OpenRaiser/Intern-Atlas huggingface dataset:
Plug. Play. Persist. Inside a Ready-to-Go Havoc C2 Infrastructure
Venue: arXiv cs.CR 2025
Year: 2025

This analysis focuses on a single Azure-hosted Virtual Machine at 52.230.23.114 that the adversary converted into an all-in-one delivery, staging and Command-and-Control node. The host advertises an out-of-date Apache 2.4.52 instance whose open directory exposes phishing lures, PowerShell loaders, Reflective Shell-Code, compiled Havoc Demon implants and a toolbox of lateral-movement binaries; the same server also answers on 8443/80 for encrypted beacon traffic. The web tier is riddled with publicly documented critical vulnerabilities, that would have allowed initial code-execution had the attackers not already owned the device. Initial access is delivered through an HTML file that, once de-obfuscated, perfectly mimics Google Unusual sign-in attempt notification and funnels victims toward credential collection. A PowerShell command follows: it disables AMSI in-memory, downloads a Base64-encoded stub, allocates RWX pages and starts the shell-code without ever touching disk. That stub reconstructs a DLL in memory using the Reflective-Loader technique and hands control to Havoc Demon implant. Every Demon variant-32- and 64-bit alike-talks to the same backend, resolves Windows APIs with hashed look-ups, and hides its activity behind indirect syscalls. Runtime telemetry shows interests in registry under Image File Execution Options, deliberate queries to Software Restriction Policy keys, and heavy use of Crypto DLLs to protect payloads and C2 traffic. The attacker toolkit further contains Chisel, PsExec, Doppelganger and Whisker, some of them re-compiled under user directories that leak the developer personas tonzking123 and thobt. Collectively the findings paint a picture of a technically adept actor who values rapid re-tooling over deep operational security, leaning on Havoc modularity and on legitimate cloud services to blend malicious flows into ordinary enterprise traffic.
Computer Vision for Objects used in Group Work: Challenges and Opportunities
Venue: arXiv cs.CV 2025
Year: 2025

Interactive and spatially aware technologies are transforming educational frameworks, particularly in K-12 settings where hands-on exploration fosters deeper conceptual understanding. However, during collaborative tasks, existing systems often lack the ability to accurately capture real-world interactions between students and physical objects. This issue could be addressed with automatic 6D pose estimation, i.e., estimation of an object's position and orientation in 3D space from RGB images or videos. For collaborative groups that interact with physical objects, 6D pose estimates allow AI systems to relate objects and entities. As part of this work, we introduce FiboSB, a novel and challenging 6D pose video dataset featuring groups of three participants solving an interactive task featuring small hand-held cubes and a weight scale. This setup poses unique challenges for 6D pose because groups are holistically recorded from a distance in order to capture all participants -- this, coupled with the small size of the cubes, makes 6D pose estimation inherently non-trivial. We evaluated four state-of-the-art 6D pose estimation methods on FiboSB, exposing the limitations of current algorithms on collaborative group work. An error analysis of these methods reveals that the 6D pose methods' object detection modules fail. We address this by fine-tuning YOLO11-x for FiboSB, achieving an overall mAP_50 of 0.898. The dataset, benchmark results, and analysis of YOLO11-x errors presented here lay the groundwork for leveraging the estimation of 6D poses in difficult collaborative contexts.
Sim2Real Diffusion: Leveraging Foundation Vision Language Models for Adaptive Automated Driving
Venue: arXiv cs.RO 2025
Year: 2025

