OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Stability and Recovery for Neural Network Training
Venue: arXiv cs.LG 2026
Year: 2026

Training modern neural networks is increasingly fragile, with rare but severe destabilizing updates often causing irreversible divergence or silent performance degradation. Existing optimization methods primarily rely on preventive mechanisms embedded within the optimizer, offering limited ability to detect and recover from instability once it occurs. We introduce a supervisory runtime stability framework that treats optimization as a controlled stochastic process. By isolating an innovation signal derived from secondary measurements, such as validation probes, the framework enables automatic detection and recovery from destabilizing updates without modifying the underlying optimizer. We provide theoretical runtime safety guarantees that formalize bounded degradation and recovery. Our implementation incurs minimal overhead and is compatible with memory-constrained training settings.
BMDS-Net: A Bayesian Multi-Modal Deep Supervision Network for Robust Brain Tumor Segmentation
Venue: arXiv cs.CV 2026
Year: 2026

Accurate brain tumor segmentation from multi-modal magnetic resonance imaging (MRI) is a prerequisite for precise radiotherapy planning and surgical navigation. While recent Transformer-based models such as Swin UNETR have achieved impressive benchmark performance, their clinical utility is often compromised by two critical issues: sensitivity to missing modalities (common in clinical practice) and a lack of confidence calibration. Merely chasing higher Dice scores on idealized data fails to meet the safety requirements of real-world medical deployment. In this work, we propose BMDS-Net, a unified framework that prioritizes clinical robustness and trustworthiness over simple metric maximization. Our contribution is three-fold. First, we construct a robust deterministic backbone by integrating a Zero-Init Multimodal Contextual Fusion (MMCF) module and a Residual-Gated Deep Decoder Supervision (DDS) mechanism, enabling stable feature learning and precise boundary delineation with significantly reduced Hausdorff Distance, even under modality corruption. Second, and most importantly, we introduce a memory-efficient Bayesian fine-tuning strategy that transforms the network into a probabilistic predictor, providing voxel-wise uncertainty maps to highlight potential errors for clinicians. Third, comprehensive experiments on the BraTS 2021 dataset demonstrate that BMDS-Net not only maintains competitive accuracy but, more importantly, exhibits superior stability in missing-modality scenarios where baseline models fail. The source code is publicly available at https://github.com/RyanZhou168/BMDS-Net.
MetaWorld: Skill Transfer and Composition in a Hierarchical World Model for Grounding High-Level Instructions
Venue: arXiv cs.RO 2026
Year: 2026

Humanoid robot loco-manipulation remains constrained by the semantic-physical gap. Current methods face three limitations: Low sample efficiency in reinforcement learning, poor generalization in imitation learning, and physical inconsistency in VLMs. We propose MetaWorld, a hierarchical world model that integrates semantic planning and physical control via expert policy transfer. The framework decouples tasks into a VLM-driven semantic layer and a latent dynamics model operating in a compact state space. Our dynamic expert selection and motion prior fusion mechanism leverages a pre-trained multi-expert policy library as transferable knowledge, enabling efficient online adaptation via a two-stage framework. VLMs serve as semantic interfaces, mapping instructions to executable skills and bypassing symbol grounding. Experiments on Humanoid-Bench show MetaWorld outperforms world model-based RL in task completion and motion coherence. Our code will be found at https://anonymous.4open.science/r/metaworld-2BF4/
One-Shot Federated Clustering of Non-Independent Completely Distributed Data
Venue: arXiv cs.LG 2026
Year: 2026

