Codecfakes (CFs) are a type of speech deepfakes generated through Audio Language Models (ALMs), with Neural Audio Codecs (NACs) forming the core mechanism for speech encoding and generation. CFs exhibit distributional characteristics that differ from vocoder-based deepfakes, causing detectors trained on vocoder data to generalize poorly to CFs detection. Although this has led to the development of CF detection benchmarks, existing resources are largely confined to English—and to a limited extent Chinese—leaving South-East Asian (SEA) languages unexplored. To bridge this gap, we introduce SEA-CF, the first large-scale benchmark for CF detection spanning multiple SEA languages, diverse speaker profiles, and a wide range of NAC architectures. SEA-CF is constructed by synthesizing publicly available real speech corpora. Our experiments show that state-of-the-art (SOTA) CF detectors trained on English-centric datasets fail to generalize to SEA speech due to language-specific phonetic structures, tonal variations, and rich prosodic diversity. We further conduct a comprehensive zero-shot and fine-tuned evaluation of recent SOTA ALMs on SEA-CF. Fine-tuning the ALMs improves performance, however, these are very large being impractical for real-world application due to their scale, particularly in low-resource and latency-constrained settings. To address this limitation, we propose a novel small-ALM, GARUDA tailored for CF detection, which delivers strong performance while remaining lightweight. Extensive evaluations demonstrate that the proposed Small-ALM outperforms strong end-to-end and ALM-based baselines, establishing a new, practical direction for robust CF detection in SEA languages and beyond.
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Precise pest identification is essential for sustainable agriculture. Current visual recognition systems are brittle in the wild, where performance degrades due to occlusion and variable illumination. In contrast, plant electrophysiological signals serve as a robust, all-weather physiological modality, capable of detecting cryptic feeding behaviors that escape optical sensors. However, this field remains constrained by the scarcity of data and the absence of specialized algorithms. To bridge this gap, we introduce the Herbivory-Induced Plant Bio-signal Multimodal (HIPB-MM) dataset, the first fine-grained dataset comprising 4,023 synchronized plant electrophysiological signal-video pairs recording the feeding processes of three typical pest species. To address the weak and non-stationary nature of these signals, we propose the Herbivory-Induced Physiological Sensing (HIPS) framework. It integrates a Morphological Semantic Decoupling strategy to recover robust slow-wave semantics, and a Generation-State Encoder to model latent physiological states. Complementing this, an auxiliary dual-stream visual branch calibrates signal representations using explicit behavioral and morphological cues. Experiments demonstrate that HIPS establishes a solid benchmark (69.81% accuracy), comprehensively outperforming state-of-the-art baselines. Crucially, this work validates plant electrophysiology as a low-cost, all-weather modality for sustainable crop protection, effectively reducing pesticide dependency and safeguarding ecosystem health.
Spread through air spaces (STAS) constitutes a novel invasive pattern in lung adenocarcinoma (LUAD), associated with tumor recurrence and diminished survival rates. However, large-scale STAS diagnosis in LUAD remains a labor-intensive endeavor, compounded by the propensity for oversight and misdiagnosis due to its distinctive pathological characteristics and morphological features. Consequently, there is a pressing clinical imperative to leverage deep learning models for STAS diagnosis. This study initially assembled histopathological images from STAS patients at the Second Xiangya Hospital and the Third Xiangya Hospital of Central South University, alongside the TCGA-LUAD cohort. Three senior pathologists conducted cross-verification annotations to construct the STAS-SXY, STAS-TXY, and STAS-TCGA datasets. We then propose a multi‑pattern attention-aware multiple instance learning framework, named STAMP, to analyze and diagnose the presence of STAS across multi‑center histopathology images. Specifically, the dual‑branch architecture guides the model to learn STAS‑associated pathological features from distinct semantic spaces. Transformer-based instance encoding and a multi‑pattern attention aggregation modules dynamically selects regions closely associated with STAS pathology, suppressing irrelevant noise and enhancing the discriminative power of global representations. Moreover, a similarity regularization constraint prevents feature redundancy across branches, thereby improving overall diagnostic accuracy. Extensive experiments demonstrated that STAMP achieved competitive diagnostic results on STAS-SXY, STAS-TXY and STAS-TCGA, with AUCs of 0.8058, 0.8017, and 0.7928, respectively, surpassing the clinical level. The 10 open baseline results establish a benchmark for STAS diagnostic research and facilitate the future generalizability and clinical integration of computational pathology technologies. Dataset features and code are accessible at https://github.com/panliangrui/IJCAI2026.
