OpenRaiser/Intern-Atlas huggingface dataset:
Delay-Aware Reinforcement Learning for Highway On-Ramp Merging under Stochastic Communication Latency
Venue: arXiv cs.RO 2026
Year: 2026

Delayed and partially observable state information poses significant challenges for reinforcement learning (RL)-based control in real-world autonomous driving. In highway on-ramp merging, a roadside unit (RSU) can sense nearby traffic, perform edge perception, and transmit state estimates to the ego vehicle over vehicle-to-infrastructure (V2I) links. With recent advancements in intelligent transportation infrastructure and edge computing, such RSU-assisted perception is increasingly realistic and already deployed in modern connected roadway systems. However, edge processing time and wireless transmission can introduce stochastic V2I communication delays, violating the Markov assumption and substantially degrading control performance. In this work, we propose DAROM, a Delay-Aware Reinforcement Learning framework for On-ramp Merging that is robust to stochastic delays. We model the problem as a random delay Markov decision process (RDMDP) and develop a unified RL agent for joint longitudinal and lateral control. To recover a Markovian representation under delayed observations, we introduce a Delay-Aware Encoder that conditions on delayed observations, masked action histories, and observed delay magnitude to infer the current latent state. We further integrate a physics-based safety controller to reduce collision risk during merging. Experiments in the Simulation of Urban MObility (SUMO) simulator using real-world traffic data from the Next Generation Simulation (NGSIM) dataset demonstrate that DAROM consistently outperforms standard RL baselines across traffic densities. In particular, the gated recurrent unit (GRU)-based encoder achieves over 99% success in high-density traffic with random V2I delays of up to 2.0 seconds.
Towards automated formal security analysis of SAML V2.0 Web Browser SSO standard -- the POST/Artifact use case
Venue: arXiv cs.CR 2026
Year: 2026

Single Sign-On (SSO) protocols streamline user authentication with a unified login for multiple online services, improving usability and security. One of the most common SSO protocol frameworks - the Security Assertion Markup Language V2.0 (SAML) Web SSO Profile - has been in use for more than two decades, primarily in government, education and enterprise environments. Despite its mission-critical nature, only certain deployments and configurations of the Web SSO Profile have been formally analyzed. This paper attempts to bridge this gap by performing a comprehensive formal security analysis of the SAML V2.0 SP-initiated SSO with POST/Artifact Bindings use case. Rather than focusing on a specific deployment and configuration, we closely follow the specification with the goal of capturing many different deployments allowed by the standard. Modeling and analysis is performed using Tamarin prover - state-of-the-art tool for automated verification of security protocols in the symbolic model of cryptography. Technically, we build a meta-model of the use case that we instantiate to eight different protocol variants. Using the Tamarin prover, we formally verify a number of critical security properties for those protocol variants, while identifying certain drawbacks and potential vulnerabilities.
Spatial Fairness: The Case for its Importance, Limitations of Existing Work, and Guidelines for Future Research
Venue: arXiv cs.CY 2026
Year: 2026

Despite location being increasingly used in decision-making systems deployed in sensitive domains such as mortgages and insurance, little attention has been paid to the unfairness that may seep in due to the correlation of location with characteristics considered protected under anti-discrimination law, such as race or national origin. This position paper argues for the urgent need to consider fairness with respect to location, termed $\textit{spatial fairness}$. It outlines the harms perpetuated through location's correlation with protected characteristics, which may be particularly consequential due to its treatment as a neutral or purely technical attribute, abstracted from its historical, political, and socioeconomic context. This interdisciplinary work connects knowledge from fields such as public policy, economic development, and geography to highlight how existing fair-AI research falls short in addressing spatial biases, and fails to consider challenges unique to spatial data. Furthermore, we identify limitations in the small body of prior work on spatial fairness work, and propose guidelines to inform future research aimed at mitigating spatial biases in data-driven decision-making systems.
Real-World Evaluation of two Cooperative Intersection Management Approaches
Venue: arXiv cs.RO 2026
Year: 2026

