# Research Papers — 2025-12 · bundle page 77 of 175 > Full-text markdown of papers #3801–3850 of 8703 published in 2025-12, newest first. AI-readable mirrors of the full 8703-paper 2025-12 archive (50 papers per file). Canonical page: https://aipod.dev/papers/archive/2025-12/page/77 [← Previous bundle](https://aipod.dev/papers/archive/2025-12/page/76.md) [Next bundle →](https://aipod.dev/papers/archive/2025-12/page/78.md) ### Seismology modeling agent: A smart assistant for geophysical researchers Authors: Yukun Ren, Siwei Yu, Kai Chen, Jianwei Ma Published: 2025-12-16 Categories: cs.AI, cs.SE arXiv: https://arxiv.org/abs/2512.14429v1 To address the steep learning curve and reliance on complex manual file editing and command-line operations in the traditional workflow of the mainstream open-source seismic wave simulation software SPECFEM, this paper proposes an intelligent, interactive workflow powered by Large Language Models (LLMs). We introduce the first Model Context Protocol (MCP) server suite for SPECFEM (supporting 2D, 3D Cartesian, and 3D Globe versions), which decomposes the entire simulation process into discrete, agent-executable tools spanning from parameter generation and mesh partitioning to solver execution and visualization. This approach enables a paradigm shift from file-driven to intent-driven conversational interactions. The framework supports both fully automated execution and human-in-the-loop collaboration, allowing researchers to guide simulation strategies in real time and retain scientific decision-making authority while significantly reducing tedious low-level operations. Validated through multiple case studies, the workflow operates seamlessly in both autonomous and interactive modes, yielding high-fidelity results consistent with standard baselines. As the first application of MCP technology to computational seismology, this study significantly lowers the entry barrier, enhances reproducibility, and offers a promising avenue for advancing computational geophysics toward AI-assisted and automated scientific research. The complete source code is available at https://github.com/RenYukun1563/specfem-mcp. --- ### Effect of Document Packing on the Latent Multi-Hop Reasoning Capabilities of Large Language Models Authors: Gabriele Prato, Shagun Sodhani, Alessandro Sordoni, Sarath Chandar Published: 2025-12-16 Categories: cs.CL, cs.AI, cs.LG arXiv: https://arxiv.org/abs/2512.14427v1 The standard practice for training large language models involves packing multiple documents together to optimize computational efficiency. However, the impact of this process on the models' capabilities remains largely unexplored. To address this gap, we investigate how different document-packing strategies influence the latent multi-hop reasoning abilities of LLMs. Our findings indicate that packing can improve model performance compared to training on individual documents, at the expense of more compute. To further understand the underlying mechanisms, we conduct an ablation study, identifying key factors that explain the advantages of packing. Ultimately, our research deepens the understanding of LLM training dynamics and provides practical insights for optimizing model development. --- ### The Devil is in Attention Sharing: Improving Complex Non-rigid Image Editing Faithfulness via Attention Synergy Authors: Zhuo Chen, Fanyue Wei, Runze Xu, Jingjing Li, Lixin Duan, Angela Yao, Wen Li Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14423v2 Training-free image editing with large diffusion models has become practical, yet faithfully performing complex non-rigid edits (e.g., pose or shape changes) remains highly challenging. We identify a key underlying cause: attention collapse in existing attention sharing mechanisms, where either positional embeddings or semantic features dominate visual content retrieval, leading to over-editing or under-editing. To address this issue, we introduce SynPS, a method that Synergistically leverages Positional embeddings and Semantic information for faithful non-rigid image editing. We first propose an editing measurement that quantifies the required editing magnitude at each denoising step. Based on this measurement, we design an attention synergy pipeline that dynamically modulates the influence of positional embeddings, enabling SynPS to balance semantic modifications and fidelity preservation. By adaptively integrating positional and semantic cues, SynPS effectively avoids both over- and under-editing. Extensive experiments on public and newly curated benchmarks demonstrate the superior performance and faithfulness of our approach. --- ### Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset Authors: Waqas Ahmed Published: 2025-12-16 Categories: cs.CR, cs.LG arXiv: https://arxiv.org/abs/2512.14422v1 The cybersecurity of Industrial Control Systems that manage critical infrastructure such as Water Distribution Systems has become increasingly important as digital connectivity expands. BATADAL benchmark data is a good source of testing intrusion detection techniques, but it presents several important problems, such as imbalance in the number of classes, multivariate time dependence, and stealthy attacks. We consider a hybrid ensemble learning model that will enhance the detection ability of cyber-attacks in WDS by using the complementary capabilities of machine learning and deep learning models. Three base learners, namely, Random Forest , eXtreme Gradient Boosting , and Long Short-Term Memory network, have been strictly compared and seven ensemble types using simple averaged and stacked learning with a logistic regression meta-learner. Random Forest analysis identified top predictors turned into temporal and statistical features, and Synthetic Minority Oversampling Technique (SMOTE) was used to overcome the class imbalance issue. The analyics indicates that the single Long Short-Term Memory network model is of poor performance (F1 = 0.000, AUC = 0.4460), but tree-based models, especially eXtreme Gradient Boosting, perform well (F1 = 0.7470, AUC=0.9684). The hybrid stacked ensemble of Random Forest , eXtreme Gradient Boosting , and Long Short-Term Memory network scored the highest, with the attack class of 0.7205 with an F1-score and a AUC of 0.9826 indicating that the heterogeneous stacking between model precision and generalization can work. The proposed framework establishes a robust and scalable solution for cyber-attack detection in time-dependent industrial systems, integrating temporal learning and ensemble diversity to support the secure operation of critical infrastructure. --- ### LCMem: A Universal Model for Robust Image Memorization Detection Authors: Mischa Dombrowski, Felix Nützel, Bernhard Kainz Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14421v1 Recent advances in generative image modeling have achieved visual realism sufficient to deceive human experts, yet their potential for privacy preserving data sharing remains insufficiently understood. A central obstacle is the absence of reliable memorization detection mechanisms, limited quantitative evaluation, and poor generalization of existing privacy auditing methods across domains. To address this, we propose to view memorization detection as a unified problem at the intersection of re-identification and copy detection, whose complementary goals cover both identity consistency and augmentation-robust duplication, and introduce Latent Contrastive Memorization Network (LCMem), a cross-domain model evaluated jointly on both tasks. LCMem achieves this through a two-stage training strategy that first learns identity consistency before incorporating augmentation-robust copy detection. Across six benchmark datasets, LCMem achieves improvements of up to 16 percentage points on re-identification and 30 percentage points on copy detection, enabling substantially more reliable memorization detection at scale. Our results show that existing privacy filters provide limited performance and robustness, highlighting the need for stronger protection mechanisms. We show that LCMem sets a new standard for cross-domain privacy auditing, offering reliable and scalable memorization detection. Code and model is publicly available at https://github.com/MischaD/LCMem. --- ### DISCODE: Distribution-Aware Score Decoder for Robust Automatic Evaluation of Image Captioning Authors: Nakamasa Inoue, Kanoko Goto, Masanari Oi, Martyna Gruszka, Mahiro Ukai, Takumi Hirose, Yusuke Sekikawa Published: 2025-12-16 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2512.14420v2 Large vision-language models (LVLMs) have shown impressive performance across a broad range of multimodal tasks. However, robust image caption evaluation using LVLMs remains challenging, particularly under domain-shift scenarios. To address this issue, we introduce the Distribution-Aware Score Decoder (DISCODE), a novel finetuning-free method that generates robust evaluation scores better aligned with human judgments across diverse domains. The core idea behind DISCODE lies in its test-time adaptive evaluation approach, which introduces the Adaptive Test-Time (ATT) loss, leveraging a Gaussian prior distribution to improve robustness in evaluation score estimation. This loss is efficiently minimized at test time using an analytical solution that we derive. Furthermore, we introduce the Multi-domain Caption Evaluation (MCEval) benchmark, a new image captioning evaluation benchmark covering six distinct domains, designed to assess the robustness of evaluation metrics. In our experiments, we demonstrate that DISCODE achieves state-of-the-art performance as a reference-free evaluation metric across MCEval and four representative existing benchmarks. --- ### Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Space