RNN
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DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns
2026-03-17 09:46:11Trung V. Phan, Tri Gia Nguyen, Thomas BauschertAbstract
This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive, stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environment is modeled as a partially observable Markov decision process (POMDP), where host provenance and network telemetry are fused into unified provenance graphs. Building on our prior work, StageFinder, a graph neural encoder and an LSTM-based stage estimator infer probabilistic attacker stages aligned with the MITRE ATT&CK framework. These stage beliefs, combined with graph embeddings, guide a hierarchical Proximal Policy Optimization (PPO) agent that selects defense actions across monitoring, access control, containment, and remediation. Evaluated in a realistic enterprise testbed using CALDERA-driven APT playbooks, DeepStage achieves a stage-weighted F1-score of 0.89, outperforming a risk-aware DRL baseline by 21.9%. The results demonstrate effective stage-aware and cost-efficient autonomous cyber defense.
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URL
https://arxiv.org/abs/2603.16969
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Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks
2026-03-16 18:41:21Samira Abedini, Sina Mavali, Lea Sch\"onherr, Martin Pawelczyk, Rebekka BurkholzAbstract
Large Language Model (LLM)-based Multi-Agent Systems (MASs) are increasingly deployed for agentic tasks, such as web automation, itinerary planning, and collaborative problem solving. Yet, their interactive nature introduces new security risks: malicious or compromised agents can exploit communication channels to propagate misinformation and manipulate collective outcomes. In this paper, we study how such manipulation can arise and spread by borrowing the Friedkin-Johnsen opinion formation model from social sciences to propose a general theoretical framework to study LLM-MAS. Remarkably, this model closely captures LLM-MAS behavior, as we verify in extensive experiments across different network topologies and attack and defense scenarios. Theoretically and empirically, we find that a single highly stubborn and persuasive agent can take over MAS dynamics, underscoring the systems' high susceptibility to attacks by triggering a persuasion cascade that reshapes collective opinion. Our theoretical analysis reveals three mechanisms to increase system security: a) increasing the number of benign agents, b) increasing the innate stubbornness or peer-resistance of agents, or c) reducing trust in potential adversaries. Because scaling is computationally expensive and high stubbornness degrades the network's ability to reach consensus, we propose a new mechanism to mitigate threats by a trust-adaptive defense that dynamically adjusts inter-agent trust to limit adversarial influence while maintaining cooperative performance. Extensive experiments confirm that this mechanism effectively defends against manipulation.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15809
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Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions
2026-03-16 18:18:14Sadaf Tabatabaee, Sarah S. LamAbstract
Efficient classification of surgical procedures by urgency is paramount to optimize patient care and resource allocation within healthcare systems. This study introduces an unsupervised neural network approach to automatically categorize surgical transcriptions into three urgency levels: immediate, urgent, and elective. Leveraging BioClinicalBERT, a domain-specific language model, surgical transcripts are transformed into high-dimensional embeddings that capture their semantic nuances. These embeddings are subsequently clustered using both K-means and Deep Embedding Clustering (DEC) algorithms, in which DEC demonstrates superior performance in the formation of cohesive and well-separated clusters. To ensure clinical relevance and accuracy, the clustering results undergo validation through the Modified Delphi Method, which involves expert review and refinement. Following validation, a neural network that integrates Bidirectional Long Short-Term Memory (BiLSTM) layers with BioClinicalBERT embeddings is developed for classification tasks. The model is rigorously evaluated using cross-validation and metrics such as accuracy, precision, recall, and F1-score, which achieve robust performance and demonstrate strong generalization capabilities on unseen data. This unsupervised framework not only addresses the challenge of limited labeled data but also provides a scalable and reliable solution for real-time surgical prioritization, which ultimately enhances operational efficiency and patient outcomes in dynamic medical environments.