Cooperative maneuver planning promises to significantly improve traffic efficiency at unsignalized intersections by leveraging connected automated vehicles. Previous works on this topic have been mostly developed for completely automated traffic in a simple simulated environment. In contrast, our previously introduced planning approaches are specifically designed to handle real-world mixed traffic. The two methods are based on multi-scenario prediction and graph-based reinforcement learning, respectively. This is the first study to perform evaluations in a novel mixed traffic simulation framework as well as real-world drives with prototype connected automated vehicles in public traffic. The simulation features the same connected automated driving software stack as deployed on one of the automated vehicles. Our quantitative evaluations show that cooperative maneuver planning achieves a substantial reduction in crossing times and the number of stops. In a realistic environment with few automated vehicles, there are noticeable efficiency gains with only slightly increasing criticality metrics.
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
Venue: arXiv cs.CV 2026
Year: 2026

This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customization methods by higher-fidelity identity preservation and better instruction following, benefiting from two subtle designs. First, we encode the face identity into a series of feature maps instead of one image token as in prior arts, allowing the model to retain more details of the reference faces (e.g., scars, tattoos, and face shape ). Second, we introduce a disentangled integration strategy to balance the text and image guidance during the text-to-image generation process, alleviating the conflict between the reference faces and the text prompts (e.g., personalizing an adult into a "child" or an "elder"). Extensive experimental results demonstrate the effectiveness of our method on various applications, including human image personalization, face swapping under language prompts, making virtual characters into real people, etc. Project Page: https://jshilong.github.io/flashface-page.
Safety Verification of Wait-Only Non-Blocking Broadcast Protocols
Venue: arXiv cs.LO 2026
Year: 2026

Broadcast protocols are programs designed to be executed by networks of processes. Each process runs the same protocol, and communication between them occurs in synchronously in two ways: broadcast, where one process sends a message to all others, and rendez-vous, where one process sends a message to at most one other process. In both cases, communication is non-blocking, meaning the message is sent even if no process is able to receive it. We consider two coverability problems: the state coverability problem asks whether there exists a number of processes that allows reaching a given state of the protocol, and the configuration coverability problem asks whether there exists a number of processes that allows covering a given configuration. These two problems are known to be decidable and Ackermann-hard. We show that when the protocol is Wait-Only (i.e., it has no state from which a process can both send and receive messages), these problems become P-complete and PSPACE-complete, respectively.
Imperceptible Protection against Style Imitation from Diffusion Models
Venue: arXiv cs.CV 2026
Year: 2026

Recent progress in diffusion models has profoundly enhanced the fidelity of image generation, but it has raised concerns about copyright infringements. While prior methods have introduced adversarial perturbations to prevent style imitation, most are accompanied by the degradation of artworks' visual quality. Recognizing the importance of maintaining this, we introduce a visually improved protection method while preserving its protection capability. To this end, we devise a perceptual map to highlight areas sensitive to human eyes, guided by instance-aware refinement, which refines the protection intensity accordingly. We also introduce a difficulty-aware protection by predicting how difficult the artwork is to protect and dynamically adjusting the intensity based on this. Lastly, we integrate a perceptual constraints bank to further improve the imperceptibility. Results show that our method substantially elevates the quality of the protected image without compromising on protection efficacy.
Sound event localization and classification using WASN in Outdoor Environment
Venue: arXiv cs.SD 2026
Year: 2026

Deep learning-based sound event localization and classification is an emerging research area within wireless acoustic sensor networks. However, current methods for sound event localization and classification typically rely on a single microphone array, making them susceptible to signal attenuation and environmental noise, which limits their monitoring range. Moreover, methods using multiple microphone arrays often focus solely on source localization, neglecting the aspect of sound event classification. In this paper, we propose a deep learning-based method that employs multiple features and attention mechanisms to estimate the location and class of sound source. We introduce a Soundmap feature to capture spatial information across multiple frequency bands. We also use the Gammatone filter to generate acoustic features more suitable for outdoor environments. Furthermore, we integrate attention mechanisms to learn channel-wise relationships and temporal dependencies within the acoustic features. To evaluate our proposed method, we conduct experiments using simulated datasets with different levels of noise and size of monitoring areas, as well as different arrays and source positions. The experimental results demonstrate the superiority of our proposed method over state-of-the-art methods in both sound event classification and sound source localization tasks. And we provide further analysis to explain the reasons for the observed errors.
Weighted Reservoir Sampling With Replacement from Data Streams
Venue: arXiv cs.DS 2026
Year: 2026