Offline robotic control requires long-horizon reasoning from fixed datasets while avoiding unsafe extrapolation beyond demonstrated behavior. We propose GRALP, a principled framework that resolves this tension by jointly enforcing support preservation and controllability at the level of temporal abstraction. GRALP adopts a deliberate architectural separation: diffusion is used exclusively as a deterministic action decoder for executing fixed latent skills, while planning and value estimation operate entirely in latent space under conservative constraints. This design enables stable value learning, controllable skill composition, and efficient planning without trajectory-level diffusion sampling at inference. Across unified D4RL benchmarks, GRALP achieves the highest average performance on Navigation, Sequential (Kitchen), and Adroit domains while remaining competitive on locomotion tasks. On contact-rich RoboSuite manipulation with human demonstrations (Lift and Pick-and-Place), GRALP achieves consistently high success rates (over 94%). These results indicate that reliable long-horizon offline control emerges when expressivity is confined to execution and decision-making operates over support-aligned latent abstractions.
Recent breakthroughs in Vision-Language Models (VLMs) have shown strong potential in medical analysis, but they remain limited in the brain lesion image domain, especially when pathological regions occupy a small portion of the image. This is because VLMs tend to put excessive attention on background regions that are visually similar to target regions. Based on the findings, we propose SAM-GPT, a novel framework that leverages segmentation-derived spatial priors to support VLM-based lesion classification. It first uses an enhanced segmentation model to localize pathological regions for diagnostic tasks, and then converts lesion attributes (e.g., size, pixel range, lesion location) into linguistic guidance for a vision–language model. To improve small lesion recognition, we incorporate a new Hilbert scanning method into Mamba that improves both local spatial continuity and global spatial modeling. Experiments on benchmark datasets show that our model achieves an average Dice coefficient of 72.80% on the brain lesion segmentation task and an accuracy of 80.56% on the brain disease classification task, demonstrating the effectiveness of the proposed framework. The code is available at https://github.com/1JF-Wang/sam-gpt.
Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, participating institutions often possess heterogeneous computational resources, resulting in imbalanced LoRA ranks, which pose a major challenge for effective collaboration. In addition, real-world applications in domains such as healthcare and transportation frequently suffer from missing modalities due to user mistakes or device failures, which significantly degrade global model performance in federated settings. To the best of our knowledge, no prior work has addressed these two challenges simultaneously in federated VLLMs. To tackle these issues, we propose FediLoRA, a lightweight federated LoRA aggregation framework that effectively mitigates the impact of missing modalities in heterogeneous environment. FediLoRA is explicitly motivated by the observation that simple averaging and structured editing can jointly benefit both global and personalized models. Our approach achieves strong performance across multiple general-domain and medical-domain benchmark datasets. Additional experiments on healthcare data further demonstrate that FediLoRA is well-suited for practical, real-world deployment scenarios. Our code is released at https://github.com/gotobcn8/FediLoRA.
We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in supervised biomedical entity linking (BEL): the scarcity of expert-annotated training data. SynCABEL leverages large language models to generate context-rich synthetic training examples for all candidate concepts in a target knowledge base, providing broad supervision without manual annotation. We demonstrate that SynCABEL, when combined with decoder-only models and guided inference establish new state-of-the-art results across three widely used multilingual benchmarks: MedMentions for English, QUAERO for French, and SPACCC for Spanish. Evaluating data efficiency, we show that SynCABEL reaches the performance of full human supervision using up to 60% less annotated data, substantially reducing reliance on labor-intensive and costly expert labeling. Finally, acknowledging that standard evaluation based on exact code matching often underestimates clinically valid predictions due to ontology redundancy, we introduce an LLM-as-a-judge protocol. This analysis reveals that SynCABEL significantly improves the rate of clinically valid predictions. Our synthetic datasets, models, and code are released to support reproducibility and future research. • HuggingFace Datasets & Models • GitHub Repository
Medical image quality assessment (IQA) serves as a critical safeguard for precise clinical diagnosis and treatment. However, existing methods still face challenges arising from data scarcity and heterogeneity across imaging domains, which confine solutions to domain-specific designs and limit their cross-domain generalization ability. In response, we construct a multi-domain and multi-organ dataset comprising 9,105 2D and 3D medical images across three imaging domains and 18 organs, annotated by radiologists. Building upon this, we propose a cross-domain universal medical IQA method termed CDMIQA, which integrates efficient feature extractors with Hierarchical Perceptual Encoding Modules to capture and refine multi-level perceptual features while mitigating interference from noise and artifacts. Notably, the Adaptive Semantic Perception Module is designed to extract semantic features; it enhances adaptability to domain-specific images by dynamically encoding quality variations across different imaging domains and organs. Furthermore, a Top-down Feature Fusion Module is utilized to progressively aggregate features under the guidance of semantic information, reinforcing the model's feature representation capability. Experimental results on the proposed dataset demonstrate that our method outperforms standard competitors and exhibits superior generalization ability across diverse domains. Our code and dataset are available at https://github.com/Leilei-Huang-work/CDMIQA.
