Existing Programming-By-Example (PBE) systems often rely on simplified benchmarks that fail to capture the high structural complexity of real-world regexes, such as deeper nesting and frequent use of union operations. To overcome the resulting performance drop, we propose ReSyn, a synthesizer-agnostic divide-and-conquer framework that decomposes complex synthesis problem into manageable sub-problems. We also introduce Set2Regex, a parameter-efficient synthesizer capturing the permutation invariance of examples. Experimental results demonstrate that ReSyn significantly boosts accuracy across various synthesizers, and its combination with Set2Regex establishes a new state-of-the-art on challenging real-world benchmark. The complete source code, datasets, and pre-trained model checkpoints are publicly available at https://github.com/mrseongminkim/ReSyn.
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Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are often constrained by suboptimal representation fidelity. We identify two critical bottlenecks: temporal non-causality in standard encoders that induces temporal confounding in non-stationary dynamics, and the absence of explicit channel saliency mechanisms that allows noise to contaminate the latent space. To address these challenges, we propose the Causal Attention and Spatio-temporal Encoder Network (CASE-Net), an architecture designed for structural manifold pre-conditioning. CASE-Net synergizes a Causal Temporal Encoder, which enforces physical arrow-of-time constraints via masked self-attention and causal convolutions, with an Adaptive Channel Recalibration module functioning as an information bottleneck to suppress detrimental noise. Comprehensive evaluations across six heterogeneous domains demonstrate that CASE-Net establishes new state-of-the-art benchmarks on four tasks, achieving a peak accuracy of 98.6% on the AWR dataset and superior robustness in non-stationary regimes.
While large-scale reasoning models have achieved remarkable performance gains by scaling test-time computation through extended Chain-of-Thought (CoT) sequences, their practical utility is severely constrained by protracted inference lengths and high computational latency. In this paper, we present Differentiable Hierarchical Fusion (DHF), a novel framework that merges reasoning models with efficient base models via differentiable optimization to produce concise, accurate outputs. We introduce a dual-factor adaptive weighting mechanism to capture intra-block (Attention vs. MLP) variance and inter-block (shallow vs. deep layers) importance hierarchies, thereby addressing key limitations of static merging heuristics. Specifically, DHF optimizes fusion coefficients using gradient descent on a loss function that jointly minimizes cross-entropy (for accuracy) and response length (for conciseness). Furthermore, we enhance an outlier-aware initialization strategy to seed coefficients based on activation density and construct multi-calibration datasets to improve the model’s generalization ability. Comprehensive evaluations on Qwen and LLaMA models across six reasoning benchmarks show that DHF reduces the average response length by 55% while boosting accuracy by 2.8%–6% compared to existing state-of-the-art merging methods.
Molecular property prediction is a fundamental task with wide-ranging applications in chemistry, materials science, and drug discovery. Beyond predictive accuracy, the reliability of model predictions, particularly their uncertainty awareness, is critical for high-stakes downstream applications. However, most existing methods predominantly optimize performance metrics while overlooking prediction reliability, often producing results that are both inaccurate and overconfident. To address this challenge, we propose an uncertainty-guided framework that integrates adaptive local adversarial perturbation with evidential deep learning for reliable molecular property prediction. The proposed method identifies sensitive molecular regions through gradient-based sensitivity analysis and applies locally adaptive perturbations whose magnitudes are dynamically modulated by predictive uncertainty. This approach explicitly challenges the model to preserve predictive stability under structure-aware perturbations, thereby enhancing its sensitivity to critical molecular substructures. Furthermore, we introduce an evidential calibration strategy that aligns uncertainty with the prediction discrepancy between clean and adversarial molecular views, enabling the learned evidence to faithfully reflect prediction stability. Extensive experiments on benchmark molecular property prediction datasets demonstrate that our approach achieves state-of-the-art predictive performance while providing more reliable uncertainty estimates than widely used uncertainty quantification methods. These results highlight the potential of the proposed framework as a reliable tool for risk-sensitive virtual screening and molecular decision-making. Our code is available at https://github.com/Ubehind/ugap.
Accurate spatio-temporal forecasting requires modeling interactions across multiple spatial and temporal scales. Existing Graph Neural Network (GNN) forecasters primarily operate at a single local scale, limiting their ability to capture global processes that govern system dynamics. Hierarchical GNNs (HGNNs) aim to address this limitation by learning multiscale representations, but in practice, they often fail to preserve global information during coarse-to-fine propagation. We identify this limitation as downward memory loss, where global trends learned at coarse resolutions diminish before influencing fine-grained predictions, leading to misaligned local dynamics. We propose HiGFlow, an HGNN forecaster that explicitly preserves multiscale trends through a self-updating memory buffer that integrates into the coarse-to-fine information flow. This design maintains global contextual signals as an inductive bias throughout hierarchical decoding. We provide a theoretical analysis indicating that when decoding, HiGFlow preserves multiscale trends where residual-based architectures may fail to do so. Empirically, HiGFlow achieves substantially lower MAE and RMSE than state-of-the-art forecasting models across multiple benchmark datasets, demonstrating the importance of explicit global memory in multiscale spatio-temporal forecasting. Our implementation is available at https://github.com/TB862/HiGFlow.