Abstract (translated)
URL
https://arxiv.org/abs/2604.06214
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Music Genre Classification: A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches
2026-03-16 15:43:48Sachin Prajuli, Abhishek Karna, OmPrakash DhaklAbstract
Automatic music genre classification is a long-standing challenge in Music Information Retrieval (MIR); work on non-Western music traditions remains scarce. Nepali music encompasses culturally rich and acoustically diverse genres--from the call-and-response duets of Lok Dohori to the rhythmic poetry of Deuda and the distinctive melodies of Tamang Selo--that have not been addressed by existing classification systems. In this paper, we construct a novel dataset of approximately 8,000 labeled 30-second audio clips spanning eight Nepali music genres and conduct a systematic comparison of nine classification models across two paradigms. Five classical machine learning classifiers (Logistic Regression, SVM, KNN, Random Forest, and XGBoost) are trained on 51 hand-crafted audio features extracted via Librosa, while four deep learning architectures (CNN, RNN, parallel CNN-RNN, and sequential CNN followed by RNN) operate on Mel spectrograms of dimension 640 x 128. Our experiments reveal that the sequential Convolutional Recurrent Neural Network (CRNN)--in which convolutional layers feed into an LSTM--achieves the highest accuracy of 84%, substantially outperforming both the best classical models (Logistic Regression and XGBoost, both at 71%) and all other deep architectures. We provide per-class precision, recall, F1-score, confusion matrices, and ROC analysis for every model, and offer a culturally grounded interpretation of misclassification patterns that reflects genuine overlaps in Nepal's musical traditions.
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URL
https://arxiv.org/abs/2603.15440
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Solution for 10th Competition on Ambivalence/Hesitancy Video Recognition Challenge using Divergence-Based Multimodal Fusion
2026-03-15 15:37:25Aislan Gabriel O. Souza, Agostinho Freire, Leandro Honorato Silva, Igor Lucas B. da Silva, Jo\~ao Vin\'icius R. de Andrade, Gabriel C. de Albuquerque, Lucas Matheus da S. Oliveira, M\'ario Stela Guerra, Luciana MachadoAbstract
We address the Ambivalence/Hesitancy (A/H) Video Recognition Challenge at the 10th ABAW Competition (CVPR 2026). We propose a divergence-based multimodal fusion that explicitly measures cross-modal conflict between visual, audio, and textual channels. Visual features are encoded as Action Units (AUs) extracted via Py-Feat, audio via Wav2Vec 2.0, and text via BERT. Each modality is processed by a BiLSTM with attention pooling and projected into a shared embedding space. The fusion module computes pairwise absolute differences between modality embeddings, directly capturing the incongruence that characterizes A/H. On the BAH dataset, our approach achieves a Macro F1 of 0.6808 on the validation test set, outperforming the challenge baseline of 0.2827. Statistical analysis across 1{,}132 videos confirms that temporal variability of AUs is the dominant visual discriminator of A/H.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16939
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PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis
2026-03-15 14:50:23Mritula Chandrasekaran, Sanket Kachole, Jarek Francik, Dimitrios MakrisAbstract
Pathological gait analysis is constrained by limited and variable clinical datasets, which restrict the modeling of diverse gait impairments. To address this challenge, we propose a Pathological Gait-conditioned Generative Adversarial Network (PGcGAN) that synthesises pathology-specific gait sequences directly from observed 3D pose keypoint trajectories data. The framework incorporates one-hot encoded pathology labels within both the generator and discriminator, enabling controlled synthesis across six gait categories. The generator adopts a conditional autoencoder architecture trained with adversarial and reconstruction objectives to preserve structural and temporal gait characteristics. Experiments on the Pathological Gait Dataset demonstrate strong alignment between real and synthetic sequences through PCA and t-SNE analyses, visual kinematic inspection, and downstream classification tasks. Augmenting real data with synthetic sequences improved pathological gait recognition across GRU, LSTM, and CNN models, indicating that pathology-conditioned gait synthesis can effectively support data augmentation in pathological gait analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14409
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M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
2026-03-15 12:53:09Mayank Mishra, Shawn Tan, Ion Stoica, Joseph Gonzalez, Tri DaoAbstract
Transformers are highly parallel but are limited to computations in the TC$^0$ complexity class, excluding tasks such as entity tracking and code execution that provably require greater expressive power. Motivated by this limitation, we revisit non-linear Recurrent Neural Networks (RNNs) for language modeling and introduce Matrix-to-Matrix RNN (M$^2$RNN): an architecture with matrix-valued hidden states and expressive non-linear state transitions. We demonstrate that the language modeling performance of non-linear RNNs is limited by their state size. We also demonstrate how the state size expansion mechanism enables efficient use of tensor cores. Empirically, M$^2$RNN achieves perfect state tracking generalization at sequence lengths not seen during training. These benefits also translate to large-scale language modeling. In hybrid settings that interleave recurrent layers with attention, Hybrid M$^2$RNN outperforms equivalent Gated DeltaNet hybrids by $0.4$-$0.5$ perplexity points on a 7B MoE model, while using $3\times$ smaller state sizes for the recurrent layers. Notably, replacing even a single recurrent layer with M$^2$RNN in an existing hybrid architecture yields accuracy gains comparable to Hybrid M$^2$RNN with minimal impact on training throughput. Further, the Hybrid Gated DeltaNet models with a single M$^2$RNN layer also achieve superior long-context generalization, outperforming state-of-the-art hybrid linear attention architectures by up to $8$ points on LongBench. Together, these results establish non-linear RNN layers as a compelling building block for efficient and scalable language models.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14360