Authors: Dejun Hu, Zhiming Li, Jia-Rui Shen, Jia-Ning Tu, Zi-Hao Ye, Junliang Zhang Published: 2025-12-16 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.14418v2 Machine learning is profoundly reshaping molecular and materials modeling; however, given the vast scale of chemical space (10^30-10^60), it remains an open scientific question whether models can achieve convergent learning across this space. We introduce a Dual-Axis Representation-Complete Convergent Learning (RCCL) strategy, enabled by a molecular representation that integrates graph convolutional network (GCN) encoding of local valence environments, grounded in modern valence bond theory, together with no-bridge graph (NBG) encoding of ring/cage topologies, providing a quantitative measure of chemical-space coverage. This framework formalizes representation completeness, establishing a principled basis for constructing datasets that support convergent learning for large models. Guided by this RCCL framework, we develop the FD25 dataset, systematically covering 13,302 local valence units and 165,726 ring/cage topologies, achieving near-complete combinatorial coverage of organic molecules with H/C/N/O/F elements. Graph neural networks trained on FD25 exhibit representation-complete convergent learning and strong out-of-distribution generalization, with an overall prediction error of approximately 1.0 kcal/mol MAE across external benchmarks. Our results establish a quantitative link between molecular representation, structural completeness, and model generalization, providing a foundation for interpretable, transferable, and data-efficient molecular intelligence. --- ### PortAgent: LLM-driven Vehicle Dispatching Agent for Port Terminals Authors: Jia Hu, Junqi Li, Weimeng Lin, Peng Jia, Yuxiong Ji, Jintao Lai Published: 2025-12-16 Categories: cs.AI arXiv: https://arxiv.org/abs/2512.14417v1 Vehicle Dispatching Systems (VDSs) are critical to the operational efficiency of Automated Container Terminals (ACTs). However, their widespread commercialization is hindered due to their low transferability across diverse terminals. This transferability challenge stems from three limitations: high reliance on port operational specialists, a high demand for terminal-specific data, and time-consuming manual deployment processes. Leveraging the emergence of Large Language Models (LLMs), this paper proposes PortAgent, an LLM-driven vehicle dispatching agent that fully automates the VDS transferring workflow. It bears three features: (1) no need for port operations specialists; (2) low need of data; and (3) fast deployment. Specifically, specialist dependency is eliminated by the Virtual Expert Team (VET). The VET collaborates with four virtual experts, including a Knowledge Retriever, Modeler, Coder, and Debugger, to emulate a human expert team for the VDS transferring workflow. These experts specialize in the domain of terminal VDS via a few-shot example learning approach. Through this approach, the experts are able to learn VDS-domain knowledge from a few VDS examples. These examples are retrieved via a Retrieval-Augmented Generation (RAG) mechanism, mitigating the high demand for terminal-specific data. Furthermore, an automatic VDS design workflow is established among these experts to avoid extra manual interventions. In this workflow, a self-correction loop inspired by the LLM Reflexion framework is created --- ### Pattern Recognition of Aluminium Arbitrage in Global Trade Data Authors: Muhammad Sukri Bin Ramli Published: 2025-12-16 Categories: econ.GN, cs.LG arXiv: https://arxiv.org/abs/2512.14410v1 As the global economy transitions toward decarbonization, the aluminium sector has become a focal point for strategic resource management. While policies such as the Carbon Border Adjustment Mechanism (CBAM) aim to reduce emissions, they have inadvertently widened the price arbitrage between primary metal, scrap, and semi-finished goods, creating new incentives for market optimization. This study presents a unified, unsupervised machine learning framework to detect and classify emerging trade anomalies within UN Comtrade data (2020 to 2024). Moving beyond traditional rule-based monitoring, we apply a four-layer analytical pipeline utilizing Forensic Statistics, Isolation Forests, Network Science, and Deep Autoencoders. Contrary to the hypothesis that Sustainability Arbitrage would be the primary driver, empirical results reveal a contradictory and more severe phenomenon of Hardware Masking. Illicit actors exploit bi-directional tariff incentives by misclassifying scrap as high-count heterogeneous goods to justify extreme unit-price outliers of >$160/kg, a 1,900% markup indicative of Trade-Based Money Laundering (TBML) rather than commercial arbitrage. Topologically, risk is not concentrated in major exporters but in high-centrality Shadow Hubs that function as pivotal nodes for illicit rerouting. These actors execute a strategy of Void-Shoring, systematically suppressing destination data to Unspecified Code to fracture mirror statistics and sever forensic trails. Validated by SHAP (Shapley Additive Explanations), the results confirm that price deviation is the dominant predictor of anomalies, necessitating a paradigm shift in customs enforcement from physical volume checks to dynamic, algorithmic valuation auditing. --- ### Broadening View Synthesis of Dynamic Scenes from Constrained Monocular Videos Authors: Le Jiang, Shaotong Zhu, Yedi Luo, Shayda Moezzi, Sarah Ostadabbas Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14406v1 In dynamic Neural Radiance Fields (NeRF) systems, state-of-the-art novel view synthesis methods often fail under significant viewpoint deviations, producing unstable and unrealistic renderings. To address this, we introduce Expanded Dynamic NeRF (ExpanDyNeRF), a monocular NeRF framework that leverages Gaussian splatting priors and a pseudo-ground-truth generation strategy to enable realistic synthesis under large-angle rotations. ExpanDyNeRF optimizes density and color features to improve scene reconstruction from challenging perspectives. We also present the Synthetic Dynamic Multiview (SynDM) dataset, the first synthetic multiview dataset for dynamic scenes with explicit side-view supervision-created using a custom GTA V-based rendering pipeline. Quantitative and qualitative results on SynDM and real-world datasets demonstrate that ExpanDyNeRF significantly outperforms existing dynamic NeRF methods in rendering fidelity under extreme viewpoint shifts. Further details are provided in the supplementary materials. --- ### From STLS to Projection-based Dictionary Selection in Sparse Regression for System Identification Authors: Hangjun Cho, Fabio V. G. Amaral, Andrei A. Klishin, Cassio M. Oishi, Steven L. Brunton Published: 2025-12-16 Categories: stat.ML, cs.LG, math.OC, physics.comp-ph arXiv: https://arxiv.org/abs/2512.14404v1 In this work, we revisit dictionary-based sparse regression, in particular, Sequential Threshold Least Squares (STLS), and propose a score-guided library selection to provide practical guidance for data-driven modeling, with emphasis on SINDy-type algorithms. STLS is an algorithm to solve the $\ell_0$ sparse least-squares problem, which relies on splitting to efficiently solve the least-squares portion while handling the sparse term via proximal methods. It produces coefficient vectors whose components depend on both the projected reconstruction errors, here referred to as the scores, and the mutual coherence of dictionary terms. The first contribution of this work is a theoretical analysis of the score and dictionary-selection strategy. This could be understood in both the original and weak SINDy regime. Second, numerical experiments on ordinary and partial differential equations highlight the effectiveness of score-based screening, improving both accuracy and interpretability in dynamical system identification. These results suggest that integrating score-guided methods to refine the dictionary more accurately may help SINDy users in some cases to enhance their robustness for data-driven discovery of governing equations. --- ### GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion Authors: Fangzhou Lin, Guoshun He, Zhenyu Guo, Zhe Huang, Jinsong Tao Published: 2025-12-16 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.14400v2 Electric load is simultaneously affected across multiple time scales by exogenous factors such as weather and calendar rhythms, sudden events, and policies. Therefore, this paper proposes GRAFT (GRid-Aware Forecasting with Text), which modifies and improves STanHOP to better support grid-aware forecasting and multi-source textual interventions. Specifically, GRAFT strictly aligns daily-aggregated news, social media, and policy texts with half-hour load, and realizes text-guided fusion to specific time positions via cross-attention during both training and rolling forecasting. In addition, GRAFT provides a plug-and-play external-memory interface to accommodate different information sources in real-world deployment. We construct and release a unified aligned benchmark covering 2019--2021 for five Australian states (half-hour load, daily-aligned weather/calendar variables, and three categories of external texts), and conduct systematic, reproducible evaluations at three scales -- hourly, daily, and monthly -- under a unified protocol for comparison across regions, external sources, and time scales. Experimental results show that GRAFT significantly outperforms strong baselines and reaches or surpasses the state of the art across multiple regions and forecasting horizons. Moreover, the model is robust in event-driven scenarios and enables temporal localization and source-level interpretation of text-to-load effects through