In this work, we present a new random sampling method for data streams where the probability of an element's inclusion in the sample is proportional to a weight associated with that element. Our method is based on sampling with replacement, although most of the literature on this topic has focused on sampling without replacement.Our algorithm generates a weighted random sample in one pass over a population of unknown size. At any point in time, the sample is representative of the population seen so far and can be directly used by other modules without requiring any post-processing. We formally prove the correctness and efficiency of our method. An experimental analysis shows the performance of our method in practice when compared to state-of-the-art methods.
Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Venue: arXiv cs.HC 2026
Year: 2026

Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users. Feedback from these sessions revealed that while the model offers potential benefits to medical practitioners, the need for navigational guidance was identified as a key area for improvement. These findings underscore the importance of early deployment of AI models in real-world settings, leading to insights that can guide the refinement of the model and system based on actual user feedback.
Versatile Behavior Diffusion for Generalized Traffic Agent Simulation
Venue: arXiv cs.RO 2026
Year: 2026

Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants, thereby limiting their utility in the evaluation and validation of autonomous driving systems. We introduce Versatile Behavior Diffusion (VBD), a novel traffic scenario generation framework based on diffusion generative models that synthesizes scene-consistent, realistic, and controllable multi-agent interactions. VBD achieves strong performance in closed-loop traffic simulation, generating scene-consistent agent behaviors that reflect complex agent interactions. A key capability of VBD is inference-time scenario editing through multi-step refinement, guided by behavior priors and model-based optimization objectives, enabling flexible and controllable behavior generation. Despite being trained on real-world traffic datasets with only normal conditions, we introduce conflict-prior and game-theoretic guidance approaches. These approaches enable the generation of interactive, customizable, or long-tail safety-critical scenarios, which are essential for comprehensive testing and validation of autonomous driving systems. Extensive experiments validate the effectiveness and versatility of VBD and highlight its promise as a foundational tool for advancing traffic simulation and autonomous vehicle development. Project website: https://sites.google.com/view/versatile-behavior-diffusion
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
Venue: arXiv cs.CV 2026
Year: 2026

Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, paying inadequate attention to the inherent differences between RGB and thermal modalities. Recent progress in vision foundation models (VFMs) trained through self-supervision on vast amounts of unlabeled data has proven their ability to extract informative, general-purpose features. However, this potential has yet to be fully leveraged in the domain. In this study, we take one step toward this new research area by exploring a feasible strategy to fully exploit VFM features for RGB-thermal scene parsing. Specifically, we delve deeper into the unique characteristics of RGB and thermal modalities, thereby designing a hybrid, asymmetric encoder that incorporates both a VFM and a convolutional neural network. This design allows for more effective extraction of complementary heterogeneous features, which are subsequently fused in a dual-path, progressive manner. Moreover, we introduce an auxiliary task to further enrich the local semantics of the fused features, thereby improving the overall performance of RGB-thermal scene parsing. Our proposed HAPNet, equipped with all these components, demonstrates superior performance compared to all other state-of-the-art RGB-thermal scene parsing networks, achieving top ranks across three widely used public RGB-thermal scene parsing datasets. We believe this new paradigm has opened up new opportunities for future developments in data-fusion scene parsing approaches.
An Objective Improvement Approach to Solving Discounted Payoff Games
Venue: arXiv cs.DS 2026
Year: 2026

While discounted payoff games and classic games that reduce to them, like parity and mean-payoff games, are symmetric, their solutions are not. We have taken a fresh view on the properties that optimal solutions need to have, and devised a novel way to converge to them, which is entirely symmetric. We achieve this by building a constraint system that uses every edge to define an inequation, and update the objective function by taking a single outgoing edge for each vertex into account. These edges loosely represent strategies of both players, where the objective function intuitively asks to make the inequation to these edges sharp. In fact, where they are not sharp, there is an `error' represented by the difference between the two sides of the inequation, which is 0 where the inequation is sharp. Hence, the objective is to minimise the sum of these errors. For co-optimal strategies, and only for them, it can be achieved that all selected inequations are sharp or, equivalently, that the sum of these errors is zero. While no co-optimal strategies have been found, we step-wise improve the error by improving the solution for a given objective function or by improving the objective function for a given solution. This also challenges the gospel that methods for solving payoff games are either based on strategy improvement or on value iteration.
Unifying Low Dimensional Observations in Deep Learning Through the Deep Linear Unconstrained Feature Model
Venue: arXiv cs.LG 2026
Year: 2026