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Deep Learning for Virtual Reality User Identification: A Benchmark
2026-03-14 16:46:00Davide Frizzo, Fabrizio Genilotti, David Petrovic, Arianna Stropeni, Francesco Borsatti, Davide Dalle Pezze, Riccardo De Monte, Manuel Barusco, Gian Antonio SustoAbstract
Virtual Reality (VR) applications require robust user identification systems to ensure secure access to equipment and protect worker identities. Motion tracking data from VR headsets and controllers has emerged as a powerful behavioral biometric, with recent studies demonstrating identification accuracies exceeding 94% across a large user base. However, the application of modern deep learning architectures, particularly State Space Models (SSM), to VR scenarios remains largely unexplored. In this work, we benchmark user identification performance across the large-scale Who is Alyx VR dataset, gathering data from 71 users playing the popular Half-Life:Alyx game. We evaluate both established architectures (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Temporal Convolutional Network (TCN), Transformer) and the emerging SSMs on time series motion data. Our results provide the first comprehensive benchmark of state-of-the-art and novel architectures for VR user identification, establishing baseline performance metrics for future privacy preserving authentication systems in manufacturing environments.
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URL
https://arxiv.org/abs/2604.16341
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Sat-JEPA-Diff: Bridging Self-Supervised Learning and Generative Diffusion for Remote Sensing
2026-03-14 13:30:49Kursat Komurcu, Linas PetkeviciusAbstract
Predicting satellite imagery requires a balance between structural accuracy and textural detail. Standard deterministic methods like PredRNN or SimVP minimize pixel-based errors but suffer from the "regression to the mean" problem, producing blurry outputs that obscure subtle geographic-spatial features. Generative models provide realistic textures but often misleadingly reveal structural anomalies. To bridge this gap, we introduce Sat-JEPA-Diff, which combines Self-Supervised Learning (SSL) with Hidden Diffusion Models (LDM). An IJEPA module predicts stable semantic representations, which then route a frozen Stable Diffusion backbone via a lightweight cross-attention adapter. This ensures that the synthesized high-accuracy textures are based on absolutely accurate structural predictions. Evaluated on a global Sentinel-2 dataset, Sat-JEPA-Diff excels at resolving sharp boundaries. It achieves leading perceptual scores (GSSIM: 0.8984, FID: 0.1475) and significantly outperforms deterministic baselines, despite standard autoregressive stability limits. The code and dataset are publicly available on this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13943
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Distributed Acoustic Sensing for Urban Traffic Monitoring: Spatio-Temporal Attention in Recurrent Neural Networks
2026-03-14 11:30:39Izhan Fakhruzi, Manuel Titos, Carmen Ben\'itez, Luz Garc\'iaAbstract
Effective urban traffic monitoring is essential for improving mobility, enhancing safety, and supporting sustainable cities. Distributed Acoustic Sensing (DAS) enables large-scale traffic observation by transforming existing fiber-optic infrastructure into dense arrays of vibration sensors. However, modeling the high-resolution spatio-temporal structure of DAS data for reliable traffic event recognition remains challenging. This study presents a real-world DAS-based traffic monitoring experiment conducted in Granada, Spain, where vehicles cross a fiber deployed perpendicular to the roadway. Recurrent neural networks (RNNs) are employed to model intra- and inter-event temporal dependencies. Spatial and temporal attention mechanisms are systematically integrated within the RNN architecture to analyze their impact on recognition performance, parameter efficiency, and interpretability. Results show that an appropriate and complementary placement of attention modules improves the balance between accuracy and model complexity. Attention heatmaps provide physically meaningful interpretations of classification decisions by highlighting informative spatial locations and temporal segments. Furthermore, the proposed SA-bi-TA configuration demonstrates spatial transferability, successfully recognizing traffic events at sensing locations different from those used during training, with only moderate performance degradation. These findings support the development of scalable and interpretable DAS-based traffic monitoring systems capable of operating under heterogeneous urban sensing conditions.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13903
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Music Source Restoration with Ensemble Separation and Targeted Reconstruction
2026-03-13 07:40:42Xinlong Deng, Yu Xia, Jie JiangAbstract