attention read-out. We release the benchmark, preprocessing scripts, and forecasting results to facilitate standardized empirical evaluation and reproducibility in power grid load forecasting. --- ### Trunc-Opt vine building algorithms Authors: Dániel Pfeifer, Edith Alice Kovács Published: 2025-12-16 Categories: stat.ME, stat.CO, stat.ML arXiv: https://arxiv.org/abs/2512.14399v1 Vine copula models have become highly popular and practical tools for modelling multivariate probability distributions due to their flexibility in modelling different kinds of dependences between the random variables involved. However, their flexibility comes with the drawback of a high-dimensional parameter space. To tackle this problem, truncated vine copulas were introduced by Kurowicka (2010) (Gaussian case) and Brechmann and Czado (2013) (general case). Truncated vine copulas contain conditionally independent pair copulas after the truncation level. So far, in the general case, truncated vine constructing algorithms started from the lowest tree in order to encode the largest dependences in the lower trees. The novelty of this paper starts from the observation that a truncated vine is determined by the first tree after the truncation level (see Kovács and Szántai (2017)). This paper introduces a new score for fitting truncated vines to given data, called the Weight of the truncated vine. Then we propose a completely new methodology for constructing truncated vines. We prove theorems which motivate this new approach. While earlier algorithms did not use conditional independences, we give algorithms for constructing and encoding truncated vines which do exploit them. Finally, we illustrate the algorithms on real datasets and compare the results with well-known methods included in R packages. Our method generally compare favorably to previously known methods. --- ### SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design Authors: Yunjia Yang, Weishao Tang, Mengxin Liu, Nils Thuerey, Yufei Zhang, Haixin Chen Published: 2025-12-16 Categories: cs.LG, physics.flu-dyn arXiv: https://arxiv.org/abs/2512.14397v2 Machine-learning surrogate models have shown promise in accelerating aerodynamic design, yet progress toward generalizable predictors for three-dimensional wings has been limited by the scarcity and restricted diversity of existing datasets. Here, we present SuperWing, a comprehensive open dataset of transonic swept-wing aerodynamics comprising 4,239 parameterized wing geometries and 28,856 Reynolds-averaged Navier-Stokes flow field solutions. The wing shapes in the dataset are generated using a simplified yet expressive geometry parameterization that incorporates spanwise variations in airfoil shape, twist, and dihedral, allowing for an enhanced diversity without relying on perturbations of a baseline wing. All shapes are simulated under a broad range of Mach numbers and angles of attack covering the typical flight envelope. To demonstrate the dataset's utility, we benchmark two state-of-the-art Transformers that accurately predict surface flow and achieve a 2.5 drag-count error on held-out samples. Models pretrained on SuperWing further exhibit strong zero-shot generalization to complex benchmark wings such as DLR-F6 and NASA CRM, underscoring the dataset's diversity and potential for practical usage. --- ### Massive Editing for Large Language Models Based on Dynamic Weight Generation Authors: Wentao Wan, Qiqing Lao, Zhiwei Xie, Hefeng Wu, Runnan Lin, Liang Lin, Keze Wang Published: 2025-12-16 Categories: cs.AI arXiv: https://arxiv.org/abs/2512.14395v4 Knowledge Editing (KE) is a field that studies how to modify some knowledge in Large Language Models (LLMs) at a low cost (compared to pre-training). Currently, performing large-scale edits on LLMs while ensuring the Reliability, Generality, and Locality metrics of the edits remain a challenge. This paper proposes a Massive editing approach for LLMs based on dynamic weight Generation (MeG). Our MeG involves attaching a dynamic weight neuron to specific layers of the LLMs and using a diffusion model to conditionally generate the weights of this neuron based on the input query required for the knowledge. This allows the use of adding a single dynamic weight neuron to achieve the goal of large-scale knowledge editing. Experiments show that our MeG can significantly improve the performance of large-scale KE in terms of Reliability, Generality, and Locality metrics compared to existing knowledge editing methods, particularly with a high percentage point increase in the absolute value index for the Locality metric, demonstrating the advantages of our proposed method. Code is available at https://github.com/RodeWayne/MeG-for-Knowledge-Editing. --- ### RePo: Language Models with Context Re-Positioning Authors: Huayang Li, Tianyu Zhao, Deng Cai, Richard Sproat Published: 2025-12-16 Categories: cs.LG, cs.AI, cs.CL arXiv: https://arxiv.org/abs/2512.14391v3 In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices. The rigid position information poses the full burden of organizing the input structure to attention layers, thus reducing the amount of attention that could be allocated for more critical information. To address this, we propose RePo, a novel mechanism that alleviates the burden for attention layers via context re-positioning. Unlike conventional approaches, RePo utilizes a differentiable module, $f_φ$, to assign token positions that capture contextual dependencies, rather than replying on pre-defined order. By continually pre-training on the OLMo-2 1B \& 7B models, we demonstrate that RePo consistently enhances performance on tasks involving noisy contexts, structured data, and longer context length, while maintaining competitive performance on general short-context tasks. Analysis reveals that RePo successfully allocates more attention mass to distant but relevant information, assigns positions in a dense and non-linear space, and captures the intrinsic structure of the input context. Our code is at https://github.com/SakanaAI/repo. --- ### Black-Box Auditing of Quantum Model: Lifted Differential Privacy with Quantum Canaries Authors: Baobao Song, Shiva Raj Pokhrel, Athanasios V. Vasilakos, Tianqing Zhu, Gang Li Published: 2025-12-16 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.14388v1 Quantum machine learning (QML) promises significant computational advantages, yet models trained on sensitive data risk memorizing individual records, creating serious privacy vulnerabilities. While Quantum Differential Privacy (QDP) mechanisms provide theoretical worst-case guarantees, they critically lack empirical verification tools for deployed models. We introduce the first black-box privacy auditing framework for QML based on Lifted Quantum Differential Privacy, leveraging quantum canaries (strategically offset-encoded quantum states) to detect memorization and precisely quantify privacy leakage during training. Our framework establishes a rigorous mathematical connection between canary offset and trace distance bounds, deriving empirical lower bounds on privacy budget consumption that bridge the critical gap between theoretical guarantees and practical privacy verification. Comprehensive evaluations across both simulated and physical quantum hardware demonstrate our framework's effectiveness in measuring actual privacy loss in QML models, enabling robust privacy verification in QML systems. --- ### EcoScapes: LLM-Powered Advice for Crafting Sustainable Cities Authors: Martin Röhn, Nora Gourmelon, Vincent Christlein Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14373v1 Climate adaptation is vital for the sustainability and sometimes the mere survival of our urban areas. However, small cities often struggle with limited personnel resources and integrating vast amounts of data from multiple sources for a comprehensive analysis. To overcome these challenges, this paper proposes a multi-layered system combining specialized LLMs, satellite imagery analysis and a knowledge base to aid in developing effective climate adaptation strategies. The corresponding code can be found at https://github.com/Photon-GitHub/EcoScapes. --- ### A Comprehensive Safety Metric to Evaluate Perception in Autonomous Systems Authors: Georg Volk, Jörg Gamerdinger, Alexander von Bernuth, Oliver Bringmann Published: 2025-12-16 Categories: cs.RO, cs.CV arXiv: https://arxiv.org/abs/2512.14367v1 Complete perception of the environment and its correct interpretation is crucial for autonomous vehicles. Object perception is the main component of automotive surround sensing. Various metrics already exist for the evaluation of object perception. However, objects can be of different importance depending on their velocity, orientation, distance, size, or the potential damage that could be caused by a collision due to a missed detection. Thus, these additional parameters have to be considered for safety evaluation. We propose a new safety metric that incorporates all these parameters and returns a single easily interpretable safety assessment score for object perception. This new metric is evaluated with both real world and virtual data sets and compared to state of the art metrics. --- ### Optimizing Rank for High-Fidelity Implicit Neural Representations Authors: Julian McGinnis, Florian A. Hölzl, Suprosanna Shit, Florentin Bieder, Paul Friedrich, Mark Mühlau, Bjoern Menze, Daniel Rueckert, Benedikt Wiestler Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14366v3 Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-frequency content. This has directed research efforts towards architectural interventions, such as coordinate embeddings or specialized activation functions, to represent high-frequency signals. In this paper, we challenge the notion that the