Drug repositioning has emerged as an attractive drug development strategy with deep learning-based computational methods showing great potential in predicting Drug-Disease Associations (DDAs). However, dominant computational paradigms typically rely on Random Negative Sampling (RNS) and static embedding fusion, leading to two fundamental limitations. First, RNS treats unobserved pairs uniformly, resulting in coarse decision boundaries that fail to distinguish true associations from ambiguous candidates. Second, static fusion applies a monolithic combination of heterogeneous features, failing to adapt to the sample-specific dominance of different biological mechanisms. To address these issues, we propose DIAM, which establishes a mechanism-adaptive paradigm by explicitly decoupling structural and molecular signals. Specifically, DIAM introduces a Dual-Stream Biological Mechanism Decoupling module to construct global structural propagation and local molecular interaction views explicitly. Leveraging these views, we design a biological plausibility score to guide the hard negative sampling, enforcing finer-grained decision boundaries. Furthermore, an Adaptive Residual Gating (ARG) is devised to perform instance-aware modulation, dynamically weighing the contribution of global and local views for each specific pair. Extensive experiments on three benchmark datasets demonstrate that DIAM outperforms seven state-of-the-art methods. A case study on Alzheimer's disease further validates the model's effectiveness in identifying potential candidate drugs for practical application.
Foundation models are reshaping medical imaging, yet their application in echocardiography remains limited, hindered by a heavy reliance on private datasets that prevent reproducible comparison. Echocardiography poses unique challenges, including noisy acquisitions, high frame redundancy, and limited diverse public datasets. To address this, we introduce CardioBench, a comprehensive benchmark for echocardiography foundation models. Specifically, CardioBench unifies eight publicly available datasets into a standardized suite spanning four regression and five classification tasks, covering functional, structural, diagnostic, and view recognition endpoints. Leveraging this framework, we evaluate several leading foundation models, including cardiac-specific, biomedical, and general-purpose encoders, under consistent zero-shot, probing, and alignment protocols. Our analysis reveals that while general-purpose encoders transfer well and often close the gap with probing, they struggle significantly with fine-grained distinctions like view classification and subtle pathology recognition. Results indicate that models capturing temporal cardiac dynamics perform best on functional tasks, while retrieval-based approaches generalize more consistently across datasets. By releasing preprocessing, splits, and public evaluation pipelines, CardioBench establishes a reproducible reference point to guide the architectural design of future echocardiography and possibly other medical imaging foundation models.
Predicting single-cell gene responses to chemical perturbations is vital for personalized therapy, yet existing deep learning models face significant hurdles. Standard regression-based approaches suffer from "mean prediction bias," failing to capture cellular heterogeneity, while current Flow Matching (FM) methods struggle with the extreme sparsity of single-cell data and the dimensionality mismatches inherent in transformer-based generation. To address these challenges, we introduce sc2Flow, a dual-stage framework that unifies discrete and continuous flow matching. sc2Flow first predicts the binary mask of expressed genes and subsequently models their quantitative levels using a scalable Transformer. This decoupling effectively resolves dimensionality conflicts and eliminates parameter redundancy. Extensive benchmarks on Sci-Plex3 and other datasets demonstrate that sc2Flow significantly outperforms state-of-the-art baselines on distribution matching, successfully mitigating mean bias to preserve critical biological heterogeneity.
Active learning aims to maximize model performance with minimal annotation costs by selecting the most informative samples from large unlabeled pools, which often face a budget dilemma: uncertainty-based methods induce redundancy under low budgets, while representativeness-based methods struggle to mine challenging samples under high budgets. Although some heuristic parameter interpolation schemes attempt to bridge this gap, such strategies suffer from a misalignment between theoretical assumptions and real-world distributions, failing to achieve a good exploration-exploitation balance across all scenarios. In this paper, we propose a general active learning framework based on Distribution-Aware Energy Minimization, which reformulates sample selection as minimizing the energy function for the distributional discrepancy between the selected subset and the global uncertainty field. This physical-inspired perspective naturally derives a Hamiltonian comprising attractive terms and repulsive terms, mathematically achieving an intrinsic and dynamic balance between uncertainty and representativeness. Furthermore, we transform the optimization objective to the ground-state search of an Ising Model, enabling efficient solutions via Coherent Ising Machine. Extensive numerical experiments show that our method outperforms prior state-of-the-art methods across multi-budget regimes on several benchmark datasets. Validation on a real quantum hardware also demonstrates the potential for quantum computer in future large-scale selection tasks.