The Inaugural Music Source Restoration (MSR) Challenge targets the recovery of original, unprocessed stems from fully mixed and mastered music. Unlike conventional music source separation, MSR requires reversing complex production processes such as equalization, compression, reverberation, and other real-world degradations. To address MSR, we propose a two-stage system. First, an ensemble of pre-trained separation models produces preliminary source estimates. Then a set of pre-trained BSRNN-based restoration models performs targeted reconstruction to refine these estimates. On the official MSR benchmark, our system surpasses the baselines on all metrics, ranking second among all submissions. The code is available at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2603.16926
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Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
2026-03-13 07:25:27Ali Forootani, Raffaele IervolinoAbstract
This paper proposes a unified family of learnable Koopman operator parameterizations that integrate linear dynamical systems theory with modern deep learning forecasting architectures. We introduce four learnable Koopman variants-scalar-gated, per-mode gated, MLP-shaped spectral mapping, and low-rank Koopman operators which generalize and interpolate between strictly stable Koopman operators and unconstrained linear latent dynamics. Our formulation enables explicit control over the spectrum, stability, and rank of the linear transition operator while retaining compatibility with expressive nonlinear backbones such as Patchtst, Autoformer, and Informer. We evaluate the proposed operators in a large-scale benchmark that also includes LSTM, DLinear, and simple diagonal State-Space Models (SSMs), as well as lightweight transformer variants. Experiments across multiple horizons and patch lengths show that learnable Koopman models provide a favorable bias-variance trade-off, improved conditioning, and more interpretable latent dynamics. We provide a full spectral analysis, including eigenvalue trajectories, stability envelopes, and learned spectral distributions. Our results demonstrate that learnable Koopman operators are effective, stable, and theoretically principled components for deep forecasting.
Abstract (translated)
URL
https://arxiv.org/abs/2602.02592
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Dynamic Sparse Attention: Access Patterns and Architecture
Abstract
Dynamic sparse attention (DSA) reduces the per-token attention bandwidth by restricting computation to a top-k subset of cached key-value (KV) entries, but its token-dependent selection pattern introduces a system-level challenge: the KV working set is fragmented, volatile, and difficult to prefetch, which can translate into poor cache locality and stalled decode throughput. We study these effects by implementing a lightweight indexer for DSA-style selection on multiple open-source backbones and logging per-layer KV indices during autoregressive decoding. Our analysis shows a gap in serving DSA backbones - a potential for a high volume of blocking LL (last level) cache miss events, causing inefficiency; we propose a novel LL cache reservation system to save KV tokens in the LL cache between decode steps, combined with a token-granularity LRU eviction policy, and show on the data we collected how this architecture can benefit serving with DSA implemented on different backbones. Finally, we propose directions for future architectural and algorithmic exploration to improve serving of DSA on modern inference platforms.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13430
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Abjad-Kids: An Arabic Speech Classification Dataset for Primary Education
2026-03-11 08:03:52Abdul Aziz Snoubara, Baraa Al_Maradni, Haya Al_Naal, Malek Al_Madrmani, Roaa Jdini, Seedra Zarzour, Khloud Al JalladAbstract
Speech-based AI educational applications have gained significant interest in recent years, particularly for children. However, children speech research remains limited due to the lack of publicly available datasets, especially for low-resource languages such as this http URL paper presents Abjad-Kids, an Arabic speech dataset designed for kindergarten and primary education, focusing on fundamental learning of alphabets, numbers, and colors. The dataset consists of 46397 audio samples collected from children aged 3 - 12 years, covering 141 classes. All samples were recorded under controlled specifications to ensure consistency in duration, sampling rate, and format. To address high intra-class similarity among Arabic phonemes and the limited samples per class, we propose a hierarchical audio classification based on CNN-LSTM architectures. Our proposed methodology decomposes alphabet recognition into a two-stage process: an initial grouping classification model followed by specialized classifiers for each group. Both strategies: static linguistic-based grouping and dynamic clustering-based grouping, were evaluated. Experimental results demonstrate that static linguistic-based grouping achieves superior performance. Comparisons between traditional machine learning with deep learning approaches, highlight the effectiveness of CNN-LSTM models combined with data augmentation. Despite achieving promising results, most of our experiments indicate a challenge with overfitting, which is likely due to the limited number of samples, even after data augmentation and model regularization. Thus, future work may focus on collecting additional data to address this issue. Abjad-Kids will be publicly available. We hope that Abjad-Kids enrich children representation in speech dataset, and be a good resource for future research in Arabic speech classification for kids.
