OpenRaiser/Intern-Atlas huggingface dataset:
Zero-Overhead Introspection for Adaptive Test-Time Compute
Venue: arXiv cs.LG 2025
Year: 2025

Large language models excel at reasoning but lack key aspects of introspection, including anticipating their own success and the computation required to achieve it. Humans use real-time introspection to decide how much effort to invest, when to make multiple attempts, when to stop, and when to signal success or failure. Without this, LLMs struggle to make intelligent meta-cognition decisions. Test-time scaling methods like Best-of-N drive up cost and latency by using a fixed budget of samples regardless of the marginal benefit of each one at any point in generation, and the absence of confidence signals can mislead people, prevent appropriate escalation to better tools, and undermine trustworthiness. Learned verifiers or reward models can provide confidence estimates, but do not enable adaptive inference and add substantial cost by requiring extra models or forward passes. We present ZIP-RC, which equips models with zero-overhead introspective predictions of reward and cost. At every token, ZIP-RC reuses reserved or unused logits in the same forward pass as next-token prediction to output a joint distribution over final reward and remaining length -- no extra models, architecture change, or inference overhead. This full joint distribution is used to compute a sampling utility which is the linear combination of the expected maximum reward, total compute, and latency of set of samples if generated to completion. During inference, we maximize this utility with meta-actions that determine which prefix of tokens to continue or initiate sampling from. On mixed-difficulty mathematical benchmarks, ZIP-RC improves accuracy by up to 12% over majority voting at equal or lower average cost, and traces smooth Pareto frontiers between quality, compute, and latency. By providing real-time reward-cost introspection, ZIP-RC enables adaptive, efficient reasoning.
Modeling and Simulation of Data Protection Systems for Business Continuity and Disaster Recovery
Venue: arXiv cs.NI 2025
Year: 2025

In today's corporate landscape, particularly where operations rely heavily on information technologies, establishing a robust business continuity plan, including a disaster recovery strategy, is essential for ensuring swift recuperation following outages. This study presents a comparative analysis of recovery solutions, focusing on systems that operate partially or entirely within cloud environments and assessing their reliability in fulfilling organizational roles securely and dependably. Two such systems were deployed and evaluated in a real-world production setting. Key performance and reliability metrics were identified using simulation software to enhance these systems, alongside a System Dynamics analysis conducted for each. This work proposes a comprehensive framework for selecting and maintaining data protection and recovery solutions within organizational structures, outlining criteria for aligning chosen approaches with operational needs while adhering to predetermined timelines specified in business continuity and disaster recovery plans. The resulting analysis and findings offer actionable insights to guide decision-making when selecting appropriate recovery concepts.
Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Deep unsupervised anomaly detection in brain magnetic resonance imaging offers a promising route to identify pathological deviations without requiring lesion-specific annotations. Yet, fragmented evaluations, heterogeneous datasets, and inconsistent metrics have hindered progress toward clinical translation. Here, we present a large-scale, multi-center benchmark of deep unsupervised anomaly detection for brain imaging. The training cohort comprised 2,976 T1 and 2,972 T2-weighted scans from healthy individuals across six scanners, with ages ranging from 6 to 89 years. Validation used 92 scans to tune hyperparameters and estimate unbiased thresholds. Testing encompassed 2,221 T1w and 1,262 T2w scans spanning healthy datasets and diverse clinical cohorts. Across all algorithms, the Dice-based segmentation performance varied between 0.03 and 0.65, indicating substantial variability. To assess robustness, we systematically evaluated the impact of different scanners, lesion types and sizes, as well as demographics (age, sex). Reconstruction-based methods, particularly diffusion-inspired approaches, achieved the strongest lesion segmentation performance, while feature-based methods showed greater robustness under distributional shifts. However, systematic biases, such as scanner-related effects, were observed for the majority of algorithms, including that small and low-contrast lesions were missed more often, and that false positives varied with age and sex. Increasing healthy training data yields only modest gains, underscoring that current unsupervised anomaly detection frameworks are limited algorithmically rather than by data availability. Our benchmark establishes a transparent foundation for future research and highlights priorities for clinical translation, including image native pretraining, principled deviation measures, fairness-aware modeling, and robust domain adaptation.
FlashVGGT: Efficient and Scalable Visual Geometry Transformers with Compressed Descriptor Attention
Venue: arXiv cs.CV 2025
Year: 2025