Simulation-based design, optimization, and validation of autonomous vehicles have proven to be crucial for their improvement over the years. Nevertheless, the ultimate measure of effectiveness is their successful transition from simulation to reality (sim2real). However, existing sim2real transfer methods struggle to address the autonomy-oriented requirements of balancing: (i) conditioned domain adaptation, (ii) robust performance with limited examples, (iii) modularity in handling multiple domain representations, and (iv) real-time performance. To alleviate these pain points, we present a unified framework for learning cross-domain adaptive representations through conditional latent diffusion for sim2real transferable automated driving. Our framework offers options to leverage: (i) alternate foundation models, (ii) a few-shot fine-tuning pipeline, and (iii) textual as well as image prompts for mapping across given source and target domains. It is also capable of generating diverse high-quality samples when diffusing across parameter spaces such as times of day, weather conditions, seasons, and operational design domains. We systematically analyze the presented framework and report our findings in terms of performance benchmarks and ablation studies. Additionally, we demonstrate its serviceability for autonomous driving using behavioral cloning case studies. Our experiments indicate that the proposed framework is capable of bridging the perceptual sim2real gap by over 40%.
Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, \emph{Reinforcement Learning} (RL) has made remarkable progress, achieving superhuman performance in a wide range of simulated environments. As research moves toward deploying RL in real-world applications, the field faces a new set of challenges inherent to real-world settings, such as large state-action spaces, non-stationarity, and partial observability. Despite their importance, these challenges are often underexplored in current benchmarks, which tend to focus on idealized, fully observable, and stationary environments, often neglecting to incorporate real-world complexities explicitly. In this paper, we introduce \texttt{Gym4ReaL}, a comprehensive suite of realistic environments designed to support the development and evaluation of RL algorithms that can operate in real-world scenarios. The suite includes a diverse set of tasks that expose algorithms to a variety of practical challenges. Our experimental results show that, in these settings, standard RL algorithms confirm their competitiveness against rule-based benchmarks, motivating the development of new methods to fully exploit the potential of RL to tackle the complexities of real-world tasks.
Origin-Destination Travel Demand Estimation: An Approach That Scales Worldwide, and Its Application to Five Metropolitan Highway Networks
Venue: arXiv cs.ET 2025
Year: 2025

Estimating Origin-Destination (OD) travel demand is vital for effective urban planning and traffic management. Developing universally applicable OD estimation methodologies is significantly challenged by the pervasive scarcity of high-fidelity traffic data and the difficulty in obtaining city-specific prior OD estimates (or seed ODs), which are often prerequisite for traditional approaches. Our proposed method directly estimates OD travel demand by systematically leveraging aggregated, anonymized statistics from Google Maps Traffic Trends, obviating the need for conventional census or city-provided OD data. The OD demand is estimated by formulating a single-level, one-dimensional, continuous nonlinear optimization problem with nonlinear equality and bound constraints to replicate highway path travel times. The method achieves efficiency and scalability by employing a differentiable analytical macroscopic network model. This model by design is computationally lightweight, distinguished by its parsimonious parameterization that requires minimal calibration effort and its capacity for instantaneous evaluation. These attributes ensure the method's broad applicability and practical utility across diverse cities globally. Using segment sensor counts from Los Angeles and San Diego highway networks, we validate our proposed approach, demonstrating a two-thirds to three-quarters improvement in the fit to segment count data over a baseline. Beyond validation, we establish the method's scalability and robust performance in replicating path travel times across diverse highway networks, including Seattle, Orlando, Denver, Philadelphia, and Boston. In these expanded evaluations, our method not only aligns with simulation-based benchmarks but also achieves an average 13% improvement in it's ability to fit travel time data compared to the baseline during afternoon peak hours.
$μ^2$Tokenizer: Differentiable Multi-Scale Multi-Modal Tokenizer for Radiology Report Generation
Venue: arXiv cs.LG 2025
Year: 2025