Abstract (translated)
基于语音的人工智能教育应用近年来备受关注，尤其针对儿童群体。然而，由于缺乏公开可用的数据集，儿童语音研究仍较为有限，特别是对于阿拉伯语这类低资源语言。本文提出Abjad-Kids——一个面向幼儿园及小学教育的阿拉伯语语音数据集，专注于字母、数字和颜色的基础学习。该数据集包含从3至12岁儿童收集的46397个音频样本，覆盖141个类别。所有样本均在受控规范下录制，以确保时长、采样率和格式的一致性。为应对阿拉伯语音素类内相似度高及每类样本有限的问题，我们提出了一种基于CNN-LSTM架构的分层音频分类方法。所提方法将字母识别分解为两阶段过程：首先进行分组分类，随后对每个组别采用专用分类器。两种策略——基于静态语言学的分组与基于动态聚类的分组——均经过评估。实验结果表明，基于静态语言学的分组表现更优。通过对比传统机器学习与深度学习方法，凸显了结合数据增强的CNN-LSTM模型的有效性。尽管取得了令人鼓舞的结果，但多数实验仍表明存在过拟合挑战，这很可能源于样本数量有限，即便经过数据增强和模型正则化处理。因此，未来工作可聚焦于收集更多数据以解决此问题。Abjad-Kids将公开提供。我们希望该数据集能丰富儿童语音数据集的代表性，并为阿拉伯语儿童语音分类的后续研究提供优质资源。
URL
https://arxiv.org/abs/2603.20255
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JEDI: Jointly Embedded Inference of Neural Dynamics
2026-03-11 07:31:20Anirudh Jamkhandi, Ali Korojy, Olivier Codol, Guillaume Lajoie, Matthew G. PerichAbstract
Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to map the mechanisms of the brain's flexibility onto the dynamics underlying neural populations. However, identifying task-specific dynamical rules from limited, noisy, and high-dimensional experimental neural recordings remains a major challenge, as experimental data often provide only partial access to brain states and dynamical mechanisms. While recurrent neural networks (RNNs) directly constrained neural data have been effective in inferring underlying dynamical mechanisms, they are typically limited to single-task domains and struggle to generalize across behavioral conditions. Here, we introduce JEDI, a hierarchical model that captures neural dynamics across tasks and contexts by learning a shared embedding space over RNN weights. This model recapitulates individual samples of neural dynamics while scaling to arbitrarily large and complex datasets, uncovering shared structure across conditions in a single, unified model. Using simulated RNN datasets, we demonstrate that JEDI accurately learns robust, generalizable, condition-specific embeddings. By reverse-engineering the weights learned by JEDI, we show that it recovers ground truth fixed point structures and unveils key features of the underlying neural dynamics in the eigenspectra. Finally, we apply JEDI to motor cortex recordings during monkey reaching to extract mechanistic insight into the neural dynamics of motor control. Our work shows that joint learning of contextual embeddings and recurrent weights provides scalable and generalizable inference of brain dynamics from recordings alone.