low-frequency bias of vanilla MLPs is an intrinsic, architectural limitation to learn high-frequency content, but instead a symptom of stable rank degradation during training. We empirically demonstrate that regulating the network's rank during training substantially improves the fidelity of the learned signal, rendering even simple MLP architectures expressive. Extensive experiments show that using optimizers like Muon, with high-rank, near-orthogonal updates, consistently enhances INR architectures even beyond simple ReLU MLPs. These substantial improvements hold across a diverse range of domains, including natural and medical images and novel view synthesis, with up to +9 dB PSNR over the same architecture. Code is available at (https://rank-inrs.github.io). --- ### Unified Semantic Transformer for 3D Scene Understanding Authors: Sebastian Koch, Johanna Wald, Hidenobu Matsuki, Pedro Hermosilla, Timo Ropinski, Federico Tombari Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14364v3 Holistic 3D scene understanding involves capturing and parsing unstructured 3D environments. Due to the inherent complexity of the real world, existing models have predominantly been developed and limited to be task-specific. We introduce UNITE, a Unified Semantic Transformer for 3D scene understanding, a novel feed-forward neural network that unifies a diverse set of 3D dense semantic indoor tasks within a single model. Our model operates on unseen scenes trained in a fully end-to-end manner and only takes a couple seconds to infer the full 3D semantic geometry. Our approach is capable of directly predicting multiple dense semantic attributes, including 3D scene segmentation, instance embeddings, open-vocabulary features, and articulations, solely from RGB images. The method is trained using a combination of 2D distillation, heavily relying on self-supervision and leverages novel multi-view losses designed to ensure 3D view consistency. We demonstrate that UNITE achieves state-of-the-art performance on several different dense indoor semantic tasks and even outperforms task-specific models, in many cases, surpassing methods that operate on ground truth 3D geometry. See the project website at unite-page.github.io --- ### Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis Authors: Nicholas Tagliapietra, Katharina Ensinger, Christoph Zimmer, Osman Mian Published: 2025-12-16 Categories: cs.LG, cs.AI, math.DS arXiv: https://arxiv.org/abs/2512.14361v1 Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time -- leading to poor performance on irregularly sampled data -- or ignore the underlying causality. We propose CaDyT, a novel method for causal discovery on dynamical systems addressing both these challenges. In contrast to state-of-the-art causal discovery methods that model the problem using discrete-time Dynamic Bayesian networks, our formulation is grounded in Difference-based causal models, which allow milder assumptions for modeling the continuous nature of the system. CaDyT leverages exact Gaussian Process inference for modeling the continuous-time dynamics which is more aligned with the underlying dynamical process. We propose a practical instantiation that identifies the causal structure via a greedy search guided by the Algorithmic Markov Condition and Minimum Description Length principle. Our experiments show that CaDyT outperforms state-of-the-art methods on both regularly and irregularly-sampled data, discovering causal networks closer to the true underlying dynamics. --- ### Mimicking Human Visual Development for Learning Robust Image Representations Authors: Ankita Raj, Kaashika Prajaapat, Tapan Kumar Gandhi, Chetan Arora Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14360v1 The human visual system is remarkably adept at adapting to changes in the input distribution; a capability modern convolutional neural networks (CNNs) still struggle to match. Drawing inspiration from the developmental trajectory of human vision, we propose a progressive blurring curriculum to improve the generalization and robustness of CNNs. Human infants are born with poor visual acuity, gradually refining their ability to perceive fine details. Mimicking this process, we begin training CNNs on highly blurred images during the initial epochs and progressively reduce the blur as training advances. This approach encourages the network to prioritize global structures over high-frequency artifacts, improving robustness against distribution shifts and noisy inputs. Challenging prior claims that blurring in the initial training epochs imposes a stimulus deficit and irreversibly harms model performance, we reveal that early-stage blurring enhances generalization with minimal impact on in-domain accuracy. Our experiments demonstrate that the proposed curriculum reduces mean corruption error (mCE) by up to 8.30% on CIFAR-10-C and 4.43% on ImageNet-100-C datasets, compared to standard training without blurring. Unlike static blur-based augmentation, which applies blurred images randomly throughout training, our method follows a structured progression, yielding consistent gains across various datasets. Furthermore, our approach complements other augmentation techniques, such as CutMix and MixUp, and enhances both natural and adversarial robustness against common attack methods. Code is available at https://github.com/rajankita/Visual_Acuity_Curriculum. --- ### TiCard: Deployable EXPLAIN-only Residual Learning for Cardinality Estimation Authors: Qizhi Wang Published: 2025-12-16 Categories: cs.AI, cs.DB arXiv: https://arxiv.org/abs/2512.14358v2 Cardinality estimation is a key bottleneck for cost-based query optimization, yet deployable improvements remain difficult: classical estimators miss correlations, while learned estimators often require workload-specific training pipelines and invasive integration into the optimizer. This paper presents TiCard, a low intrusion, correction-based framework that augments (rather than replaces) a database's native estimator. TiCard learns multiplicative residual corrections using EXPLAIN-only features, and uses EXPLAIN ANALYZE only for offline labels. We study two practical instantiations: (i) a Gradient Boosting Regressor for sub-millisecond inference, and (ii) TabPFN, an in-context tabular foundation model that adapts by refreshing a small reference set without gradient retraining. On TiDB with TPCH and the Join Order Benchmark, in a low-trace setting (263 executions total; 157 used for learning), TiCard improves operator-level tail accuracy substantially: P90 Q-error drops from 312.85 (native) to 13.69 (TiCard-GBR), and P99 drops from 37,974.37 to 3,416.50 (TiCard-TabPFN), while a join-only policy preserves near-perfect median behavior. We position TiCard as an AI4DB building block focused on deployability: explicit scope, conservative integration policies, and an integration roadmap from offline correction to in-optimizer use. --- ### Enhancing Interpretability for Vision Models via Shapley Value Optimization Authors: Kanglong Fan, Yunqiao Yang, Chen Ma Published: 2025-12-16 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2512.14354v1 Deep neural networks have demonstrated remarkable performance across various domains, yet their decision-making processes remain opaque. Although many explanation methods are dedicated to bringing the obscurity of DNNs to light, they exhibit significant limitations: post-hoc explanation methods often struggle to faithfully reflect model behaviors, while self-explaining neural networks sacrifice performance and compatibility due to their specialized architectural designs. To address these challenges, we propose a novel self-explaining framework that integrates Shapley value estimation as an auxiliary task during training, which achieves two key advancements: 1) a fair allocation of the model prediction scores to image patches, ensuring explanations inherently align with the model's decision logic, and 2) enhanced interpretability with minor structural modifications, preserving model performance and compatibility. Extensive experiments on multiple benchmarks demonstrate that our method achieves state-of-the-art interpretability. --- ### HGS: Hybrid Gaussian Splatting with Static-Dynamic Decomposition for Compact Dynamic View Synthesis Authors: Kaizhe Zhang, Yijie Zhou, Weizhan Zhang, Caixia Yan, Haipeng Du, yugui xie, Yu-Hui Wen, Yong-Jin Liu Published: 2025-12-16 Categories: cs.CV, cs.CG arXiv: https://arxiv.org/abs/2512.14352v1 Dynamic novel view synthesis (NVS) is essential for creating immersive experiences. Existing approaches have advanced dynamic NVS by introducing 3D Gaussian Splatting (3DGS) with implicit deformation fields or indiscriminately assigned time-varying parameters, surpassing NeRF-based methods. However, due to excessive model complexity and parameter redundancy, they incur large model sizes and slow rendering speeds, making them inefficient for real-time applications, particularly on resource-constrained devices. To obtain a more efficient model with fewer redundant parameters, in this paper, we propose Hybrid Gaussian Splatting (HGS), a compact and efficient framework explicitly designed to disentangle static and dynamic regions of a scene within a unified representation. The core innovation of HGS lies in our Static-Dynamic Decomposition (SDD) strategy, which leverages Radial Basis Function (RBF) modeling for Gaussian primitives. Specifically, for dynamic regions, we employ time-dependent RBFs to effectively capture temporal variations and handle abrupt scene changes, while for static regions, we reduce redundancy by sharing temporally invariant parameters. Additionally, we introduce a two-stage training strategy tailored for explicit models to enhance temporal coherence at static-dynamic