Empirical studies have revealed low dimensional structures in the eigenspectra of weights, Hessians, gradients, and feature vectors of deep networks, consistently observed across datasets and architectures in the overparameterized regime. In this work, we analyze deep unconstrained feature models (UFMs) to provide an analytic explanation of how these structures emerge at the layerwise level, including the bulk outlier Hessian spectrum and the alignment of gradient descent with the outlier eigenspace. We show that deep neural collapse underlies these phenomena, deriving explicit expressions for eigenvalues and eigenvectors of many deep learning matrices in terms of class feature means. Furthermore, we demonstrate that the full Hessian inherits its low dimensional structure from the layerwise Hessians, and empirically validate our theory in both UFMs and deep networks.
Reflexive graph lenses in univalent foundations
Venue: arXiv cs.LO 2026
Year: 2026

Martin-Löf's identity types provide a generic (albeit opaque) notion of identification or "equality" between any two elements of the same type, embodied in a canonical reflexive graph structure $(=_A, \mathbf{refl})$ on any type $A$. The miracle of Voevodsky's univalence principle is that it ensures, for essentially any naturally occurring structure in mathematics, that this the resultant notion of identification is equivalent to the type of isomorphisms in the category of such structures. Characterisations of this kind are not automatic and must be established one-by-one; to this end, several authors have employed reflexive graphs and displayed reflexive graphs to organise the characterisation of identity types. We contribute reflexive graph lenses, a new family of intermediate abstractions lying between families of reflexive graphs and displayed reflexive graphs that simplifies the characterisation of identity types for complex structures. Every reflexive graph lens gives rise to a (more complicated) displayed reflexive graph, and our experience suggests that many naturally occurring displayed reflexive graphs arise in this way. Evidence for the utility of reflexive graph lenses is given by means of several case studies, including the theory of reflexive graphs itself as well as that of polynomial type operators. Finally, we exhibit an equivalence between the type of reflexive graph fibrations and the type of univalent reflexive graph lenses.
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach
Venue: arXiv cs.NI 2026
Year: 2026

Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands and denser deployments to meet varied service demands. This evolution, while bringing numerous advantages, will also pose challenges, especially in mobility management, as it will increase the overall number of handovers due to smaller coverage areas and the higher signal attenuation. To address these challenges, we propose a deep learning based algorithm for predicting the future serving cell utilizing sequential user equipment measurements to minimize the handover failures and interruption time. Our algorithm enables network operators to dynamically adjust handover triggering events or incorporate UAV base stations for enhanced coverage and capacity, optimizing network objectives like load balancing and energy efficiency through transfer learning techniques. Our framework complies with the O-RAN specifications and can be deployed in a Near-Real-Time RAN Intelligent Controller as an xApp leveraging the E2SM-KPM service model. The evaluation results demonstrate that our algorithm achieves a 92% accuracy in predicting future serving cells with high probability. Finally, by utilizing transfer learning, our algorithm significantly reduces the retraining time by 91% and 77% when new handover trigger decisions or UAV base stations are introduced to the network dynamically.
Thought of Search: Planning with Language Models Through The Lens of Efficiency
Venue: arXiv cs.AI 2026
Year: 2026

Among the most important properties of algorithms investigated in computer science are soundness, completeness, and complexity. These properties, however, are rarely analyzed for the vast collection of recently proposed methods for planning with large language models. In this work, we alleviate this gap. We analyse these properties of using LLMs for planning and highlight that recent trends abandon both soundness and completeness for the sake of inefficiency. We propose a significantly more efficient approach that can, at the same time, maintain both soundness and completeness. We exemplify on four representative search problems, comparing to the LLM-based solutions from the literature that attempt to solve these problems. We show that by using LLMs to produce the code for the search components we can solve the entire datasets with 100\% accuracy with only a few calls to the LLM. We argue for a responsible use of compute resources; urging research community to investigate sound and complete LLM-based approaches that uphold efficiency.