Abstract (translated)
动物的大脑能够灵活而高效地利用单一神经网络完成多种行为任务。现代神经科学的一个核心目标是将大脑的灵活性映射到神经群体的动力学机制上。然而，从有限、嘈杂且高维度的实验神经记录中识别特定任务的动力学规则仍然是一个重大挑战，因为实验数据往往只能提供对脑状态和动力学机制的部分访问。尽管直接受约束于神经数据的递归神经网络（RNNs）在推断底层动态机制方面已经十分有效，但它们通常仅限于单一任务领域，并且难以跨行为条件进行泛化。 在这里，我们介绍了一种名为JEDI的分层模型，该模型通过学习共享权重的嵌入空间来捕捉跨任务和情境的神经动力学。此模型能够在单个统一模型中重现个体样本的神经动态的同时扩展到任意大小和复杂度的数据集，并揭示条件间共有的结构。使用模拟RNN数据集，我们展示了JEDI能够准确地学习出强大的、具有泛化能力的特定条件下的嵌入结构。通过逆向工程由JEDI学到的权重，我们证明它恢复了真实的固定点结构，并在特征谱中揭露了神经动态的关键特性。 最后，我们将JEDI应用于猴子伸手运动过程中记录到的运动皮层数据集，从而提取出对运动控制神经动力学机制的理解。我们的工作表明，同时学习情境嵌入和递归权重为从记录本身推断大脑动力学提供了可扩展且泛化的推理方式。
URL
https://arxiv.org/abs/2603.10489
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Modeling Stage-wise Evolution of User Interests for News Recommendation
2026-03-11 06:42:52Zhiyong Cheng, Yike Jin, Zhijie Zhang, Huilin Chen, Zhangling Duan, Meng WangAbstract
Personalized news recommendation is highly time-sensitive, as user interests are often driven by emerging events, trending topics, and shifting real-world contexts. These dynamics make it essential to model not only users' long-term preferences, which reflect stable reading habits and high-order collaborative patterns, but also their short-term, context-dependent interests that change rapidly over time. However, most existing approaches rely on a single static interaction graph, which struggles to capture both long-term preference patterns and short-term interest changes as user behavior evolves. To address this challenge, we propose a unified framework that learns user preferences from both global and local temporal perspectives. A global preference modeling component captures long-term collaborative signals from the overall interaction graph, while a local preference modeling component partitions historical interactions into stage-wise temporal subgraphs to represent short-term dynamics. Within this module, an LSTM branch models the progressive evolution of recent interests, and a self-attention branch captures long-range temporal dependencies. Extensive experiments on two large-scale real-world datasets show that our approach consistently outperforms strong baselines and delivers fresher and more relevant recommendations across diverse user behaviors and temporal settings.
Abstract (translated)
个性化新闻推荐具有很强的时间敏感性，因为用户的兴趣往往受到新兴事件、热门话题以及不断变化的现实背景的影响。这些动态特性使得不仅需要建模用户长期稳定的阅读习惯和高阶协作模式（反映他们的长期偏好），还需要捕捉他们短期的、受上下文影响的兴趣变化（随着行为演变而迅速改变）。然而，大多数现有方法依赖于单一静态的交互图，这很难同时捕捉到用户的长期偏好的模式以及随时间快速变化的短期兴趣。 为了解决这一挑战，我们提出了一种统一框架，该框架从全局和局部的时间角度学习用户偏好。其中，一个全局偏好建模组件通过整个交互图捕获长期协作信号；而另一个局部偏好建模组件将历史互动划分为分阶段的时间子图来表示短期动态变化。在局部偏好模块中，LSTM支路用于建模最近兴趣的逐步演变过程，自注意力支路则捕捉长时间跨度内的时间依赖性。 我们在两个大规模的真实世界数据集上进行了广泛的实验，结果表明我们的方法能够持续优于强基线模型，并为各种用户行为和时间设置提供更为新鲜且相关的推荐。
URL
https://arxiv.org/abs/2603.10471
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A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
Abstract
Accurate forecasting of financial market volatility is crucial for risk management, option pricing, and portfolio optimization. Traditional econometric models and classical machine learning methods face challenges in handling the inherent non-linear and non-stationary characteristics of financial time series. In recent years, the rapid development of quantum computing has provided a new paradigm for solving complex optimization and sampling problems. This paper proposes a novel hybrid quantum-classical computing framework aimed at combining the powerful representation capabilities of classical neural networks with the unique advantages of quantum models. For the specific task of financial market volatility forecasting, we designed and implemented a hybrid model based on this framework, which combines a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM is responsible for extracting complex dynamic features from historical time series data, while the QCBM serves as a learnable prior module, providing the model with a high-quality prior distribution to guide the forecasting process. We evaluated the model on two real financial datasets consisting of 5-minute high-frequency data from the Shanghai Stock Exchange (SSE) Composite Index and CSI 300 Index. Experimental results show that, compared to a purely classical LSTM baseline model, our hybrid quantum-classical model demonstrates significant advantages across multiple key metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and QLIKE loss, proving the great potential of quantum computing in enhancing the capabilities of financial forecasting models. More broadly, the proposed hybrid framework offers a flexible architecture that may be adapted to other machine learning tasks involving high-dimensional, complex, or non-linear data distributions.