3D reconstruction from multi-view images is a core challenge in computer vision. Recently, feed-forward methods have emerged as efficient and robust alternatives to traditional per-scene optimization techniques. Among them, state-of-the-art models like the Visual Geometry Grounding Transformer (VGGT) leverage full self-attention over all image tokens to capture global relationships. However, this approach suffers from poor scalability due to the quadratic complexity of self-attention and the large number of tokens generated in long image sequences. In this work, we introduce FlashVGGT, an efficient alternative that addresses this bottleneck through a descriptor-based attention mechanism. Instead of applying dense global attention across all tokens, FlashVGGT compresses spatial information from each frame into a compact set of descriptor tokens. Global attention is then computed as cross-attention between the full set of image tokens and this smaller descriptor set, significantly reducing computational overhead. Moreover, the compactness of the descriptors enables online inference over long sequences via a chunk-recursive mechanism that reuses cached descriptors from previous chunks. Experimental results show that FlashVGGT achieves reconstruction accuracy competitive with VGGT while reducing inference time to just 9.3% of VGGT for 1,000 images, and scaling efficiently to sequences exceeding 3,000 images. Our project page is available at https://wzpscott.github.io/flashvggt_page/.
RoMe: Row Granularity Access Memory System for Large Language Models
Venue: arXiv cs.AR 2025
Year: 2025

Modern HBM-based memory systems have evolved over generations while retaining cache line granularity accesses. Preserving this fine granularity necessitated the introduction of bank groups and pseudo channels. These structures expand timing parameters and control overhead, significantly increasing memory controller scheduling complexity. Large language models (LLMs) now dominate deep learning workloads, streaming contiguous data blocks ranging from several kilobytes to megabytes per operation. In a conventional HBM-based memory system, these transfers are fragmented into hundreds of 32B cache line transactions. This forces the memory controller to employ unnecessarily intricate scheduling, leading to growing inefficiency. To address this problem, we propose RoMe. RoMe accesses DRAM at row granularity and removes columns, bank groups, and pseudo channels from the memory interface. This design simplifies memory scheduling, thereby requiring fewer pins per channel. The freed pins are aggregated to form additional channels, increasing overall bandwidth by 12.5% with minimal extra pins. RoMe demonstrates how memory scheduling logic can be significantly simplified for representative LLM workloads, and presents an alternative approach for next-generation HBM-based memory systems achieving increased bandwidth with minimal hardware overhead.
TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoring
Venue: arXiv cs.LG 2025
Year: 2025

Change-point detection (CPD) in high-dimensional, large-volume time series is challenging for statistical consistency, scalability, and interpretability. We introduce TimePred, a self-supervised framework that reduces multivariate CPD to univariate mean-shift detection by predicting each sample's normalized time index. This enables efficient offline CPD using existing algorithms and supports the integration of XAI attribution methods for feature-level explanations. Our experiments show competitive CPD performance while reducing computational cost by up to two orders of magnitude. In an industrial manufacturing case study, we demonstrate improved detection accuracy and illustrate the practical value of interpretable change-point insights.
Velocity-Adaptive Access Scheme for Semantic-Aware Vehicular Networks: Joint Fairness and AoI Optimization
Venue: arXiv cs.NI 2025
Year: 2025

In this paper, we address the problem of fair access and Age of Information (AoI) optimization in 5G New Radio (NR) Vehicle to Everything (V2X) Mode 2. Specifically, vehicles need to exchange information with the road side unit (RSU). However, due to the varying vehicle speeds leading to different communication durations, the amount of data exchanged between different vehicles and the RSU may vary. This may poses significant safety risks in high-speed environments. To address this, we define a fairness index through tuning the selection window of different vehicles and consider the image semantic communication system to reduce latency. However, adjusting the selection window may affect the communication time, thereby impacting the AoI. Moreover, considering the re-evaluation mechanism in 5G NR, which helps reduce resource collisions, it may lead to an increase in AoI. We analyze the AoI using Stochastic Hybrid System (SHS) and construct a multi-objective optimization problem to achieve fair access and AoI optimization. Sequential Convex Approximation (SCA) is employed to transform the non-convex problem into a convex one, and solve it using convex optimization. We also provide a large language model (LLM) based algorithm. The scheme's effectiveness is validated through numerical simulations.
WhiteLie: A Robust System for Spoofing User Data in Android Platforms
Venue: arXiv cs.CR 2025
Year: 2025

Android employs a permission framework that empowers users to either accept or deny sharing their private data (for example, location) with an app. However, many apps tend to crash when they are denied permission, leaving users no choice but to allow access to their data in order to use the app. In this paper, we introduce a comprehensive and robust user data spoofing system, WhiteLie, that can spoof a variety of user data and feed it to target apps. Additionally, it detects privacy-violating behaviours, automatically responding by supplying spoofed data instead of the user's real data, without crashing or disrupting the apps. Unlike prior approaches, WhiteLie requires neither device rooting nor altering the app's binary, making it deployable on stock Android devices. Through experiments on more than 70 popular Android apps, we demonstrate that WhiteLie is able to deceive apps into accepting spoofed data without getting detected. Our evaluation further demonstrates that WhiteLie introduces negligible overhead in terms of battery usage, CPU consumption, and app execution latency. Our findings underscore the feasibility of implementing user-centric privacy-enhancing mechanisms within the existing Android ecosystem.
A Systematic Characterization of LLM Inference on GPUs
Venue: arXiv cs.AR 2025
Year: 2025