Automated radiology report generation (RRG) aims to produce detailed textual reports from clinical imaging, such as computed tomography (CT) scans, to improve the accuracy and efficiency of diagnosis and provision of management advice. RRG is complicated by two key challenges: (1) inherent complexity in extracting relevant information from imaging data under resource constraints, and (2) difficulty in objectively evaluating discrepancies between model-generated and expert-written reports. To address these challenges, we propose $μ^2$LLM, a $\underline{\textbf{mu}}$ltiscale $\underline{\textbf{mu}}$ltimodal large language models for RRG tasks. The novel $μ^2$Tokenizer, as an intermediate layer, integrates multi-modal features from the multiscale visual tokenizer and the text tokenizer, then enhances report generation quality through direct preference optimization (DPO), guided by GREEN-RedLlama. Experimental results on four large CT image-report medical datasets demonstrate that our method outperforms existing approaches, highlighting the potential of our fine-tuned $μ^2$LLMs on limited data for RRG tasks. At the same time, for prompt engineering, we introduce a five-stage, LLM-driven pipeline that converts routine CT reports into paired visual-question-answer triples and citation-linked reasoning narratives, creating a scalable, high-quality supervisory corpus for explainable multimodal radiology LLM. All code, datasets, and models will be publicly available in our official repository. https://github.com/Siyou-Li/u2Tokenizer
Exploring Theory-Laden Observations in the Brain Basis of Emotional Experience
Venue: arXiv cs.LG 2025
Year: 2025

In the science of emotion, it is widely assumed that folk emotion categories form a biological and psychological typology, and studies are routinely designed and analyzed to identify emotion-specific patterns. This approach shapes the observations that studies report, ultimately reinforcing the assumption that guided the investigation. Here, we reanalyzed data from one such typologically-guided study that reported mappings between individual brain patterns and group-averaged ratings of 34 emotion categories. Our reanalysis was guided by an alternative view of emotion categories as populations of variable, situated instances, and which predicts a priori that there will be significant variation in brain patterns within a category across instances. Correspondingly, our analysis made minimal assumptions about the structure of the variance present in the data. As predicted, we did not observe the original mappings and instead observed significant variation across individuals. These findings demonstrate how starting assumptions can ultimately impact scientific conclusions and suggest that a hypothesis must be supported using multiple analytic methods before it is taken seriously.
Training for X-Ray Vision: Amodal Segmentation, Amodal Content Completion, and View-Invariant Object Representation from Multi-Camera Video
Venue: arXiv cs.CV 2025
Year: 2025

Amodal segmentation and amodal content completion require using object priors to estimate occluded masks and features of objects in complex scenes. Until now, no data has provided an additional dimension for object context: the possibility of multiple cameras sharing a view of a scene. We introduce MOVi-MC-AC: Multiple Object Video with Multi-Cameras and Amodal Content, the largest amodal segmentation and first amodal content dataset to date. Cluttered scenes of generic household objects are simulated in multi-camera video. MOVi-MC-AC contributes to the growing literature of object detection, tracking, and segmentation by including two new contributions to the deep learning for computer vision world. Multiple Camera (MC) settings where objects can be identified and tracked between various unique camera perspectives are rare in both synthetic and real-world video. We introduce a new complexity to synthetic video by providing consistent object ids for detections and segmentations between both frames and multiple cameras each with unique features and motion patterns on a single scene. Amodal Content (AC) is a reconstructive task in which models predict the appearance of target objects through occlusions. In the amodal segmentation literature, some datasets have been released with amodal detection, tracking, and segmentation labels. While other methods rely on slow cut-and-paste schemes to generate amodal content pseudo-labels, they do not account for natural occlusions present in the modal masks. MOVi-MC-AC provides labels for ~5.8 million object instances, setting a new maximum in the amodal dataset literature, along with being the first to provide ground-truth amodal content. The full dataset is available at https://huggingface.co/datasets/Amar-S/MOVi-MC-AC ,
An AST-guided LLM Approach for SVRF Code Synthesis
Venue: arXiv cs.SE 2025
Year: 2025