Federated Learning (FL) that extracts data knowledge while protecting the privacy of multiple clients has achieved remarkable results in distributed privacy-preserving IoT systems, including smart traffic flow monitoring, smart grid load balancing, and so on. Since most data collected from edge devices are unlabeled, unsupervised Federated Clustering (FC) is becoming increasingly popular for exploring pattern knowledge from complex distributed data. However, due to the lack of label guidance, the common Non-Independent and Identically Distributed (Non-IID) issue of clients have greatly challenged FC by posing the following problems: How to fuse pattern knowledge (i.e., cluster distribution) from Non-IID clients; How are the cluster distributions among clients related; and How does this relationship connect with the global knowledge fusion? In this paper, a more tricky but overlooked phenomenon in Non-IID is revealed, which bottlenecks the clustering performance of the existing FC approaches. That is, different clients could fragment a cluster, and accordingly, a more generalized Non-IID concept, i.e., Non-ICD (Non-Independent Completely Distributed), is derived. To tackle the above FC challenges, a new framework named GOLD (Global Oriented Local Distribution Learning) is proposed. GOLD first finely explores the potential incomplete local cluster distributions of clients, then uploads the distribution summarization to the server for global fusion, and finally performs local cluster enhancement under the guidance of the global distribution. Extensive experiments, including significance tests, ablation studies, scalability evaluations, qualitative results, etc., have been conducted to show the superiority of GOLD.
EuleroDec: A Complex-Valued RVQ-VAE for Efficient and Robust Audio Coding
Venue: arXiv cs.SD 2026
Year: 2026

Audio codecs power discrete music generative modelling, music streaming and immersive media by shrinking PCM audio to bandwidth-friendly bit-rates. Recent works have gravitated towards processing in the spectral domain; however, spectrogram-domains typically struggle with phase modeling which is naturally complex-valued. Most frequency-domain neural codecs either disregard phase information or encode it as two separate real-valued channels, limiting spatial fidelity. This entails the need to introduce adversarial discriminators at the expense of convergence speed and training stability to compensate for the inadequate representation power of the audio signal. In this work we introduce an end-to-end complex-valued RVQ-VAE audio codec that preserves magnitude-phase coupling across the entire analysis-quantization-synthesis pipeline and removes adversarial discriminators and diffusion post-filters. Without GANs or diffusion we match or surpass much longer-trained baselines in-domain and reach SOTA out-of-domain performance. Compared to standard baselines that train for hundreds of thousands of steps, our model reducing training budget by an order of magnitude is markedly more compute-efficient while preserving high perceptual quality.
Reconstructing Training Data from Adapter-based Federated Large Language Models
Venue: arXiv cs.CR 2026
Year: 2026

Adapter-based Federated Large Language Models (FedLLMs) are widely adopted to reduce the computational, storage, and communication overhead of full-parameter fine-tuning for web-scale applications while preserving user privacy. By freezing the backbone and training only compact low-rank adapters, these methods appear to limit gradient leakage and thwart existing Gradient Inversion Attacks (GIAs). Contrary to this assumption, we show that low-rank adapters create new, exploitable leakage channels. We propose the Unordered-word-bag-based Text Reconstruction (UTR) attack, a novel GIA tailored to the unique structure of adapter-based FedLLMs. UTR overcomes three core challenges: low-dimensional gradients, frozen backbones, and combinatorially large reconstruction spaces by: (i) inferring token presence from attention patterns in frozen layers, (ii) performing sentence-level inversion within the low-rank subspace of adapter gradients, and (iii) enforcing semantic coherence through constrained greedy decoding guided by language priors. Extensive experiments across diverse models (GPT2-Large, BERT, Qwen2.5-7B) and datasets (CoLA, SST-2, Rotten Tomatoes) demonstrate that UTR achieves near-perfect reconstruction accuracy (ROUGE-1/2 > 99), even with large batch size settings where prior GIAs fail completely. Our results reveal a fundamental tension between parameter efficiency and privacy in FedLLMs, challenging the prevailing belief that lightweight adaptation inherently enhances security. Our code and data are available at https://github.com/shwksnshwowk-wq/GIA.
Variants of Higher-Dimensional Automata
Venue: arXiv cs.FL 2026
Year: 2026