boundaries. Experimental results demonstrate that our method reduces model size by up to 98% and achieves real-time rendering at up to 125 FPS at 4K resolution on a single RTX 3090 GPU. It further sustains 160 FPS at 1352 * 1014 on an RTX 3050 and has been integrated into the VR system. Moreover, HGS achieves comparable rendering quality to state-of-the-art methods while providing significantly improved visual fidelity for high-frequency details and abrupt scene changes. --- ### Towards Transferable Defense Against Malicious Image Edits Authors: Jie Zhang, Shuai Dong, Shiguang Shan, Xilin Chen Published: 2025-12-16 Categories: cs.CV, cs.AI, cs.CY, cs.LG arXiv: https://arxiv.org/abs/2512.14341v2 Recent approaches employing imperceptible perturbations in input images have demonstrated promising potential to counter malicious manipulations in diffusion-based image editing systems. However, existing methods suffer from limited transferability in cross-model evaluations. To address this, we propose Transferable Defense Against Malicious Image Edits (TDAE), a novel bimodal framework that enhances image immunity against malicious edits through coordinated image-text optimization. Specifically, at the visual defense level, we introduce FlatGrad Defense Mechanism (FDM), which incorporates gradient regularization into the adversarial objective. By explicitly steering the perturbations toward flat minima, FDM amplifies immune robustness against unseen editing models. For textual enhancement protection, we propose an adversarial optimization paradigm named Dynamic Prompt Defense (DPD), which periodically refines text embeddings to align the editing outcomes of immunized images with those of the original images, then updates the images under optimized embeddings. Through iterative adversarial updates to diverse embeddings, DPD enforces the generation of immunized images that seek a broader set of immunity-enhancing features, thereby achieving cross-model transferability. Extensive experimental results demonstrate that our TDAE achieves state-of-the-art performance in mitigating malicious edits under both intra- and cross-model evaluations. --- ### Implicit Bias and Invariance: How Hopfield Networks Efficiently Learn Graph Orbits Authors: Michael Murray, Tenzin Chan, Kedar Karhadker, Christopher J. Hillar Published: 2025-12-16 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.14338v5 Many learning problems are organized by group symmetries. While invariance is often imposed through architectures or group averaging, we ask when it can emerge from training on a finite random subset of an orbit. We study this question in classical Hopfield networks, where strict memorization can be expressed as a linear margin problem. Reparameterizing minimization of energy flow (MEF) as an exponential loss connects gradient descent to the corresponding minimum-norm hard-margin memorizer. Our main result shows that, for independent uniform samples from any finite permutation orbit, the exact sample hard-margin support vector machine (HSVM) concentrates exponentially around the invariant full-orbit HSVM. Consequently, an orbit-size-independent polynomial number of samples suffices both for approximate parameter invariance and for simultaneous memorization of every orbit element; directional convergence transfers this conclusion asymptotically to MEF gradient descent. For graph-isomorphism orbits, we characterize the invariant parameters as a three-dimensional subspace and show that every such orbit is memorizable. For cliques of fixed linear density, additional symmetry sharpens the uniform memorization bound to $O(v^4\log(1/δ))$, exponentially smaller than the orbit size. Together with experiments across several learning rules, these results give a finite-sample account of how optimization bias can recover symmetry from partial group-structured data. --- ### Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure Authors: Jooyeol Yun, Jaegul Choo Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14336v1 Scalable Vector Graphics (SVG) are central to modern web design, and the demand to animate them continues to grow as web environments become increasingly dynamic. Yet automating the animation of vector graphics remains challenging for vision-language models (VLMs) despite recent progress in code generation and motion planning. VLMs routinely mis-handle SVGs, since visually coherent parts are often fragmented into low-level shapes that offer little guidance of which elements should move together. In this paper, we introduce a framework that recovers the semantic structure required for reliable SVG animation and reveals the missing layer that current VLM systems overlook. This is achieved through a statistical aggregation of multiple weak part predictions, allowing the system to stably infer semantics from noisy predictions. By reorganizing SVGs into semantic groups, our approach enables VLMs to produce animations with far greater coherence. Our experiments demonstrate substantial gains over existing approaches, suggesting that semantic recovery is the key step that unlocks robust SVG animation and supports more interpretable interactions between VLMs and vector graphics. --- ### Dual Attention Guided Defense Against Malicious Edits Authors: Jie Zhang, Shuai Dong, Shiguang Shan, Xilin Chen Published: 2025-12-16 Categories: cs.CV, cs.AI, cs.CY, cs.LG arXiv: https://arxiv.org/abs/2512.14333v1 Recent progress in text-to-image diffusion models has transformed image editing via text prompts, yet this also introduces significant ethical challenges from potential misuse in creating deceptive or harmful content. While current defenses seek to mitigate this risk by embedding imperceptible perturbations, their effectiveness is limited against malicious tampering. To address this issue, we propose a Dual Attention-Guided Noise Perturbation (DANP) immunization method that adds imperceptible perturbations to disrupt the model's semantic understanding and generation process. DANP functions over multiple timesteps to manipulate both cross-attention maps and the noise prediction process, using a dynamic threshold to generate masks that identify text-relevant and irrelevant regions. It then reduces attention in relevant areas while increasing it in irrelevant ones, thereby misguides the edit towards incorrect regions and preserves the intended targets. Additionally, our method maximizes the discrepancy between the injected noise and the model's predicted noise to further interfere with the generation. By targeting both attention and noise prediction mechanisms, DANP exhibits impressive immunity against malicious edits, and extensive experiments confirm that our method achieves state-of-the-art performance. --- ### Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring Authors: Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, John D. Kelleher Published: 2025-12-16 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2512.14332v2 The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately. However, a growing body of studies show that LRMs are still inefficient, over-generating verification and reflection steps. To address this challenge, we introduce the Step-Tagging framework, a lightweight sentence-classifier enabling real-time annotation of the type of reasoning steps that an LRM is generating. To monitor reasoning behaviors, we introduced ReasonType: a novel taxonomy of reasoning steps. Building on this framework, we demonstrated that online monitoring of the count of specific steps can produce effective interpretable early stopping criteria of LRM inferences. We evaluate the Step-tagging framework on three open-source reasoning models across standard benchmark datasets: MATH500, GSM8K, AIME and non-mathematical tasks (GPQA and MMLU-Pro). We achieve 20 to 50% token reduction while maintaining comparable accuracy to standard generation, with largest gains observed on more computation-heavy tasks. This work offers a novel way to increase control over the generation of LRMs, and a new tool to study behaviors of LRMs. --- ### Criminal Liability in AI-Enabled Autonomous Vehicles: A Comparative Study Authors: Sahibpreet Singh, Manjit Singh Published: 2025-12-16 Categories: cs.CY, cs.AI, cs.CR arXiv: https://arxiv.org/abs/2512.14330v1 AI revolutionizes transportation through autonomous vehicles (AVs) but introduces complex criminal liability issues regarding infractions. This study employs a comparative legal analysis of primary statutes, real-world liability claims, and academic literature across the US, Germany, UK, China, and India; jurisdictions selected for their technological advancement and contrasting regulatory approaches. The research examines the attribution of human error, AI moral agency, and the identification of primary offenders in AV incidents. Findings reveal fragmented regulatory landscapes: India and the US rely on loose networks of state laws, whereas the UK enacted the pioneering Automated and Electric Vehicles Act 2018. Germany enforces strict safety standards, distinguishing liability based on the vehicle's operating mode, while China similarly aims for a stringent liability regime. The study concludes that globally harmonized legal standards are essential to foster technological innovation while ensuring minimum risk and clear liability attribution. --- ### Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion Authors: Yanning Dai, Chenyu Tang, Ruizhi Zhang, Wenyu Yang, Yilan Zhang, Yuhui Wang, Junliang Chen, Xuhang Chen, Ruimou Xie, Yangyue Cao, Qiaoying Li, Jin Cao et al. Published: 2025-12-16 Categories: cs.CE, cs.AI arXiv: https://arxiv.org/abs/2512.14329v2 Dynamic prediction of locomotor capacity after stroke could enable more individualized rehabilitation, yet