Abstract (translated)
金融市场波动的准确预测对于风险管理、期权定价和投资组合优化至关重要。传统的计量经济学模型和经典机器学习方法在处理金融时间序列内在的非线性和非平稳特性时面临挑战。近年来，量子计算技术的迅速发展为解决复杂的优化和采样问题提供了一个新的范式。本文提出了一种新颖的混合量子-经典计算框架，旨在结合经典神经网络的强大表示能力与量子模型的独特优势。针对金融市场波动预测这一特定任务，我们基于该框架设计并实现了一个融合长短期记忆（LSTM）网络与量子电路诞生机（QCBM）的混合模型。LSTM负责从历史时间序列数据中提取复杂的动态特征，而QCBM作为可学习先验模块，为模型提供高质量的先验分布以指导预测过程。 我们在由上海证券交易所综合指数和沪深300指数组成的两个真实金融数据集上对该模型进行了评估。这些数据集包含5分钟高频数据。实验结果表明，与仅基于经典LSTM网络的基准模型相比，我们的混合量子-经典模型在均方误差（MSE）、均方根误差（RMSE）和QLIKE损失等多个关键指标上展现出显著优势，证明了量子计算技术在增强金融预测模型能力方面的巨大潜力。更广泛地说，所提出的混合框架提供了一种灵活的架构，可以适应其他涉及高维、复杂或非线性数据分布的机器学习任务。 该研究不仅验证了混合量子-经典模型在金融市场波动预测中的优越性能，还为未来将量子计算技术应用于更多领域的探索铺平道路。
URL
https://arxiv.org/abs/2603.09789
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The Patrologia Graeca Corpus: OCR, Annotation, and Open Release of Noisy Nineteenth-Century Polytonic Greek Editions
2026-03-10 10:21:54Chahan Vidal-Gor\`ene (CJM, LIPN), Bastien KindtAbstract
We present the Patrologia Graeca Corpus, the first large-scale open OCR and linguistic resource for nineteenthcentury editions of Ancient Greek. The collection covers the remaining undigitized volumes of the Patrologia Graeca (PG), printed in complex bilingual (Greek-Latin) layouts and characterized by highly degraded polytonic Greek typography. Through a dedicated pipeline combining YOLO-based layout detection and CRNN-based text recognition, we achieve a character error rate (CER) of 1.05% and a word error rate (WER) of 4.69%, largely outperforming existing OCR systems for polytonic Greek. The resulting corpus contains around six million lemmatized and part-of-speech tagged tokens, aligned with full OCR and layout annotations. Beyond its philological value, this corpus establishes a new benchmark for OCR on noisy polytonic Greek and provides training material for future models, including LLMs.
Abstract (translated)
我们介绍了Patrologia Graeca语料库，这是首个针对19世纪古希腊文版的大型开源OCR（光学字符识别）和语言资源。该收藏涵盖了未被数字化的Patrologia Graeca (PG) 卷，这些卷采用复杂的双语文本布局（希伯来-拉丁文），并且特征是高度退化的多音调希腊文字体。 通过结合基于YOLO的版面检测与CRNN基础的文字识别方法组成的专用管道，我们达到了1.05%的字符错误率(CER)和4.69%的单词错误率(WER)，这大大超越了现有的针对多音调希腊语的OCR系统。生成的语料库包含大约六百万个经过词形还原且标注了词性标签的词汇单元，与完整的OCR和版面注释对齐。 除了其文献学价值外，该语料库还为嘈杂环境下的多音调希腊语OCR设立了新的基准，并提供了未来模型（包括大语言模型）的训练材料。
URL
https://arxiv.org/abs/2603.09470
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Vision-Augmented On-Track System Identification for Autonomous Racing via Attention-Based Priors and Iterative Neural Correction
2026-03-10 09:12:16Zhiping Wu, Cheng Hu, Yiqin Wang, Lei Xie, Hongye SuAbstract
Operating autonomous vehicles at the absolute limits of handling requires precise, real-time identification of highly non-linear tire dynamics. However, traditional online optimization methods suffer from "cold-start" initialization failures and struggle to model high-frequency transient dynamics. To address these bottlenecks, this paper proposes a novel vision-augmented, iterative system identification framework. First, a lightweight CNN (MobileNetV3) translates visual road textures into a continuous heuristic friction prior, providing a robust "warm-start" for parameter optimization. Next, a S4 model captures complex temporal dynamic residuals, circumventing the memory and latency limitations of traditional MLPs and RNNs. Finally, a derivative-free Nelder-Mead algorithm iteratively extracts physically interpretable Pacejka tire parameters via a hybrid virtual simulation. Co-simulation in CarSim demonstrates that the lightweight vision backbone reduces friction estimation error by 76.1 using 85 fewer FLOPs, accelerating cold-start convergence by 71.4. Furthermore, the S4-augmented framework improves parameter extraction accuracy and decreases lateral force RMSE by over 60 by effectively capturing complex vehicle dynamics, demonstrating superior performance compared to conventional neural architectures.