Standard Verification Rule Format (SVRF) is essential for semiconductor applications like Design Rule Check (DRC), Layout Versus Schematic (LVS), and Optical Proximity Correction (OPC) and it faces challenges as advancing nodes create complex design rules that renders traditional SVRF development ineffective and highlight an expertise gap. This paper introduces a novel methodology integrating Abstract Syntax Tree (AST) embedding and Retrieval-Augmented Generation (RAG) for enhanced SVRF code synthesis, ensuring semantic accuracy and error minimization through structural validation with domain-specific insights for precise code generation. We evaluate different T5-based models and propose an innovative SVRF-specific scoring framework that complements standard metrics like BLEU and ROUGE-L. In our approach, AST provides rigorous structural validation, while RAG infuses relevant domain knowledge, effectively enhancing the code generation workflow. Testing on a comprehensive benchmark of 740 DRC rule implementations, our methodology demonstrates up to a 40\% improvement in code generation accuracy compared to basic text-based fine-tuning process. This fusion of industry expertise with advanced coding strategies not only optimizes SVRF development under limited dataset constraints but also creates a more intuitive and efficient coding environment. Consequently, users can rapidly iterate through design cycles, reduce manual error correction, and significantly improve overall productivity.
CGEarthEye:A High-Resolution Remote Sensing Vision Foundation Model Based on the Jilin-1 Satellite Constellation
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning methods have significantly advanced the development of intelligent rinterpretation in remote sensing (RS), with foundational model research based on large-scale pre-training paradigms rapidly reshaping various domains of Earth Observation (EO). However, compared to the open accessibility and high spatiotemporal coverage of medium-resolution data, the limited acquisition channels for ultra-high-resolution optical RS imagery have constrained the progress of high-resolution remote sensing vision foundation models (RSVFM). As the world's largest sub-meter-level commercial RS satellite constellation, the Jilin-1 constellation possesses abundant sub-meter-level image resources. This study proposes CGEarthEye, a RSVFM framework specifically designed for Jilin-1 satellite characteristics, comprising five backbones with different parameter scales with totaling 2.1 billion parameters. To enhance the representational capacity of the foundation model, we developed JLSSD, the first 15-million-scale multi-temporal self-supervised learning (SSL) dataset featuring global coverage with quarterly temporal sampling within a single year, constructed through multi-level representation clustering and sampling strategies. The framework integrates seasonal contrast, augmentation-based contrast, and masked patch token contrastive strategies for pre-training. Comprehensive evaluations across 10 benchmark datasets covering four typical RS tasks demonstrate that the CGEarthEye consistently achieves state-of-the-art (SOTA) performance. Further analysis reveals CGEarthEye's superior characteristics in feature visualization, model convergence, parameter efficiency, and practical mapping applications. This study anticipates that the exceptional representation capabilities of CGEarthEye will facilitate broader and more efficient applications of Jilin-1 data in traditional EO application.
Customizable ROI-Based Deep Image Compression
Venue: arXiv cs.CV 2025
Year: 2025

Region of Interest (ROI)-based image compression optimizes bit allocation by prioritizing ROI for higher-quality reconstruction. However, as the users (including human clients and downstream machine tasks) become more diverse, ROI-based image compression needs to be customizable to support various preferences. For example, different users may define distinct ROI or require different quality trade-offs between ROI and non-ROI. Existing ROI-based image compression schemes predefine the ROI, making it unchangeable, and lack effective mechanisms to balance reconstruction quality between ROI and non-ROI. This work proposes a paradigm for customizable ROI-based deep image compression. First, we develop a Text-controlled Mask Acquisition (TMA) module, which allows users to easily customize their ROI for compression by just inputting the corresponding semantic \emph{text}. It makes the encoder controlled by text. Second, we design a Customizable Value Assign (CVA) mechanism, which masks the non-ROI with a changeable extent decided by users instead of a constant one to manage the reconstruction quality trade-off between ROI and non-ROI. Finally, we present a Latent Mask Attention (LMA) module, where the latent spatial prior of the mask and the latent Rate-Distortion Optimization (RDO) prior of the image are extracted and fused in the latent space, and further used to optimize the latent representation of the source image. Experimental results demonstrate that our proposed customizable ROI-based deep image compression paradigm effectively addresses the needs of customization for ROI definition and mask acquisition as well as the reconstruction quality trade-off management between the ROI and non-ROI.
HelixPipe: Efficient Distributed Training of Long Sequence Transformers with Attention Parallel Pipeline Parallelism
Venue: arXiv cs.LG 2025
Year: 2025