current assessments largely provide static impairment scores and do not indicate whether patients can perform specific tasks such as slope walking or stair climbing. Here, we present a wearable-informed data-physics hybrid generative framework that reconstructs a stroke survivor's locomotor control from wearable inertial sensing and predicts task-conditioned post-stroke locomotion in new environments. From a single 20 m level-ground walking trial recorded by five IMUs, the framework personalizes a physics-based digital avatar using a healthy-motion prior and hybrid imitation learning, generating dynamically feasible, patient-specific movements for inclined walking and stair negotiation. Across 11 stroke inpatients, predicted postures reached 82.2% similarity for slopes and 69.9% for stairs, substantially exceeding a physics-only baseline. In a multicentre pilot randomized study (n = 21; 28 days), access to scenario-specific locomotion predictions to support task selection and difficulty titration was associated with larger gains in Fugl-Meyer lower-extremity scores than standard care (mean change 6.0 vs 3.7 points; $p < 0.05$). These results suggest that wearable-informed generative digital avatars may augment individualized gait rehabilitation planning and provide a pathway toward dynamically personalized post-stroke motor recovery strategies. --- ### Semantic Mismatch and Perceptual Degradation: A New Perspective on Image Editing Immunity Authors: Shuai Dong, Jie Zhang, Guoying Zhao, Shiguang Shan, Xilin Chen Published: 2025-12-16 Categories: cs.CV, cs.AI, cs.CY, cs.LG arXiv: https://arxiv.org/abs/2512.14320v1 Text-guided image editing via diffusion models, while powerful, raises significant concerns about misuse, motivating efforts to immunize images against unauthorized edits using imperceptible perturbations. Prevailing metrics for evaluating immunization success typically rely on measuring the visual dissimilarity between the output generated from a protected image and a reference output generated from the unprotected original. This approach fundamentally overlooks the core requirement of image immunization, which is to disrupt semantic alignment with attacker intent, regardless of deviation from any specific output. We argue that immunization success should instead be defined by the edited output either semantically mismatching the prompt or suffering substantial perceptual degradations, both of which thwart malicious intent. To operationalize this principle, we propose Synergistic Intermediate Feature Manipulation (SIFM), a method that strategically perturbs intermediate diffusion features through dual synergistic objectives: (1) maximizing feature divergence from the original edit trajectory to disrupt semantic alignment with the expected edit, and (2) minimizing feature norms to induce perceptual degradations. Furthermore, we introduce the Immunization Success Rate (ISR), a novel metric designed to rigorously quantify true immunization efficacy for the first time. ISR quantifies the proportion of edits where immunization induces either semantic failure relative to the prompt or significant perceptual degradations, assessed via Multimodal Large Language Models (MLLMs). Extensive experiments show our SIFM achieves the state-of-the-art performance for safeguarding visual content against malicious diffusion-based manipulation. --- ### From YOLO to VLMs: Advancing Zero-Shot and Few-Shot Detection of Wastewater Treatment Plants Using Satellite Imagery in MENA Region Authors: Akila Premarathna, Kanishka Hewageegana, Garcia Andarcia Mariangel Published: 2025-12-16 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2512.14312v1 In regions of the Middle East and North Africa (MENA), there is a high demand for wastewater treatment plants (WWTPs), crucial for sustainable water management. Precise identification of WWTPs from satellite images enables environmental monitoring. Traditional methods like YOLOv8 segmentation require extensive manual labeling. But studies indicate that vision-language models (VLMs) are an efficient alternative to achieving equivalent or superior results through inherent reasoning and annotation. This study presents a structured methodology for VLM comparison, divided into zero-shot and few-shot streams specifically to identify WWTPs. The YOLOv8 was trained on a governmental dataset of 83,566 high-resolution satellite images from Egypt, Saudi Arabia, and UAE: ~85% WWTPs (positives), 15% non-WWTPs (negatives). Evaluated VLMs include LLaMA 3.2 Vision, Qwen 2.5 VL, DeepSeek-VL2, Gemma 3, Gemini, and Pixtral 12B (Mistral), used to identify WWTP components such as circular/rectangular tanks, aeration basins and distinguish confounders via expert prompts producing JSON outputs with confidence and descriptions. The dataset comprises 1,207 validated WWTP locations (198 UAE, 354 KSA, 655 Egypt) and equal non-WWTP sites from field/AI data, as 600mx600m Geo-TIFF images (Zoom 18, EPSG:4326). Zero-shot evaluations on WWTP images showed several VLMs out-performing YOLOv8's true positive rate, with Gemma-3 highest. Results confirm that VLMs, particularly with zero-shot, can replace YOLOv8 for efficient, annotation-free WWTP classification, enabling scalable remote sensing. --- ### Continual Learning at the Edge: An Agnostic IIoT Architecture Authors: Pablo García-Santaclara, Bruno Fernández-Castro, Rebeca P. Díaz-Redondo, Carlos Calvo-Moa, Henar Mariño-Bodelón Published: 2025-12-16 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2512.14311v1 The exponential growth of Internet-connected devices has presented challenges to traditional centralized computing systems due to latency and bandwidth limitations. Edge computing has evolved to address these difficulties by bringing computations closer to the data source. Additionally, traditional machine learning algorithms are not suitable for edge-computing systems, where data usually arrives in a dynamic and continual way. However, incremental learning offers a good solution for these settings. We introduce a new approach that applies the incremental learning philosophy within an edge-computing scenario for the industrial sector with a specific purpose: real time quality control in a manufacturing system. Applying continual learning we reduce the impact of catastrophic forgetting and provide an efficient and effective solution. --- ### PSMamba: Progressive Self-supervised Vision Mamba for Plant Disease Recognition Authors: Abdullah Al Mamun, Miaohua Zhang, David Ahmedt-Aristizabal, Zeeshan Hayder, Mohammad Awrangjeb Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14309v1 Self-supervised Learning (SSL) has become a powerful paradigm for representation learning without manual annotations. However, most existing frameworks focus on global alignment and struggle to capture the hierarchical, multi-scale lesion patterns characteristic of plant disease imagery. To address this gap, we propose PSMamba, a progressive self-supervised framework that integrates the efficient sequence modelling of Vision Mamba (VM) with a dual-student hierarchical distillation strategy. Unlike conventional single teacher-student designs, PSMamba employs a shared global teacher and two specialised students: one processes mid-scale views to capture lesion distributions and vein structures, while the other focuses on local views to capture fine-grained cues such as texture irregularities and early-stage lesions. This multi-granular supervision facilitates the joint learning of contextual and detailed representations, with consistency losses ensuring coherent cross-scale alignment. Experiments on three benchmark datasets show that PSMamba consistently outperforms state-of-the-art SSL methods, delivering superior accuracy and robustness in both domain-shifted and fine-grained scenarios. --- ### Improving the Accuracy of Amortized Model Comparison with Self-Consistency Authors: Šimon Kucharský, Aayush Mishra, Daniel Habermann, Stefan T. Radev, Paul-Christian Bürkner Published: 2025-12-16 Categories: stat.ML, cs.LG, stat.CO arXiv: https://arxiv.org/abs/2512.14308v3 Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However, ABI methods are highly sensitive to model misspecification: when observed data fall outside the training distribution (generative scope of the statistical models), neural surrogates can behave unpredictably. This makes it a challenge in a model comparison setting, where multiple statistical models are considered, of which at least some are misspecified. Recent work on self-consistency (SC) provides a promising remedy to this issue, accessible even for empirical data (without ground-truth labels). In this work, we investigate how SC can improve amortized model comparison conceptualized in four different ways. Across two synthetic and two real-world case studies, we find that approaches for model comparison that estimate marginal likelihoods through approximate parameter posteriors consistently outperform methods that directly approximate model evidence or posterior model probabilities. SC training improves robustness when the likelihood is available, even under severe model misspecification. The benefits of SC for methods without access of analytic likelihoods are more limited and inconsistent. Our results suggest practical guidance for reliable amortized Bayesian model comparison: prefer parameter posterior-based methods and augment them with SC training on empirical datasets to mitigate extrapolation bias under model misspecification. --- ### Inflation Attitudes of Large Language Models Authors: Nikoleta Anesti, Edward Hill, Andreas Joseph Published: 2025-12-16 Categories: cs.CL, econ.EM arXiv: https://arxiv.org/abs/2512.14306v1 This paper investigates the ability of Large Language Models (LLMs), specifically