This work presents a systematic characterization of Large Language Model (LLM) inference to address fragmented understanding. Through comprehensive experiments, we establish a four-dimensional analytical framework: (1) Two-Phase Heterogeneity Observation; (2) Microarchitectural Root Cause Analysis; (3) System Scaling Principles; and (4) Emerging Paradigm Boundaries. Our investigation progresses systematically from observation to foresight: identifying performance phenomena, revealing hardware causes, validating system behavior, and exploring new paradigms. This study not only consolidates a reliable empirical foundation for existing research but also provides new discoveries and practical optimization guidance for LLM inference.
StarDist: A Code Generator for Distributed Graph Algorithms
Venue: arXiv cs.DC 2025
Year: 2025

Relational data, occurring in the real world, are often structured as graphs, which provide the logical abstraction required to make analytical derivations simpler. As graphs get larger, the irregular access patterns exhibited in most graph algorithms, hamper performance. This, along with NUMA and physical memory limits, results in scaling complexities with sequential/shared memory frameworks. StarPlat's MPI backend abstracts away the programmatic complexity involved in designing optimal distributed graph algorithms. It provides an instrument for coding graph algorithms that scale over distributed memory. In this work, we provide an analysis-transformation framework that leverages general semantics associated with iterations involving nodes and their neighbors, within StarPlat, to aggregate communication. The framework scans for patterns that warrant re-ordering in neighborhood access patterns, aggregate communication, and avoid communication altogether with opportunistic caching in reduction constructs. We also architect an optimized bulk-reduction substrate using Open MPI's passive Remote Memory Access (RMA) constructs. We applied our optimization logic to StarPlat's distributed backend and outperformed d-Galois by 2.05 and DRONE by 1.44 times in Single Source Shortest Paths across several big data graphs.
Open-world Hand-Object Interaction Video Generation Based on Structure and Contact-aware Representation
Venue: arXiv cs.CV 2025
Year: 2025

Generating realistic hand-object interactions (HOI) videos is a significant challenge due to the difficulty of modeling physical constraints (e.g., contact and occlusion between hands and manipulated objects). Current methods utilize HOI representation as an auxiliary generative objective to guide video synthesis. However, there is a dilemma between 2D and 3D representations that cannot simultaneously guarantee scalability and interaction fidelity. To address this limitation, we propose a structure and contact-aware representation that captures hand-object contact, hand-object occlusion, and holistic structure context without 3D annotations. This interaction-oriented and scalable supervision signal enables the model to learn fine-grained interaction physics and generalize to open-world scenarios. To fully exploit the proposed representation, we introduce a joint-generation paradigm with a share-and-specialization strategy that generates interaction-oriented representations and videos. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two real-world datasets in generating physics-realistic and temporally coherent HOI videos. Furthermore, our approach exhibits strong generalization to challenging open-world scenarios, highlighting the benefit of our scalable design. Our project page is https://hgzn258.github.io/SCAR/.
Forced Migration and Information-Seeking Behavior on Wikipedia: Insights from the Ukrainian Refugee Crisis
Venue: arXiv cs.CY 2025
Year: 2025

Gathering information about where to migrate is an important part of the migration process, especially during forced migration, when people must make rapid decisions under uncertainty. This study examines how forced migration relates to online information-seeking on Wikipedia. Focusing on the 2022 Russian invasion of Ukraine, we analyze how the resulting refugee crisis, which led to over six million Ukrainians fleeing across Europe, shaped views of Wikipedia articles about European cities. We compare changes in views of Ukrainian-language Wikipedia articles, used as a proxy for information-seeking by Ukrainians, with those in four other language editions. Our findings show that views of Ukrainian-language articles about European cities correlate more strongly with the number of Ukrainian refugees applying for temporary protection in European countries than views in other languages. Because Poland and Germany became the main destinations for refugees, we examine these countries more closely and find that applications for temporary protection in Polish and German cities are also more strongly correlated with views of their Ukrainian-language Wikipedia articles. We further analyze the timing between refugee flows to Poland and online information-seeking. Refugee border crossings occurred before increases in Ukrainian-language views of Polish city articles, indicating that information-seeking surged after displacement. This reactive pattern contrasts with the pre-departure planning typical of regular labor migration. Moreover, while official protection applications often lagged behind border crossings by weeks, Wikipedia activity rose almost immediately. Overall, Wikipedia usage offers a near real-time indicator of emerging migration patterns during crises.
IGen: Scalable Data Generation for Robot Learning from Open-World Images
Venue: arXiv cs.RO 2025
Year: 2025