The theory of higher-dimensional automata (HDAs) has seen rapid progress in recent years, and first applications, notably to Petri net analysis, are starting to show. It has, however, emerged that HDAs themselves often are too strict a formalism to use and reason about. In order to solve specific problems, weaker variants of HDAs have been introduced, such as HDAs with interfaces, partial HDAs, ST-automata or even relational HDAs. In this paper we collect definitions of these and a few other variants into a coherent whole and explore their properties and translations between them. We show that with regard to languages, the spectrum of variants collapses into two classes, languages closed under subsumption and those that are not. We also show that partial HDAs admit a Kleene theorem and that, contrary to HDAs, they are determinizable.
CTF for education
Venue: arXiv cs.CR 2026
Year: 2026

In this paper, we take a close look at how CTF can be used in cybersecurity education. We divide the CTF competitions into four different categories, which are attack-based CTFs, defense-based CTFs, jeopardy CTFs and gamified and wargames CTFs. We start our analysis by summarizing the main characteristics of different CTF types. We then compare them with each other in both learning objectives and other aspects like accessibility. We conclude that combining all four CTF formats can help participants build one's cybersecurity knowledge. By doing that, we hope that our findings will provide some useful insights for future CTF educators.
Split Algorithm in Linear Time for the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Windows
Venue: arXiv cs.DS 2026
Year: 2026

For many kinds of vehicle routing problems (VRPs), a popular heuristic approach involves constructing a Traveling Salesman Problem (TSP) solution, referred to as a long tour, then partitioning segments of the solution into routes for different vehicles with respect to problem constraints. Previously, a Split algorithm with a worst-case runtime of $Θ(n)$ was proposed for the capacitated VRP (CVRP) that finds the most cost-efficient partition of customers, given a long tour. This was an improvement over the previously fastest-known Split algorithm with a worst-case runtime of $Θ(n^2)$ that was based on Bellman's shortest path algorithm. While this linear Split has been an integral part of modern state-of-the-art CVRP approaches, little progress has been made in extending this algorithm to handle additional VRP variants, limiting the general applicability of the algorithm. In this work, we propose an extension of the linear Split that handles two cardinal VRP variants simultaneously: (i) simultaneous pickups and deliveries (VRPSPD) and (ii) time windows (VRPTW). The resulting $Θ(n)$ algorithm is guaranteed to be optimal, assuming travel times between nodes satisfy the triangle inequality. Additionally, we extend the linear Split to handle a capacity penalty for the VRPSPD. For the VRPTW, we extend the linear Split to handle the CVRP capacity penalty in conjunction with the popular time warp penalty function. Computational experiments are performed to empirically validate the speed gains of these linear Splits against their $Θ$($n^2$) counterparts.
AlignUI: A Method for Designing LLM-Generated UIs Aligned with User Preferences
Venue: arXiv cs.HC 2026
Year: 2026

Designing user interfaces that align with user preferences is a time-consuming process, which requires iterative cycles of prototyping, user testing, and refinement. Recent advancements in LLM-based UI generation have enabled efficient UI generation to assist the UI design process. We introduce AlignUI, a method that aligns LLM-generated UIs with user tasks and preferences by using a user preference dataset to guide the LLM's reasoning process. The dataset was crowdsourced from 50 general users (the target users of generated UIs) and contained 720 UI control preferences on eight image-editing tasks. We evaluated AlignUI by generating UIs for six unseen tasks and conducting a user study with 72 additional general users. The results showed that the generated UIs closely align with multiple dimensions of user preferences. We conclude by discussing the applicability of our method to support user-aligned UI design for multiple task domains and user groups, as well as personalized user needs.
Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning
Venue: arXiv cs.AR 2026
Year: 2026