GPT-3.5-turbo (GPT), to form inflation perceptions and expectations based on macroeconomic price signals. We compare the LLM's output to household survey data and official statistics, mimicking the information set and demographic characteristics of the Bank of England's Inflation Attitudes Survey (IAS). Our quasi-experimental design exploits the timing of GPT's training cut-off in September 2021 which means it has no knowledge of the subsequent UK inflation surge. We find that GPT tracks aggregate survey projections and official statistics at short horizons. At a disaggregated level, GPT replicates key empirical regularities of households' inflation perceptions, particularly for income, housing tenure, and social class. A novel Shapley value decomposition of LLM outputs suited for the synthetic survey setting provides well-defined insights into the drivers of model outputs linked to prompt content. We find that GPT demonstrates a heightened sensitivity to food inflation information similar to that of human respondents. However, we also find that it lacks a consistent model of consumer price inflation. More generally, our approach could be used to evaluate the behaviour of LLMs for use in the social sciences, to compare different models, or to assist in survey design. --- ### A Threshold-Triggered Deep Q-Network-Based Framework for Self-Healing in Autonomic Software-Defined IIoT-Edge Networks Authors: Agrippina Mwangi, León Navarro-Hilfiker, Lukasz Brewka, Mikkel Gryning, Elena Fumagalli, Madeleine Gibescu Published: 2025-12-16 Categories: cs.NI, cs.AI, cs.ET, cs.PF, hep-ex arXiv: https://arxiv.org/abs/2512.14297v1 Stochastic disruptions such as flash events arising from benign traffic bursts and switch thermal fluctuations are major contributors to intermittent service degradation in software-defined industrial networks. These events violate IEC~61850-derived quality-of-service requirements and user-defined service-level agreements, hindering the reliable and timely delivery of control, monitoring, and best-effort traffic in IEC~61400-25-compliant wind power plants. Failure to maintain these requirements often results in delayed or lost control signals, reduced operational efficiency, and increased risk of wind turbine generator downtime. To address these challenges, this study proposes a threshold-triggered Deep Q-Network self-healing agent that autonomically detects, analyzes, and mitigates network disruptions while adapting routing behavior and resource allocation in real time. The proposed agent was trained, validated, and tested on an emulated tri-clustered switch network deployed in a cloud-based proof-of-concept testbed. Simulation results show that the proposed agent improves disruption recovery performance by 53.84% compared to a baseline shortest-path and load-balanced routing approach and outperforms state-of-the-art methods, including the Adaptive Network-based Fuzzy Inference System by 13.1% and the Deep Q-Network and traffic prediction-based routing optimization method by 21.5%, in a super-spine leaf data-plane architecture. Additionally, the agent maintains switch thermal stability by proactively initiating external rack cooling when required. These findings highlight the potential of deep reinforcement learning in building resilience in software-defined industrial networks deployed in mission-critical, time-sensitive application scenarios. --- ### Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting Authors: Georgios Bouchouras, Dimitrios Doumanas, Andreas Soularidis, Konstantinos Kotis, George A. Vouros Published: 2025-12-16 Categories: cs.AI arXiv: https://arxiv.org/abs/2512.14288v1 This paper explores the integration of Large Language Models (LLMs) in the engineering of a Parkinson's Disease (PD) monitoring and alerting ontology through four key methodologies: One Shot (OS) prompt techniques, Chain of Thought (CoT) prompts, X-HCOME, and SimX-HCOME+. The primary objective is to determine whether LLMs alone can create comprehensive ontologies and, if not, whether human-LLM collaboration can achieve this goal. Consequently, the paper assesses the effectiveness of LLMs in automated ontology development and the enhancement achieved through human-LLM collaboration. Initial ontology generation was performed using One Shot (OS) and Chain of Thought (CoT) prompts, demonstrating the capability of LLMs to autonomously construct ontologies for PD monitoring and alerting. However, these outputs were not comprehensive and required substantial human refinement to enhance their completeness and accuracy. X-HCOME, a hybrid ontology engineering approach that combines human expertise with LLM capabilities, showed significant improvements in ontology comprehensiveness. This methodology resulted in ontologies that are very similar to those constructed by experts. Further experimentation with SimX-HCOME+, another hybrid methodology emphasizing continuous human supervision and iterative refinement, highlighted the importance of ongoing human involvement. This approach led to the creation of more comprehensive and accurate ontologies. Overall, the paper underscores the potential of human-LLM collaboration in advancing ontology engineering, particularly in complex domains like PD. The results suggest promising directions for future research, including the development of specialized GPT models for ontology construction. --- ### SS4D: Native 4D Generative Model via Structured Spacetime Latents Authors: Zhibing Li, Mengchen Zhang, Tong Wu, Jing Tan, Jiaqi Wang, Dahua Lin Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14284v1 We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimizing over 3D or video generative models, we train a generator directly on 4D data, achieving high fidelity, temporal coherence, and structural consistency. At the core of our method is a compressed set of structured spacetime latents. Specifically, (1) To address the scarcity of 4D training data, we build on a pre-trained single-image-to-3D model, preserving strong spatial consistency. (2) Temporal consistency is enforced by introducing dedicated temporal layers that reason across frames. (3) To support efficient training and inference over long video sequences, we compress the latent sequence along the temporal axis using factorized 4D convolutions and temporal downsampling blocks. In addition, we employ a carefully designed training strategy to enhance robustness against occlusion --- ### The Trust in AI-Generated Health Advice (TAIGHA) Scale and Short Version (TAIGHA-S): Development and Validation Study Authors: Marvin Kopka, Azeem Majeed, Gabriella Spinelli, Austen El-Osta, Markus Feufel Published: 2025-12-16 Categories: cs.HC, cs.AI arXiv: https://arxiv.org/abs/2512.14278v1 Artificial Intelligence tools such as large language models are increasingly used by the public to obtain health information and guidance. In health-related contexts, following or rejecting AI-generated advice can have direct clinical implications. Existing instruments like the Trust in Automated Systems Survey assess trustworthiness of generic technology, and no validated instrument measures users' trust in AI-generated health advice specifically. This study developed and validated the Trust in AI-Generated Health Advice (TAIGHA) scale and its four-item short form (TAIGHA-S) as theory-based instruments measuring trust and distrust, each with cognitive and affective components. The items were developed using a generative AI approach, followed by content validation with 10 domain experts, face validation with 30 lay participants, and psychometric validation with 385 UK participants who received AI-generated advice in a symptom-assessment scenario. After automated item reduction, 28 items were retained and reduced to 10 based on expert ratings. TAIGHA showed excellent content validity (S-CVI/Ave=0.99) and CFA confirmed a two-factor model with excellent fit (CFI=0.98, TLI=0.98, RMSEA=0.07, SRMR=0.03). Internal consistency was high (α=0.95). Convergent validity was supported by correlations with the Trust in Automated Systems Survey (r=0.67/-0.66) and users' reliance on the AI's advice (r=0.37 for trust), while divergent validity was supported by low correlations with reading flow and mental load (all |r|<0.25). TAIGHA-S correlated highly with the full scale (r=0.96) and showed good reliability (α=0.88). TAIGHA and TAIGHA-S are validated instruments for assessing user trust and distrust in AI-generated health advice. Reporting trust and distrust separately permits a more complete evaluation of AI interventions, and the short scale is well-suited for time-constrained settings. --- ### SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questions Authors: Panayiotis Smeros, Vincent Emonet, Ruijie Wang, Ana-Claudia Sima, Tarcisio Mendes de Farias Published: 2025-12-16 Categories: cs.IR, cs.AI, cs.CL arXiv: https://arxiv.org/abs/2512.14277v2 The advent of large language models is contributing to the emergence of novel approaches that promise to better tackle the challenge of generating structured queries, such as SPARQL queries, from natural language. However, these new approaches mostly focus on response accuracy while ignoring other evaluation criteria, such as runtime and cost to generate SPARQL queries. Consequently, they are often not production-ready or easy to deploy over real-world knowledge graphs with good accuracy. To mitigate these issues, in this paper, we describe and systematically evaluate SPARQL-LLM, an open-source and triplestore-agnostic approach, powered by lightweight metadata, that generates SPARQL queries from natural language text. First, we describe its architecture, which consists of dedicated components for metadata indexing, prompt building, and query generation and execution. Then, we