Image-text alignment relies on large-scale, well-aligned image-text pairs, yet web-crawled datasets inevitably introduce noisy correspondence. Existing methods typically re-weight training pairs by estimating semantic matching degrees from the model's own similarity predictions, which are inherently unreliable under noise and often overestimate partially aligned pairs. To overcome this limitation, we introduce a novel semantic matching degree estimation method based on ranking consistency. Our basic idea is that for a well-aligned image-text pair (I, T), the image I and text T should occupy semantically consistent positions in the embedding space. Thus, using I and T as queries to retrieve other texts (images) should yield two highly consistent rankings. Therefore, we reweight the training paris by measuring the Normalized Discounted Cumulative Gain (NDCG) between its cross-modal and intra-modal retrieval results. This signal is derived from the global data manifold structure rather than point-wise predictions, making it more robust to noise. Experiments on multiple cross-modal retrieval benchmarks validate the effectiveness of our method. Code is available at https://github.com/Aliinton/RCR.
Offline safe reinforcement learning learns high-return policies that satisfy hard safety constraints using only a pre-collected dataset. This setting is challenging due to the inability to explore, and the risk of propagating value errors through unsafe state-space regions. To address this, first, we characterize the safe state region by developing a framework for learning control barrier functions (CBFs) using a novel generalized Bellman operator, yielding a persistent safety set, from which the agent can remain safe indefinitely. Second, we show that several existing safety set estimation methods (e.g., reachability-constrained RL) can be formulated within our CBF learning framework, highlighting its generality. We further propose a new CBF that ensures safety under environment dynamics uncertainty, unlike standard CBFs designed for deterministic settings. Third, we propose a new reward maximization algorithm that effectively exploits our learned persistent safety set for reward critic estimation. Empirical results on standard benchmarks show that our approach achieves state-of-the-art safety with fewer constraint violations while maintaining competitive returns.
Irregular Multivariate Time Series (IMTS) analysis is a challenging task as asynchronous irregular sampling disrupts intra-variable temporal consistency and cross-variable alignment. Most existing methods model multivariate correlations at either the variable or observation level in static ways. They suffer from correlation loss or cross-variable misalignment inevitably. In this paper, we propose a novel method DyH2-STGraph, Dynamic Hierarchical Heterogeneous Spatio-Temporal Graph, for IMTS forecasting. Within the graph, irregular observations are represented as nodes with spatio-temporal coordinates and observed values, connected with other neighbor observation nodes dynamically by spatio-temporal neighbor selection, while variables are treated as hyper nodes, connected with their constituent observation nodes as well as other variable nodes. The multivariate correlations are classified into the following three categories in specific manners: (1) Intra-variable coarse-to-fine correlations between variable nodes and their constituent observation nodes captured by hierarchical message propagation, (2) Inter-variable fine-grained correlations among neighboring observation nodes captured by spatio-temporal attention, and (3) Inter-variable coarse-grained correlations among variable nodes captured by attention. Extensive experiments on four benchmark datasets demonstrate that DyH2-STGraph significantly outperforms state-of-the-art methods while maintaining competitive efficiency.
Retrieval‑augmented generation (RAG) improves knowledge‑intensive QA tasks by incorporating external evidence, yet retrieval remains imperfect and often returns irrelevant or misleading passages. We refer to this as Noisy RAG, where LLMs suffer from inconsistent relevance signals due to the fact that relevance identification is carried out by only a small subset of internal modules (e.g., retrieval heads) and can easily be confounded by other modules. Recent work attempts to enhance the denoising ability of the LLM through external emphasis, prompting the LLM to highlight helpful passages as the emphasis before answering. However, these textual identifications themselves depend on the same inconsistent signals and thus become less reliable. Our key insight is that, because relevance signals reside in only a few modules, emphasis should be applied directly within these modules rather than only at the text level. We therefore propose \textit{selective internal emphasis} to amplify the relevance signals, implemented via a lightweight, plug‑and‑play signal amplifier that operates inside the LLM. The amplifier performs token‑ and channel‑level selective emphasis during a standard RAG fine‑tuning pipeline, with the base LLM frozen. Across four QA benchmarks and three LLM scales, our method consistently improves accuracy and robustness. Furthermore, generalization and interpretability analyses show that the amplifier captures retrieval‑related patterns rather than only dataset‑specific patterns, enabling more reliable passage-denoising.