The rapid advancement of generative AI has made audio deepfakes increasingly indistinguishable from authentic human vocals, posing significant threats to persons-of-interest (POI) such as public figures. Current detection systems primarily rely on generic, black-box models that fail to capture speaker-specific idiosyncratic traits and lack interpretability. In this paper, we propose Phoneme-based Voice Profiling (PVP), a novel personalized defense framework. By shifting the detection paradigm from macro-utterance analysis to micro-phonetic modeling, PVP captures the unique acoustic distributions underlying a POI’s habitual articulatory patterns. Specifically, our framework models speaker-specific phonetic realizations using lightweight Gaussian Mixture Models (GMMs) estimated solely from bona fide reference speech. This design enables data-efficient profiling and robust generalization to previously unseen spoofing attacks without requiring heavy spoof-specific training. Furthermore, we introduce the first large-scale Chinese POI deepfake dataset to benchmark speaker-specific detection. Experimental results demonstrate that PVP significantly outperforms state-of-the-art generic detectors in POI spoofing scenarios, achieving substantial EER reductions while providing fine-grained, phoneme-level interpretability for forensic analysis. Code and data are available at: https://github.com/JunXue-tech/PVP
Static benchmarks for LLMs are increasingly compromised by contamination and overfitting, especially on knowledge-intensive reasoning tasks. While recent dynamic benchmarks can alleviate staleness, they often increase difficulty at the expense of answerability and controllability. In this paper, we propose StressEval, a failure-driven data synthesis framework that turns observed model failures into dynamic, challenging, and controlable test instances. StressEval consists of three stages: (i) it constructs a semi-structured difficulty card that identifies the failed reasoning step and its root cause; (ii) it applies a dual-perspective instance-synthesis method that targets both knowledge gaps and reasoning breakdowns while preserving the underlying difficulty factors; and (iii) it applies a gating mechanism to retain only grounded, unambiguous instances. Seeding from multiple knowledge-intensive reasoning datasets, we employ StressEval to build Dynamic-OneEval, a focused suite of challenging dynamic benchmark. Across several state-of-the-art LLMs, Dynamic-OneEval yields substantially larger performance drops than the original benchmarks while retaining explicit difficulty factors, enabling more actionable iteration.
Clean-label backdoor attacks pose significant security threats to deep neural networks by injecting triggers without altering ground-truth labels. However, existing methods face a fundamental dilemma: sample-agnostic triggers are robust but easily detectable, while sample-specific triggers offer superior stealthiness but suffer from limited effectiveness due to feature suppression. To bridge this gap, we propose a new backdoor trigger framework called Mask-Guided Hybrid Trigger (MGHT). MGHT uses an adaptive mask to allocate spatial regions of the hybrid trigger between a sample-agnostic anchor for reliable memorization and a sample-specific camouflage for perceptual and semantic consistency. To prevent the optimization from greedily relying on a single trigger component, we further propose a Synergy-driven Co-optimization Strategy with a margin-based Synergy Loss. This ensures that the hybrid trigger is more effective and robust than either component alone. Extensive experiments on benchmark datasets demonstrate that MGHT achieves competitive performance, attaining over 99% ASR on CIFAR-10 and CelebA and showing strong effectiveness on higher-resolution benchmarks, while maintaining high visual quality (PSNR > 30 dB) and robustness to mainstream backdoor defenses.
Partial-label learning (PLL) addresses challenging scenarios where each instance is associated with a set of candidate labels and only one is the truth. Most existing PLL methods rely on static disambiguation heuristics, which are prone to error propagation when the ambiguity labels are high. To address this issue, we propose a novel mutual information guided reinforcement learning framework for partial label disambiguation (MR-PLL). In this framework, label disambiguation is formulated as a sequential Markov decision process, where an agent dynamically discriminates whether to retain, modify, or abstain from correcting ambiguous labels. To ensure reliable decision making, we introduce a mutual information guided gating mechanism that adaptively adjusts the confidence of soft label propagation according to the dependency between feature representations and labels. The abstention mechanism allows the model to postpone uncertain decisions, resulting in more reliable disambiguation. Furthermore, we design an information weighted reward term during the actor-critic process to gradually improve the label disambiguation ability of the policy, and provide theoretical analysis on convergence and bias reduction. Experiments on benchmark and real datasets verify the effectiveness of the proposed algorithm.
