world scenarios. Our code (https://github.com/GnafiY/DAE-TSE) and demo page (https://gnafiy.github.io/DAE-TSE_demo) are now publicly available.
Predicting drug–target affinity (DTA) is central to drug discovery, yet most deep learning models rely on a single static protein structure, neglecting the conformational heterogeneity that underlies many binding mechanisms. We propose MultiGeo, a DTA prediction framework that explicitly leverages multiple protein conformations rather than a single snapshot. For each target, MultiGeo starts from an ensemble of structure predictions and uses confidence scores to adaptively select a small set of high-quality, diverse conformers. A hierarchical structural encoder then extracts multi-scale geometric features from these conformers, which are aggregated by a GRU to obtain an ensemble-level representation. To avoid indiscriminately mixing noisy or redundant views, we introduce a disagreement-aware gating mechanism that adaptively fuses this ensemble representation with the dominant structure only when the additional conformers provide complementary information. Finally, ligand–target interactions are modeled via a block-wise cross-attention module that captures multi-perspective dependencies between ligand and protein features. Extensive experiments on multiple DTA benchmarks demonstrate that MultiGeo consistently outperforms state-of-the-art baselines, showing that explicitly encoding conformational diversity yields more accurate and robust affinity prediction.
Vision Transformers (ViTs) exhibit notable susceptibility to adversarial attacks, presenting a significant challenge for their deployment in security-sensitive applications. Despite their considerable empirical successes, a rigorous theoretical foundation for ViT's adversarial generalization behavior has not been adequately established. To address this limitation, we leverage empirical Rademacher complexity to analyze the mechanism of perturbation accumulation through deep ViTs layers. We establish a high-probability generalization bound for ViTs in classification tasks under adversarial settings. Our theoretical framework elucidates the roles of several factors in mitigating perturbation effects, norm regularization of weight matrices (in both MLP and attention modules) and depth-wise propagation constraints on layer-wise norms. Extensive experiments on benchmark datasets corroborate our theoretical insights, bridging the gap between ViTs architecture design and adversarial robustness.
Large Vision-Language Models (LVLMs) demonstrate powerful generative capabilities yet remain prone to object hallucinations. Most existing methods mitigate this issue through training or decoding strategies, but provide limited exploration of how hallucinations arise from internal representations during generation. In this work, we study hallucination from the perspective of dynamic representation shift during generation and propose Representation Intervention based on Visual Grounding Shift (RIVS). Specifically, we first design an adaptive threshold-based method to identify visual attention drop points within the generation, finding that hallucinated tokens usually occur with decay of visual attention. Based on this observation, we construct a hallucination-related subspace from representation differences around these points on a small calibration set, without constructing contrast samples or extra supervision. During inference, we leverage the resulting hallucination-related subspace to perform an online projection-based intervention on intermediate hidden states to suppress the hallucination-related directions, mitigating hallucinations while preserving language quality. Our RIVS is training-free and computationally efficient. Experiments on four hallucination and two reasoning datasets demonstrate that RIVS consistently reduces hallucinations in both long and short sequence generation tasks.
TaylorMoDe-GS: Taylor-Driven Gaussian Splatting Motion Model for Multi-View Dynamic Scene Deblurring
While 3D Gaussian Splatting (3DGS) has excelled in dynamic scene reconstruction, it struggles with multi-view object motion blur, where view-dependent non-uniform blur violates fundamental multi-view geometric constraints. Existing methods fail to balance complex motion fitting with physical consistency across different views. To address the challenge, we propose TaylorMoDe-GS, the first 3DGS framework tailored for multi-view dynamic object deblurring. Specifically, we shift the modeling paradigm from displacement fitting to velocity driven modeling. This is achieved by analytically deriving instantaneous 3D velocities via Taylor series. To map 3D physical motion onto the 2D image plane, we propose a velocity splatting technique. Building upon this, we introduce a neural Peano remainder network to compensate for high frequency non-linear dynamics, effectively resolving the conflict between physical priors and fitting flexibility. Combined with a learnable physical blur synthesis mechanism, our framework ensures rigorous spatiotemporal and view consistency. Extensive experimental results validate that our method achieves notable advancements in restoring high-fidelity dynamic scenes and physically consistent motion fields, effectively addressing the challenges of multi-view dynamic object deblurring. The code is released at https://github.com/Chiffin-0816/TaylorMoDe-GS.
Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characteristics. Intermediate phases—key microstructural constituents—are pivotal in regulating mechanical and functional properties. However, intermediate phase segmentation in zinc alloy microstructures faces formidable challenges: scarce annotated datasets, low contrast, difficulty detecting small targets, and heterogeneous morphologies. To this end, we construct IPSM-Bench, the largest high-quality dataset for zinc-alloy intermediate phase segmentation. Furthermore, we propose SCoP-SAM, a new Spatial Context Prior-guided SAM method that leverages the gradient structure and grayscale properties of intermediate phases to capture spatial context priors and incorporates them into the entire SAM encoding-decoding process, improving segmentation performance. Based on the proposed IPSM-Bench, we establish a new benchmark for intermediate phase segmentation to systematically evaluate state-of-the-art (SOTA) methods and advance research on zinc alloy microstructure analysis. Extensive experiments on IPSM-Bench and additional public alloy benchmarks demonstrate that our SCoP-SAM not only achieves SOTA performance for zinc-alloy intermediate phase segmentation but also generalizes remarkably well to other alloy scenarios.
Spatiotemporal fusion (STF) bridges the gap between temporal and spatial resolutions in satellite imagery, enabling effective monitoring of Earth's surface dynamics. However, existing methods rely on cloud-free reference images, a constraint that fails in realistic, cloud-prone scenarios. To overcome this, we propose the Cloud-Aware Wavelet Generative Adversarial Network (CLAW-GAN), a novel framework for high-fidelity reconstruction under cloud-contaminated conditions. CLAW-GAN introduces Regression Wavelet Analysis (RWA) to decouple spectral backgrounds from structural details. While a change-aware gated mechanism accounts for land-cover changes, the Frequency-Separated Fusion (FSF) module then independently integrates these components. To ensure visual realism, a multi-scale discriminator operates in the wavelet domain, enforcing consistency across high-frequency subbands to minimize artifacts. Evaluated on the newly introduced Global Cloud-shrouded Agricultural Regions (GCAR) benchmark and the simulated Daxing dataset, CLAW-GAN achieves state-of-the-art performance and demonstrates superior robustness across varying cloud coverage.
IC3, also known as property-directed reachability (PDR), is a commonly-used algorithm for hardware safety model checking. It checks if a state transition system complies with a given safety property. IC3 either returns UNSAFE (indicating property violation) with a counterexample trace, or SAFE with a checkable inductive invariant as the proof to safety. In practice, the performance of IC3 is dominated by a large web of interacting heuristics and implementation choices, making manual tuning costly, brittle, and hard to reproduce. This paper presents IC3-Evolve, an automated offline code-evolution framework that utilizes an LLM to propose small, slot-restricted and auditable patches to an IC3 implementation. Crucially, every candidate patch is admitted only through proof-/witness-gated validation: SAFE runs must emit a certificate that is independently checked, and UNSAFE runs must emit a replayable counterexample trace, preventing unsound edits from being deployed. Since the LLM is used only offline, the deployed artifact is a standalone evolved checker with zero ML/LLM inference overhead and no runtime model dependency. We evolve on the public hardware model checking competition (HWMCC) benchmark and evaluate the generalizability on unseen public and industrial model checking benchmarks, showing that IC3-Evolve can reliably discover practical heuristic improvements under strict correctness gates.
Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substrates. Currently, generative models dominate functional protein design but cannot model pocket-substrate interactions, which limits enzyme generation with precise catalytic environments. To address this issue, we propose EnzyPGM, a unified framework that jointly generates enzymes and substrate-binding pockets conditioned on functional priors and substrates, with a particular focus on learning accurate pocket–substrate interactions. At its core, EnzyPGM includes two main modules: a Residue-atom Bi-scale Attention (RBA) that jointly models intra-residue dependencies and fine-grained interactions between pocket residues and substrate atoms, and a Residue Function Fusion (RFF) that incorporates enzyme function priors into residue representations. Also, we curate EnzyPock, an enzyme–pocket dataset comprising 84,336 enzyme–substrate pairs across 1,036 four-level enzyme families. Extensive experiments demonstrate that EnzyPGM achieves state-of-the-art performance on EnzyPock. Notably, EnzyPGM reduces the average binding energy by 0.47 kcal/mol over EnzyGen, showing its superior performance on substrate-specific enzyme design. The code is available at https://github.com/John-Lin98/EnzyPGM.
Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: although they can accurately recognize and describe object locations in images, they often fail to faithfully execute the same spatial relations during text-to-image generation, where such relations constitute core semantic information in remote sensing. Motivated by this observation, we propose Uni-RS, the first unified multimodal model tailored for remote sensing, to explicitly address the spatial asymmetry between understanding and generation. Specifically, we first introduce explicit Spatial-Layout Planning to transform textual instructions into spatial layout plans, decoupling geometric planning from visual synthesis. We then impose Spatial-Aware Query Supervision to bias learnable queries toward spatial relations explicitly specified in the instruction. Finally, we develop Image–Caption Spatial Layout Variation to expose the model to systematic geometry-consistent spatial transformations. Extensive experiments across multiple benchmarks show that our approach substantially improves spatial faithfulness in text-to-image generation, while maintaining strong performance on multimodal understanding tasks like image captioning, visual grounding, and VQA tasks.
Graph Masked AutoEncoders (GMAEs) have achieved notable success in diverse downstream tasks. However, their superior performance usually relies on reliable and unperturbed input graphs. In real-world scenarios, graphs are vulnerable to adversarial attacks (e.g., perturbations on node features or topology). As a result, existing GMAEs tend to overfit these perturbed inputs, leading to poor robustness. Motivated by this, we propose an adversarial masked graph modeling for robust graph autoencoders, named ArmorGAE. Specifically, we first design a dual-consistency graph augmentation method that enriches the graph topology to mitigate structural vulnerabilities. Theoretically, we establish a connection between ArmorGAE and contrastive learning. It demonstrates that our augmentation strategy in GMAEs is equivalent to effectively expanding the set of high-quality positive samples in contrastive learning, providing richer and more reliable supervisory signals. We then present an adversarial masked autoencoder to mitigate model overfitting to the perturbed graph. The generator and discriminator are jointly trained under an adversarial paradigm, where the former aims to recover the underlying true data distribution, while the latter distinguishes reconstructed and real edges. Experimental results on five datasets demonstrate that ArmorGAE outperforms state-of-the-art methods in terms of classification accuracy on clean graphs and adversarial robustness under 16 distinct adversarial scenarios. The codes are publicly available at https://github.com/ZZY-GraphMiningLab/ArmorGAE.
Adaptive Learning Systems (ALS) play a key role in promoting the equity of education, which can provide personalized teaching on a large scale. However, the existing question recommendation methods face the problem of data sparsity at the scenario of cold-start, and lack the ability of long-term teaching reasoning. To address these challenges, we propose QuesRecAgent, a multi-agent framework which involves Large Language Models (LLMs) and the Relexion mechanism for adaptive question recommendation. Different from the traditional black-box methods, the QuesRecAgent applies a framework Dual-Loop State Decoupling architecture, which achieves a rapid updating of beliefs through simulated interaction in the inner loop and do the authoritative diagnosis and meta-strategy reflection to optimize the teaching strategy in the outer loop. Furthermore, with the support of a Topological Knowledge Graph (TKG) and the theory of Zone of Proximal Development (ZPD), the QuesRecAgent ensures a high-quality learning guidance even facing students with limited interaction records. Our experiments on three real-world datasets show that the QuesRecAgent outperforms state-of-the-art baselines in total knowledge gain and other metrics, providing a robust and interpretable solution for promoting high-quality personalized education.