Abstract (translated)
运行自动驾驶车辆时，在处理能力的极限下操作需要对高度非线性的轮胎动力学进行精确且实时的识别。然而，传统的在线优化方法会遇到“冷启动”初始化失败的问题，并且难以建模高频瞬态动态特性。为了解决这些问题，本文提出了一种新型的视觉增强迭代系统辨识框架。 首先，该框架使用一种轻量级CNN（MobileNetV3）将路面纹理转化为连续的摩擦先验信息，这为参数优化提供了一个稳健的“暖启动”。其次，S4模型捕捉复杂的时变动态残差，从而克服了传统MLP和RNN在内存和延迟上的限制。最后，一种无导数Nelder-Mead算法通过混合虚拟仿真迭代提取物理可解释的Pacejka轮胎参数。 在CarSim中的共仿真表明：轻量级视觉骨干网络使摩擦估算误差减少了76.1%，同时仅使用了85更少的FLOPs（浮点运算次数），加速冷启动收敛速度提高了71.4%。此外，经过S4增强的框架不仅提升了参数提取精度，还通过有效捕捉复杂的车辆动态特性将横向力均方根误差降低了超过60%，其性能明显优于传统的神经网络架构。 综上所述，该论文提出的视觉增强迭代系统辨识框架为解决自动驾驶汽车在极限条件下操作时遇到的问题提供了一种有效的解决方案。
URL
https://arxiv.org/abs/2603.09399
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The Costs of Reproducibility in Music Separation Research: a Replication of Band-Split RNN
Abstract
Music source separation is the task of isolating the instrumental tracks from a music song. Despite its spectacular recent progress, the trend towards more complex architectures and training protocols exacerbates reproducibility issues. The band-split recurrent neural networks (BSRNN) model is promising in this regard, since it yields close to state-of-the-art results on public datasets, and requires reasonable resources for training. Unfortunately, it is not straightforward to reproduce since its full code is not available. In this paper, we attempt to replicate BSRNN as closely as possible to the original paper through extensive experiments, which allows us to conduct a critical reflection on this reproducibility issue. Our contributions are three-fold. First, this study yields several insights on the model design and training pipeline, which sheds light on potential future improvements. In particular, since we were unsuccessful in reproducing the original results, we explore additional variants that ultimately yield an optimized BSRNN model, whose performance largely improves that of the original. Second, we discuss reproducibility issues from both methodological and practical perspectives. We notably underline how substantial time and energy costs could have been saved upon availability of the full pipeline. Third, our code and pre-trained models are released publicly to foster reproducible research. We hope that this study will contribute to spread awareness on the importance of reproducible research in the music separation community, and help promoting more transparent and sustainable practices.
Abstract (translated)
音乐源分离的任务是从一首歌曲中单独提取出乐器轨道。尽管这一领域近期取得了显著进展，但复杂架构和训练协议的发展加剧了再现性问题。带分频循环神经网络（BSRNN）模型在这一方面很有前景，因为它能在公开数据集上取得接近当前最佳的成果，并且需要合理的资源进行训练。然而，由于其完整代码不可用，它并不容易被复制。在这篇文章中，我们通过大量实验试图尽可能忠实地重现原始论文中的BSRNN，这使我们能够对这一再现性问题进行批判性的反思。 我们的贡献有三个方面： 1. 本研究提供了关于模型设计和训练流程的多个见解，为潜在未来改进提供了思路。特别是由于我们在复制原始结果方面不成功，我们探索了额外变体，并最终得到了一个优化后的BSRNN模型，其性能远超原版。 2. 我们从方法论和实践两个角度讨论了再现性问题。特别地，我们指出了如果整个流程完整可得，将能大幅节省时间和精力成本。 3. 我们的代码及预训练模型已公开发布，以促进可重复研究。希望这项研究有助于在音乐分离社区传播关于可重复研究重要性的认识，并帮助推广更加透明和可持续的做法。 通过上述努力，我们希望能对推进音乐源分离领域的科学进步做出贡献，同时也强调了开放性和透明性对于科学研究的重要性。
URL
https://arxiv.org/abs/2603.09187
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