Multimodal clustering (MMC) focuses on learning consistent representations through fusing discriminative features from each modality in an unsupervised fashion. Recently, information bottleneck-based MMC methods transform the representation learning into a non-redundant multimodal feature puzzle process. However, they depend on manually setting the trade-off parameter β to compress task-irrelevant features while preserving task-relevant features, which severely impairs final clustering results. In addition, manually setting β overlooks both the information quality balance and the information quantity balance, hindering the learning of compact and meaningful representations for discovering cluster patterns. In this work, we propose a novel βqq-guided information bottleneck (βqq IB) for addressing the aforementioned problems. The core of βqq IB is a self-balance mechanism that consists of a βquality component and a βquantity component. The βquality component aims to achieve an information quality balance by modeling the relation between task characteristics and data sample scale. Meanwhile, the βquantity component intends to realize information quantity balance through modeling the dynamic changes of data scale within the deep variational architect. Experiments on six benchmark datasets demonstrate the superiority of βqq IB method. To the best of our knowledge, this is the first work to investigate self-balance learning in IB-based multimodal clustering.
Parameter-efficient adaptation methods, such as adapters and prompt learning, have become popular for transferring pre-trained vision-language models (VLMs) to downstream tasks. However, under limited supervision during adaptation, overly aggressive cross-modal alignment can distort the intrinsic structure of modality-specific representations, leading to degraded generalization. In this paper, we propose Co-adaptive Dual-path Alignment (CoDA), a new adaptation framework that explicitly disentangles and coordinates cross-modal semantic alignment and intra-modal structural consistency. In CoDA, we also propose a parent class labeling strategy that injects hierarchical semantic priors into textual prompts, further stabilizing alignment under limited supervision. Extensive experiments on eleven benchmark datasets show that CoDA outperforms state-of-the-art parameter-efficient methods, particularly under few-shot learning and distribution-shift scenarios. Our code is available at https://github.com/yizhang-ac/CoDA.
Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent. However, existing methods primarily focus on population-level shapelets optimized across the entire dataset, which leads to two fundamental limitations: (i) population-level patterns often misalign with instance-specific features, resulting in suboptimal performance and potentially misleading interpretations, and (ii) most methods treat shapelets as independent entities, overlooking important temporal dependencies and interactions among multiple patterns. To address these limitations, we propose INSHAPE, an interpretable TSC framework that discovers variable-length, discriminative temporal patterns specific to each time series. INSHAPE identifies these patterns as non-overlapping segments and models their temporal dependencies, thereby providing clear instance-level interpretations while achieving strong predictive performance. Furthermore, INSHAPE bridges local and global interpretability through a bottom-up approach, aggregating instance-level shapelets into prototypical (population-level) shapelets. Extensive experiments on 128 UCR and 30 UEA benchmark datasets show that INSHAPE consistently outperforms state-of-the-art shapelet-based methods while providing more intuitive and interpretable insights.
Recent advances in text-to-audio (TTA) and audio-to-audio (ATA) generation models have enabled the creation of highly realistic environmental sounds, raising growing concerns about malicious audio manipulation in real-world scenarios. To address this emerging threat, the ESDD 2026 Challenge was introduced as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD), featuring two tracks that evaluate generalization to unseen generators and robustness under black-box, low-resource conditions. In this paper, we present BEAT2AASIST, an enhanced deepfake detection framework built upon the BEATs-AASIST baseline. Motivated by the observation that token-based audio representations may weaken explicit preservation of structured acoustic cues, the proposed method introduces a dual-branch AASIST architecture that explicitly splits BEATs-derived representations along frequency or channel dimensions. This design enables specialized modeling of complementary spoofing artifacts that may be attenuated in unified representations. To further enrich acoustic features, we incorporate multi-layer fusion strategies that aggregate information from multiple transformer layers using concatenation, CNN-gated, and SE-gated mechanisms. In addition, vocoder-based data augmentation with multiple high-fidelity neural vocoders is employed to enhance robustness against unseen and black-box spoofing attacks. Experimental results on the EnvSDD dataset demonstrate that BEAT2AASIST achieves strong and consistent performance across both challenge tracks. In particular, the proposed approach attains 3rd place in Track 2 and 4th place in Track 1 in the ESDD 2026 Challenge, despite using fewer ensemble components than top-ranked systems. These results suggest that explicit modeling of heterogeneous acoustic subspaces, combined with targeted representation fusion and data augmentation, provides an effective and efficient design strategy for real-world environmental sound deepfake detection. The code is available at https://github.com/ikwak2/BEAT2AASIST.