As transformer sequence lengths grow, existing pipeline parallelisms incur suboptimal performance due to the quadratic attention computation and the substantial memory overhead. To relieve these challenges, we propose HelixPipe, a novel pipeline parallelism for long sequence transformer training. First, HelixPipe introduces attention parallel partition, which schedules attention computations of different micro batches across different pipeline stages in parallel, reducing pipeline bubbles. Second, it employs a two-fold first-in-last-out micro batch schedule to balance memory usage and overlap communication with computation. Additionally, HelixPipe utilizes recomputation without attention and chunked MLP to mitigate fragmentation and enable longer sequences. Experiments demonstrate that HelixPipe gains increasing advantages with longer sequence lengths, and outperforms existing methods in throughput and scalability across varying pipeline sizes, model sizes, and cluster configurations. Notably, it achieves a 26\% speedup over baseline methods when training a 7B model with 128k sequence length on 64 H20 GPUs. Code is available at https://github.com/code-tunnel/Megatron-LM/tree/dev.
ATSTrack: Enhancing Visual-Language Tracking by Aligning Temporal and Spatial Scales
Venue: arXiv cs.CV 2025
Year: 2025

A main challenge of Visual-Language Tracking (VLT) is the misalignment between visual inputs and language descriptions caused by target movement. Previous trackers have explored many effective feature modification methods to preserve more aligned features. However, an important yet unexplored factor ultimately hinders their capability, which is the inherent differences in the temporal and spatial scale of information between visual and language inputs. To address this issue, we propose a novel visual-language tracker that enhances the effect of feature modification by \textbf{A}ligning \textbf{T}emporal and \textbf{S}patial scale of different input components, named as \textbf{ATSTrack}. Specifically, we decompose each language description into phrases with different attributes based on their temporal and spatial correspondence with visual inputs, and modify their features in a fine-grained manner. Moreover, we introduce a Visual-Language token that comprises modified linguistic information from the previous frame to guide the model to extract visual features that are more relevant to language description, thereby reducing the impact caused by the differences in spatial scale. Experimental results show that our proposed ATSTrack achieves performance comparable to existing methods. Our code will be released.
FreNBRDF: A Frequency-Rectified Neural Material Representation
Venue: arXiv cs.GR 2025
Year: 2025

Accurate material modeling is crucial for achieving photorealistic rendering, bridging the gap between computer-generated imagery and real-world photographs. While traditional approaches rely on tabulated BRDF data, recent work has shifted towards implicit neural representations, which offer compact and flexible frameworks for a range of tasks. However, their behavior in the frequency domain remains poorly understood. To address this, we introduce FreNBRDF, a frequency-rectified neural material representation. By leveraging spherical harmonics, we integrate frequency-domain considerations into neural BRDF modeling. We propose a novel frequency-rectified loss, derived from a frequency analysis of neural materials, and incorporate it into a generalizable and adaptive reconstruction and editing pipeline. This framework enhances fidelity, adaptability, and efficiency. Extensive experiments demonstrate that FreNBRDF improves the accuracy and robustness of material appearance reconstruction and editing compared to state-of-the-art baselines, enabling more structured and interpretable downstream tasks and applications.
Have Object-Oriented Languages Missed a Trick with Class Function and its Subclasses?
Venue: arXiv cs.PL 2025
Year: 2025

Compared to functions in mathematics, functions in programming languages seem to be under classified. Functional programming languages based on the lambda calculus famously treat functions as first-class values. Object-oriented languages have adopted ``lambdas'', notably for call-back routines in event-based programming. Typically a programming language has functions, a function has a type, and some functions act on other functions and/or return functions but there is generally a lack of (i) ``class Function'' in the OO sense of the word class and particularly (ii) subclasses of Function for functions having specific properties. Some such classes are presented here and programmed in some popular programming languages as an experimental investigation into OO languages missing this opportunity.