Prefetching and off-chip prediction are two techniques proposed to hide long memory access latencies in high-performance processors. In this work, we demonstrate that: (1) prefetching and off-chip prediction often provide complementary performance benefits, yet (2) naively combining them often fails to realize their full performance potential, and (3) existing prefetcher control policies leave significant room for performance improvement behind. Our goal is to design a holistic framework that can autonomously learn to coordinate an off-chip predictor with multiple prefetchers employed at various cache levels. To this end, we propose a new technique called Athena, which models the coordination between prefetchers and off-chip predictor (OCP) as a reinforcement learning (RL) problem. Athena acts as the RL agent that observes multiple system-level features (e.g., prefetcher/OCP accuracy, bandwidth usage) over an epoch of program execution, and uses them as state information to select a coordination action (i.e., enabling the prefetcher and/or OCP, and adjusting prefetcher aggressiveness). At the end of every epoch, Athena receives a numerical reward that measures the change in multiple system-level metrics (e.g., number of cycles taken to execute an epoch). Athena uses this reward to autonomously and continuously learn a policy to coordinate prefetchers with OCP. Our extensive evaluation using a diverse set of memory-intensive workloads shows that Athena consistently outperforms prior state-of-the-art coordination policies across a wide range of system configurations with various combinations of underlying prefetchers, OCPs, and main memory bandwidths, while incurring only modest storage overhead. Athena is freely available at https://github.com/CMU-SAFARI/Athena.
Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks
Venue: arXiv cs.IT 2026
Year: 2026

In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncertainties resulting from imperfect channel state information (CSI). In this work, we propose a robust power allocation method that formulates the power optimization problem into a least-squares framework, enhanced by Tikhonov regularization to mitigate the adverse effects of channel estimation errors. We integrate our approach with zero-forcing precoding, enabling a design that is both computationally efficient and resilient to CSI imperfections. Numerical results indicate that the proposed method outperforms existing non-robust techniques while benefiting from low computational overhead, making it well-suited for large-scale deployments under CSI uncertainty.
Conduit: Programmer-Transparent Near-Data Processing Using Multiple Compute-Capable Resources in Solid State Drives
Venue: arXiv cs.AR 2026
Year: 2026

Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support three NDP paradigms: in-storage processing (ISP), processing using DRAM in the SSD (PuD-SSD), and in-flash processing (IFP). A large body of prior SSD-based NDP techniques operate in isolation, mapping computations to only one or two NDP paradigms (i.e., ISP, PuD-SSD, or IFP) within the SSD. These techniques (1) are tailored to specific workloads or kernels, (2) do not exploit the full computational potential of an SSD, and (3) lack programmer-transparency. While several prior works propose techniques to partition computation between the host and near-memory accelerators, adapting these techniques to SSDs has limited benefits because they (1) ignore the heterogeneity of the SSD resources, and (2) make offloading decisions based on limited factors such as bandwidth utilization, or data movement cost. We propose Conduit, a general-purpose, programmer-transparent NDP framework for SSDs that leverages multiple SSD computation resources. At compile time, Conduit executes a custom compiler (e.g., LLVM) pass that (i) vectorizes suitable application code segments into SIMD operations that align with the SSD's page layout, and (ii) embeds metadata (e.g., operation type, operand sizes) into the vectorized instructions to guide runtime offloading decisions. At runtime, within the SSD, Conduit performs instruction-granularity offloading by evaluating six key features, and uses a cost function to select the most suitable SSD resource. We evaluate Conduit and two prior NDP offloading techniques using an in-house event-driven SSD simulator on six data-intensive workloads. Conduit outperforms the best-performing prior offloading policy by 1.8x and reduces energy consumption by 46%.
A Mosco sufficient condition for intrinsic stability of non-unique convex Empirical Risk Minimization
Venue: arXiv cs.LG 2026
Year: 2026

Empirical risk minimization (ERM) stability is usually studied via single-valued outputs, while convex non-strict losses yield set-valued minimizers. We identify Painlevé-Kuratowski upper semicontinuity (PK-u.s.c.) as the intrinsic stability notion for the ERM solution correspondence (set-level Hadamard well-posedness) and a prerequisite to interpret stability of selections. We then characterize a minimal non-degenerate qualitative regime: Mosco-consistent perturbations and locally bounded minimizers imply PK-u.s.c., minimal-value continuity, and consistency of vanishing-gap near-minimizers. Quadratic growth yields explicit quantitative deviation bounds.
Time-Varying Causal Treatment for Quantifying the Causal Effect of Short-Term Variations on Arctic Sea Ice Dynamics
Venue: arXiv cs.LG 2026
Year: 2026