The rise of generalist robotic policies has created an exponential demand for large-scale training data. However, on-robot data collection is labor-intensive and often limited to specific environments. In contrast, open-world images capture a vast diversity of real-world scenes that naturally align with robotic manipulation tasks, offering a promising avenue for low-cost, large-scale robot data acquisition. Despite this potential, the lack of associated robot actions hinders the practical use of open-world images for robot learning, leaving this rich visual resource largely unexploited. To bridge this gap, we propose IGen, a framework that scalably generates realistic visual observations and executable actions from open-world images. IGen first converts unstructured 2D pixels into structured 3D scene representations suitable for scene understanding and manipulation. It then leverages the reasoning capabilities of vision-language models to transform scene-specific task instructions into high-level plans and generate low-level actions as SE(3) end-effector pose sequences. From these poses, it synthesizes dynamic scene evolution and renders temporally coherent visual observations. Experiments validate the high quality of visuomotor data generated by IGen, and show that policies trained solely on IGen-synthesized data achieve performance comparable to those trained on real-world data. This highlights the potential of IGen to support scalable data generation from open-world images for generalist robotic policy training.
Evaluating SAM2 for Video Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

The Segmentation Anything Model 2 (SAM2) has proven to be a powerful foundation model for promptable visual object segmentation in both images and videos, capable of storing object-aware memories and transferring them temporally through memory blocks. While SAM2 excels in video object segmentation by providing dense segmentation masks based on prompts, extending it to dense Video Semantic Segmentation (VSS) poses challenges due to the need for spatial accuracy, temporal consistency, and the ability to track multiple objects with complex boundaries and varying scales. This paper explores the extension of SAM2 for VSS, focusing on two primary approaches and highlighting firsthand observations and common challenges faced during this process. The first approach involves using SAM2 to extract unique objects as masks from a given image, with a segmentation network employed in parallel to generate and refine initial predictions. The second approach utilizes the predicted masks to extract unique feature vectors, which are then fed into a simple network for classification. The resulting classifications and masks are subsequently combined to produce the final segmentation. Our experiments suggest that leveraging SAM2 enhances overall performance in VSS, primarily due to its precise predictions of object boundaries.
Seeing through Imagination: Learning Scene Geometry via Implicit Spatial World Modeling
Venue: arXiv cs.CV 2025
Year: 2025

Spatial reasoning, the ability to understand and interpret the 3D structure of the world, is a critical yet underdeveloped capability in Multimodal Large Language Models (MLLMs). Current methods predominantly rely on verbal descriptive tuning, which suffers from visual illiteracy, i.e., they learn spatial concepts through textual symbols alone, devoid of connection to their visual manifestations. To bridge this gap, this paper introduces MILO, an Implicit spatIaL wOrld modeling paradigm that simulates human-like spatial imagination. MILO integrates a visual generator to provide geometry-aware feedback, thereby implicitly grounding the MLLM's symbolic reasoning in perceptual experience. Complementing this paradigm, we propose RePE (Relative Positional Encoding), a novel encoding scheme that captures relative camera-pose transformations, offering superior performance over absolute coordinate systems. To support the training, we construct GeoGen, a large-scale Geometry-aware Generative dataset with approximately 2,241 videos and 67,827 observation-action-outcome triplets. Experiments demonstrate that our approach significantly enhances spatial reasoning capabilities across multiple baselines and benchmarks, offering a more holistic understanding of 3D space.
Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models
Venue: arXiv cs.LG 2025
Year: 2025

Generative diversity varies significantly across discrete latent generative models such as AR, MIM, and Diffusion. We propose a diagnostic framework, grounded in Information Bottleneck (IB) theory, to analyze the underlying strategies resolving this behavior. The framework models generation as a conflict between a 'Compression Pressure' - a drive to minimize overall codebook entropy - and a 'Diversity Pressure' - a drive to maximize conditional entropy given an input. We further decompose this diversity into two primary sources: 'Path Diversity', representing the choice of high-level generative strategies, and 'Execution Diversity', the randomness in executing a chosen strategy. To make this decomposition operational, we introduce three zero-shot, inference-time interventions that directly perturb the latent generative process and reveal how models allocate and express diversity. Application of this probe-based framework to representative AR, MIM, and Diffusion systems reveals three distinct strategies: "Diversity-Prioritized" (MIM), "Compression-Prioritized" (AR), and "Decoupled" (Diffusion). Our analysis provides a principled explanation for their behavioral differences and informs a novel inference-time diversity enhancement technique.