Graph Neural Networks are susceptible to label noise, in which message-passing mechanisms serve as conduits for propagating erroneous supervision. Current mitigation techniques typically recover clean labels via heuristics that lack theoretical grounding, which often leads to ineffective denoising. To tackle the issue, we propose NAEM, a latent label estimation framework that models clean labels as latent variables by integrating neighborhood agreement into the Expectation-Maximization (EM) paradigm. To overcome the posterior collapse problem in standard EM under severe noise, we design a structure-aware E-step that leverages neighborhood agreement as a structural prior. This mechanism acts as a dynamic confidence gate based on local consensus to prevent the model from overfitting to noise. Simultaneously, by explicitly modeling the noise transition matrix in the M-step, NAEM decouples noise dynamics from semantic representation learning. Extensive experiments on five benchmark datasets demonstrate that NAEM consistently outperforms SOTA methods under varying noise conditions, validating the effectiveness of our framework.
Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Catastrophic Forgetting (CF): Models often sacrifice their ability to retain source-domain knowledge while pursuing cross-domain robustness. This can directly threaten diagnostic safety in already-deployed clinical scenarios. To address this, we investigate data augmentation strategies and catastrophic forgetting for medical image DG segmentation. First, we propose a structure-guided style diffusion augmentation method. Constrained by anatomical structure consistency in the frequency domain, this method performs cross-domain diffusion on the amplitude spectrum, generating samples with more diverse and broader style coverage to better support domain generalization. Then, we design a collaborative learning network with a dual-branch interactive architecture (CoDG-Net), together with a novel learning bias-guided strategy that adaptively regulates knowledge transfer at both the layer level and the task level, thereby effectively mitigating catastrophic forgetting on the source domain. Experiments and ablation studies on single-source and multi-source medical DG benchmark datasets demonstrate that CoDG-Net not only outperforms existing state-of-the-art methods in target-domain segmentation performance, but also achieves a lower forgetting rate on the source-domain data. The code is available at: https://github.com/wangprocess/CoDG-Net.
Cognitive diagnosis aims to infer students’ concept-level mastery from exercise response logs and exercise-concept associations. Fully leveraging heterogeneous relations and modeling large mastery-difficulty variations remain challenging, especially with a single predictor. To address these challenges, we propose RMCD, a unified cognitive diagnosis model that integrates relation-aware graph learning with Mixture-of-Experts (MoE) prediction. RMCD constructs a heterogeneous relational graph over students, exercises, and concepts with multiple relation types, and learns node and edge representations simultaneously. It derives relation-strength vectors from student-concept and exercise-concept edges to distinguish relation effects and refine node representations. RMCD further introduces an MoE-based prediction head that adaptively combines multiple expert predictors to capture diverse mastery-difficulty discrepancies. Experiments on benchmark datasets demonstrate that RMCD consistently outperforms state-of-the-art cognitive diagnosis methods. Our algorithm is available at https://github.com/swu-qjw-lab/code/tree/main/RMCD.
Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering process. Recent approaches introduce semantic descriptions as prior information, most of which typically relying on matching-based techniques with predefined vocabularies. However, the limited matching space restricts their adaptability to downstream clustering tasks. Moreover, these methods primarily focus on reducing bias to improve performance, frequently overlooking the importance of variance reduction. To address these limitations, we propose GSEC (Image Clustering based on Generative Semantic Guidance and Bi-Layer Ensemble), a framework designed to reduce bias through generative semantic guidance and mitigate variance via ensemble learning. Our method employs Multimodal Large Language Models to generate semantic descriptions and derive image embeddings via weighted averaging. Additionally, a bi-layer ensemble strategy integrates cross-modal information through BatchEnsemble in the inner layer and aligns outputs via an alignment mechanism in the outer layer. Comparative experiments demonstrate that GSEC outperforms 20 state-of-the-art methods across six benchmark datasets, while further analysis confirms its effectiveness in simultaneously reducing both bias and variance.
Multi-channel data fusion is essential for capturing comprehensive representations in complex systems. While graph convolutional networks have demonstrated remarkable efficacy, existing fusion paradigms primarily rely on discrete architectures governed by first-order propagation. These models are typically confined to discrete message-passing mechanisms, which makes it difficult to characterize the continuous evolution of underlying system dynamics. To address these limitations, we propose a multi-channel Graph Continuous Network (mcGCN), a novel framework for multi-channel data fusion. By formulating the information propagation as a second-order partial differential equation on graphs, mcGCN transitions from discrete layer-wise updates to a continuous dynamical system. Specifically, mcGCN integrates a multi-channel encoding module with continuous feature dynamics to initialize and evolve the latent node representations over static graph topologies. This physical analogy enables more robust information flow and effectively mitigates the performance degradation typically associated with deep graph architectures. Extensive experiments on diverse benchmark datasets demonstrate that our method outperforms state-of-the-art baselines, validating its effectiveness and robustness.
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.
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.