Effective multimodal learning in real-world scenarios depends on a nuanced treatment of uncertainty, which arises at three levels: (1) Intrinsic Uncertainty from modality-specific noise or ambiguity; (2) Relational Uncertainty due to cross-modal conflicts or redundancy; and (3) Aggregated Uncertainty when fusing potentially inconsistent signals. Most existing methods overlook this hierarchy, applying a single uncertainty model. We propose Adaptive Evidential Multimodal Representation Learning (AEMRL), a framework aligned with this multi-level view. To address intrinsic uncertainty, Disentangled Evidential Uncertainty Encoding (D-EUE) provides interpretable, class-aware reliability scores per modality. For relational uncertainty, Uncertainty-Conditioned Dynamic Factorization (UDF) uses a hypernetwork to dynamically extract complementary cues and suppress conflict. To resolve aggregated uncertainty, Adaptive Fusion & Conflict-Aware Calibration (AFCAC) adaptively weights evidence streams and calibrates final predictions based on detected conflicts. Extensive experiments on five diverse benchmark datasets show that AEMRL consistently enhances task accuracy, reduces calibration error, and improves robustness to noise, semantic conflict, and missing modalities.
Data-free knowledge distillation enables model distillation without original training data, which is critical for privacy-sensitive tabular domains. However, existing methods do not perform well on tabular data because they do not explicitly address feature interactions, which are critical for encoding predictive knowledge. We identify interaction diversity, systematic coverage of feature combinations, as an important factor for effective tabular model distillation. To operationalize this insight, we propose TabKD, which learns adaptive feature bins aligned with teacher decision boundaries, then generates synthetic queries that ensure uniform pairwise interaction coverage. Across 4 benchmark datasets and 4 teacher architectures, TabKD achieves the highest student-teacher agreement in 14 of 16 configurations, outperforming 5 state-of-the-art baselines. We further show that interaction coverage strongly correlates with distillation quality, validating our core hypothesis. Our work establishes interaction-focused exploration as a principled framework for tabular model distillation.
With the increasing application of graph learning advanced by deep learning, out-of-distribution (OOD) detection for graph-structured data has become an imperative challenge in the real world. Graph neural networks (GNNs) provide a promising solution for OOD detection. However, GNNs' core message-passing mechanism inherently relies on local neighborhood aggregation, and traditional graph structures only characterize pairwise node relations, failing to capture high-order associations and global patterns. Towards this end, in this paper, we propose a Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of Distribution detection (termed HGOOD). Specifically, we construct two branches to mine graph semantics in a comprehensive manner. On the one hand, we employ a graph feature branch to encode neighborhood interactions via node-level and graph-level contrastive learning. On the other hand, we incorporate the hypergraph-global branch, which adaptively models graphs’ high-order global correlations. Building upon this, we introduce a cross-branch prototype contrast that aligns the captured graph patterns with their cross-branch clustering prototypes to enhance the semantic manifold of the in-distribution (ID) graph. Extensive experiments on benchmark datasets demonstrate that our HGOOD consistently outperforms prior methods. Our code is available at https://anonymous.4open.science/r/HGOOD-CFE2/.
Although Large Language Models (LLMs) excel at code generation, recent research reveals that they exhibit an insufficient grasp of core programming concepts, such as data flow and control flow. This limitation undermines their robustness when encountering variations in these concepts in practice; however, effective solutions that explicitly target this gap remain limited. To address this challenge, we propose ProCURE, a concept-aware consistency learning framework designed to enhance LLMs’ understanding of programming concepts. Specifically, ProCURE first performs automated concept-oriented code augmentation to construct a concept-aligned dataset covering representative programming concepts. It then conducts concept-aware fine-tuning, encouraging the model to capture fine-grained concept variations and learn appropriate generation behaviors under such variations via a novel concept-sensitive consistency loss. To quantify programming concept understanding, we introduce the Concept Consistency Score (CCScore), defined as the proportion of correct generations preserved under concept variations. A higher CCScore indicates a more profound understanding of programming concepts. We evaluate ProCURE on four open-source LLMs across three widely used code generation benchmarks. Experimental results show that ProCURE improves CCScore by an average of 17.9 points, demonstrating its effectiveness in addressing the programming concept understanding gap.