evaluate it based on a state-of-the-art challenge with multilingual questions, and a collection of questions from three of the most prevalent knowledge graphs within the field of bioinformatics. Our results demonstrate a substantial improvement of up to 59% in F1 score over the second-best system participating in the challenge, adaptability to high-resource languages such as English, Spanish, and German, as well as ability to form complex bioinformatics queries. Furthermore, our results show that our system is up to 27x faster than the second-best system participating in the challenge, while costing a maximum of $0.01 per question, making it suitable for real-time, low-cost text-to-SPARQL applications. SPARQL-LLM is publicly released as an open-source project at https://github.com/sib-swiss/sparql-llm and is currently deployed over real-world decentralized knowledge graphs at https://www.expasy.org/chat. --- ### TUN: Detecting Significant Points in Persistence Diagrams with Deep Learning Authors: Yu Chen, Hongwei Lin Published: 2025-12-16 Categories: cs.CV, cs.LG, math.AT arXiv: https://arxiv.org/abs/2512.14274v2 Persistence diagrams (PDs) provide a powerful tool for understanding the topology of the underlying shape of a point cloud. However, identifying which points in PDs encode genuine signals remains challenging. This challenge directly hinders the practical adoption of topological data analysis in many applications, where automated and reliable interpretation of persistence diagrams is essential for downstream decision-making. In this paper, we study automatic significance detection for one-dimensional persistence diagrams. Specifically, we propose Topology Understanding Net (TUN), a multi-modal network that combines enhanced PD descriptors with self-attention, a PointNet-style point cloud encoder, learned fusion, and per-point classification, alongside stable preprocessing and imbalance-aware training. It provides an automated and effective solution for identifying significant points in PDs, which are critical for downstream applications. Experiments show that TUN outperforms classic methods in detecting significant points in PDs, illustrating its effectiveness in real-world applications. --- ### Zoom-Zero: Reinforced Coarse-to-Fine Video Understanding via Temporal Zoom-in Authors: Xiaoqian Shen, Min-Hung Chen, Yu-Chiang Frank Wang, Mohamed Elhoseiny, Ryo Hachiuma Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14273v1 Grounded video question answering (GVQA) aims to localize relevant temporal segments in videos and generate accurate answers to a given question; however, large video-language models (LVLMs) exhibit limited temporal awareness. Although existing approaches based on Group Relative Policy Optimization (GRPO) attempt to improve temporal grounding, they still struggle to faithfully ground their answers in the relevant video evidence, leading to temporal mislocalization and hallucinations. In this work, we present Zoom-Zero, a coarse-to-fine framework that first localizes query-relevant segments and then temporally zooms into the most salient frames for finer-grained visual verification. Our method addresses the limits of GRPO for the GVQA task with two key innovations: (i) a zoom-in accuracy reward that validates the fidelity of temporal grounding prediction and facilitates fine-grained visual verification on grounded frames; (ii) token-selective credit assignment, which attributes rewards to the tokens responsible for temporal localization or answer generation, mitigating GRPO's issue in handling multi-faceted reward signals. Our proposed method advances grounded video question answering, improving temporal grounding by 5.2\% on NExT-GQA and 4.6\% on ReXTime, while also enhancing average answer accuracy by 2.4\%. Additionally, the coarse-to-fine zoom-in during inference further benefits long-form video understanding by preserving critical visual details without compromising global context, yielding an average improvement of 6.4\% on long-video benchmarks. --- ### A variational Bayes latent class approach for EHR-based patient phenotyping in R Authors: Brian Buckley, Adrian O'Hagan, Marie Galligan Published: 2025-12-16 Categories: stat.CO, stat.ML arXiv: https://arxiv.org/abs/2512.14272v1 The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm using closed-form coordinate ascent variational inference (CAVI) to determine the patient phenotype latent class. We then implement a variational Bayes logistic regression, where we determine the probability of the phenotype in the supplied EHR cohort, the shift in biomarkers for patients with the phenotype of interest versus a healthy population and evaluate predictive performance of binary indicator clinical codes and medication codes. The logistic model likelihood applies the latent class from the GMM step to inform the conditional. --- ### DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance Authors: Shreedhar Govil, Didier Stricker, Jason Rambach Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14266v3 Predicting driver attention is a critical problem for developing explainable autonomous driving systems and understanding driver behavior in mixed human-autonomous vehicle traffic scenarios. Although significant progress has been made through large-scale driver attention datasets and deep learning architectures, existing works are constrained by narrow frontal field-of-view and limited driving diversity. Consequently, they fail to capture the full spatial context of driving environments, especially during lane changes, turns, and interactions involving peripheral objects such as pedestrians or cyclists. In this paper, we introduce DriverGaze360, a large-scale 360$^\circ$ field of view driver attention dataset, containing $\sim$1 million gaze-labeled frames collected from 19 human drivers, enabling comprehensive omnidirectional modeling of driver gaze behavior. Moreover, our panoramic attention prediction approach, DriverGaze360-Net, jointly learns attention maps and attended objects by employing an auxiliary semantic segmentation head. This improves spatial awareness and attention prediction across wide panoramic inputs. Extensive experiments demonstrate that DriverGaze360-Net achieves state-of-the-art attention prediction performance on multiple metrics on panoramic driving images. Dataset and method available at https://dfki-av.github.io/drivergaze360. --- ### DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization Authors: Nick Leenders, Thomas Quadt, Boris Cule, Roy Lindelauf, Herman Monsuur, Joost van Oijen, Mark Voskuijl Published: 2025-12-16 Categories: cs.LG, cs.AI, math.OC arXiv: https://arxiv.org/abs/2512.14263v2 Preferential Bayesian Optimization (PBO) aims to find a decision-maker's most preferred solution in as few pairwise comparisons as possible. Existing approaches rely on Gaussian Process (GP) surrogates, which provide strong performance but limited interpretability. This limits real-world usability in high-stakes domains, such as healthcare, where interpretability and trust are essential. We propose DT-PBO, a novel tree-based surrogate model for PBO that is inherently interpretable while capturing preference uncertainty. Specifically, we introduce a novel splitting heuristic that constructs interpretable shallow decision trees directly from pairwise comparison data, and use Laplace approximation to obtain probabilistic estimates for each leaf. This enables efficient preference modeling without sacrificing interpretability. Across eight benchmark functions, our method achieves competitive convergence to GP-based PBO, particularly on functions with rugged optimization landscapes. Additional experiments show robustness against noise and a fast computational running time. Experiments on real-world datasets further demonstrate that our model provides interpretable insights into decision-maker preferences that would remain opaque under GP-based approaches. --- ### Enhancing Visual Programming for Visual Reasoning via Probabilistic Graphs Authors: Wentao Wan, Kaiyu Wu, Qingyang Ma, Nan Kang, Yunjie Chen, Liang Lin, Keze Wang Published: 2025-12-16 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.14257v1 Recently, Visual Programming (VP) based on large language models (LLMs) has rapidly developed and demonstrated significant potential in complex Visual Reasoning (VR) tasks. Previous works to enhance VP have primarily focused on improving the quality of LLM-generated visual programs. However, they have neglected to optimize the VP-invoked pre-trained models, which serve as modules for the visual sub-tasks decomposed from the targeted tasks by VP. The difficulty is that there are only final labels of targeted VR tasks rather than labels of sub-tasks. Besides, the non-differentiable nature of VP impedes the direct use of efficient gradient-based optimization methods to leverage final labels for end-to-end learning of the entire VP framework. To overcome these issues, we propose EVPG, a method to Enhance Visual Programming for visual reasoning via Probabilistic Graphs. Specifically, we creatively build a directed probabilistic graph according to the variable dependency relationships during the VP executing process, which reconstructs the non-differentiable VP executing process into a differentiable exact probability inference process on this directed probabilistic graph. As a result, this enables the VP framework to utilize the final labels for efficient, gradient-based optimization in end-to-end supervised learning on targeted VR tasks. Extensive and comprehensive experiments demonstrate the effectiveness and advantages of our EVPG, showing significant performance improvements for VP on three classical complex VR tasks: GQA, NLVRv2, and Open Images. ---