Quantifying the causal relationship between ice melt and freshwater distribution is critical, as these complex interactions manifest as regional fluctuations in sea surface height (SSH). Leveraging SSH as a proxy for sea ice dynamics enables improved understanding of the feedback mechanisms driving polar climate change and global sea-level rise. However, conventional deep learning models often struggle with reliable treatment effect estimation in spatiotemporal settings due to unobserved confounders and the absence of physical constraints. To address these challenges, we propose the Knowledge-Guided Causal Model Variational Autoencoder (KGCM-VAE) to quantify causal mechanisms between sea ice thickness and SSH. The proposed framework integrates a velocity modulation scheme in which smoothed velocity signals are dynamically amplified via a sigmoid function governed by SSH transitions to generate physically grounded causal treatments. In addition, the model incorporates Maximum Mean Discrepancy (MMD) to balance treated and control covariate distributions in the latent space, along with a causal adjacency-constrained decoder to ensure alignment with established physical structures. Experimental results on both synthetic and real-world Arctic datasets demonstrate that KGCM-VAE achieves superior PEHE compared to state-of-the-art benchmarks. Ablation studies further confirm the effectiveness of the approach, showing that the joint application of MMD and causal adjacency constraints yields a 1.88\% reduction in estimation error.
Beyond the Rabbit Hole: Mapping the Relational Harms of QAnon Radicalization
Venue: arXiv cs.CL 2026
Year: 2026

The rise of conspiracy theories has created far-reaching societal harm in the public discourse by eroding trust and fueling polarization. Beyond this public impact lies a deeply personal toll on the friends and families of conspiracy believers, a dimension often overlooked in large-scale computational research. This study fills this gap by systematically mapping radicalization journeys and quantifying the associated emotional toll inflicted on loved ones. We use the prominent case of QAnon as a case study, analyzing 12747 narratives from the r/QAnonCasualties support community through a novel mixed-methods approach. First, we use topic modeling (BERTopic) to map the radicalization trajectories, identifying key pre-existing conditions, triggers, and post-radicalization characteristics. From this, we apply an LDA-based graphical model to uncover six recurring archetypes of QAnon adherents, which we term "radicalization personas." Finally, using LLM-assisted emotion detection and regression modeling, we link these personas to the specific emotional toll reported by narrators. Our findings reveal that these personas are not just descriptive; they are powerful predictors of the specific emotional harms experienced by narrators. Radicalization perceived as a deliberate ideological choice is associated with narrator anger and disgust, while those marked by personal and cognitive collapse are linked to fear and sadness. This work provides the first empirical framework for understanding radicalization as a relational phenomenon, offering a vital roadmap for researchers and practitioners to navigate its interpersonal fallout.
A PUF-Based Security Framework for Fault and Intrusion Detection
Venue: arXiv cs.CR 2026
Year: 2026

Industrial Control Systems (ICS) rely on sensor feedback to keep safety-critical processes within operational limits. This research presents a hardware-root-of-trust that embeds a Physically Unclonable Function (PUF) at the measurement layer to authenticate sensor readings. The architecture combines voltage fingerprinting with a temporal authentication that integrates with standard industrial control system architecture. The research prototypes the PUF integration on a hardware-in-the-loop (HIL) water tank testbed using a Simulink-based PUF emulator. The system maintains 99.97% accuracy over a 5.18-hour period of normal operation and flags all injected anomalies, including spike faults, hard-over faults, and hardware trojan scenarios that push the system over to an unsafe operational state. The proposed architecture provides a process-aware, vendor-agnostic approach that can integrate with legacy plants to detect sensor signal degradation or sophisticated supply chain attacks.