OpenRaiser/Intern-Atlas huggingface dataset:
The Uncanny Valley in medical simulation-based training: a visual summary
Venue: arXiv cs.GR 2025
Year: 2025

The purpose of this review article is to provide a bibliographical as well as evidence-based visual guide regarding the effect of ``Uncanny Valley'' (UV) and how it profoundly influences medical virtual reality simulation-based training. The phenomenon, where increasingly realistic virtual humans elicit discomfort due to subtle imperfections, is crucial to understand and address in the context of medical training, where realism and immersion are key to effective learning. Our research team, consisting of experts in computer graphics, virtual reality, and medical education, brings a diverse and multidisciplinary perspective to this subject. Our collective experience spans developing advanced computer graphics systems, VR character simulation, and innovative educational technologies. We have collaborated across institutions and industries to push the boundaries of VR applications in medical training.
DRL-TH: Jointly Utilizing Temporal Graph Attention and Hierarchical Fusion for UGV Navigation in Crowded Environments
Venue: arXiv cs.RO 2025
Year: 2025

Deep reinforcement learning (DRL) methods have demonstrated potential for autonomous navigation and obstacle avoidance of unmanned ground vehicles (UGVs) in crowded environments. Most existing approaches rely on single-frame observation and employ simple concatenation for multi-modal fusion, which limits their ability to capture temporal context and hinders dynamic adaptability. To address these challenges, we propose a DRL-based navigation framework, DRL-TH, which leverages temporal graph attention and hierarchical graph pooling to integrate historical observations and adaptively fuse multi-modal information. Specifically, we introduce a temporal-guided graph attention network (TG-GAT) that incorporates temporal weights into attention scores to capture correlations between consecutive frames, thereby enabling the implicit estimation of scene evolution. In addition, we design a graph hierarchical abstraction module (GHAM) that applies hierarchical pooling and learnable weighted fusion to dynamically integrate RGB and LiDAR features, achieving balanced representation across multiple scales. Extensive experiments demonstrate that our DRL-TH outperforms existing methods in various crowded environments. We also implemented DRL-TH control policy on a real UGV and showed that it performed well in real world scenarios.
Virtual-Eyes: Quantitative Validation of a Lung CT Quality-Control Pipeline for Foundation-Model Cancer Risk Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Robust preprocessing is rarely quantified in deep-learning pipelines for low-dose CT (LDCT) lung cancer screening. We develop and validate Virtual-Eyes, a clinically motivated 16-bit CT quality-control pipeline, and measure its differential impact on generalist foundation models versus specialist models. Virtual-Eyes enforces strict 512x512 in-plane resolution, rejects short or non-diagnostic series, and extracts a contiguous lung block using Hounsfield-unit filtering and bilateral lung-coverage scoring while preserving the native 16-bit grid. Using 765 NLST patients (182 cancer, 583 non-cancer), we compute slice-level embeddings from RAD-DINO and Merlin with frozen encoders and train leakage-free patient-level MLP heads; we also evaluate Sybil and a 2D ResNet-18 baseline under Raw versus Virtual-Eyes inputs without backbone retraining. Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112). In contrast, Sybil and ResNet-18 degrade under Virtual-Eyes (Sybil AUC 0.886 to 0.837; ResNet-18 AUC 0.571 to 0.596) with evidence of context dependence and shortcut learning, and Merlin shows limited transferability (AUC approximately 0.507 to 0.567) regardless of preprocessing. These results demonstrate that anatomically targeted QC can stabilize and improve generalist foundation-model workflows but may disrupt specialist models adapted to raw clinical context.
Faster Algorithms for Global Minimum Vertex-Cut in Directed Graphs
Venue: arXiv cs.DS 2025
Year: 2025

We study the directed global minimum vertex-cut problem: given a directed vertex-weighted graph $G$, compute a vertex-cut $(L,S,R)$ in $G$ of minimum value, which is defined to be the total weight of all vertices in $S$. The problem, together with its edge-based variant, is one of the most basic in graph theory and algorithms, and has been studied extensively. The fastest currently known algorithm for directed global minimum vertex-cut (Henzinger, Rao and Gabow, FOCS 1996 and J. Algorithms 2000) has running time $\tilde{O}(mn)$, where $m$ and $n$ denote the number of edges and vertices in the input graph, respectively. A long line of work over the past decades led to faster algorithms for other main versions of the problem, including the undirected edge-based setting (Karger, STOC 1996 and J. ACM 2000), directed edge-based setting (Cen et al., FOCS 2021), and undirected vertex-based setting (Chuzhoy and Trabelsi, STOC 2025). However, for the vertex-based version in directed graphs, the 29 year-old $\tilde{O}(mn)$-time algorithm of Henzinger, Rao and Gabow remains the state of the art to this day, in all edge-density regimes. In this paper we break the $Θ(mn)$ running time barrier for the first time, by providing a randomized algorithm for directed global minimum vertex-cut, with running time $O\left(mn^{0.976}\cdot\operatorname{polylog} W\right)$ where $W$ is the ratio of largest to smallest vertex weight. Additionally, we provide a randomized $O\left(\min\left\{m^{1+o(1)}\cdot k,n^{2+o(1)}\right\}\right)$-time algorithm for the unweighted version of directed global minimum vertex-cut, where $k$ is the value of the optimal solution. The best previous algorithm for the problem achieved running time $\tilde O\left(\min\left\{k^2 \cdot m, mn^{11/12+o(1)}, n^{2+o(1)}\right\}\right)$ (Forster et al., SODA 2020, Li et al., STOC 2021).
FAST-IDS: A Fast Two-Stage Intrusion Detection System with Hybrid Compression for Real-Time Threat Detection in Connected and Autonomous Vehicles
Venue: arXiv cs.CR 2025
Year: 2025

We have implemented a multi-stage IDS for CAVs that can be deployed to resourec-constrained environments after hybrid model compression.
Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning
Venue: arXiv cs.RO 2025
Year: 2025

Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet these models primarily describe what they perceive and intend to do, rarely questioning whether their planned actions are safe or appropriate. This work introduces Counterfactual VLA (CF-VLA), a self-reflective VLA framework that enables the model to reason about and revise its planned actions before execution. CF-VLA first generates time-segmented meta-actions that summarize driving intent, and then performs counterfactual reasoning conditioned on both the meta-actions and the visual context. This step simulates potential outcomes, identifies unsafe behaviors, and outputs corrected meta-actions that guide the final trajectory generation. To efficiently obtain such self-reflective capabilities, we propose a rollout-filter-label pipeline that mines high-value scenes from a base (non-counterfactual) VLA's rollouts and labels counterfactual reasoning traces for subsequent training rounds. Experiments on large-scale driving datasets show that CF-VLA improves trajectory accuracy by up to 17.6%, enhances safety metrics by 20.5%, and exhibits adaptive thinking: it only enables counterfactual reasoning in challenging scenarios. By transforming reasoning traces from one-shot descriptions to causal self-correction signals, CF-VLA takes a step toward self-reflective autonomous driving agents that learn to think before they act.
Infinite families of graphs and stable completion of arbitrary matrices, Part I
Venue: arXiv cs.IT 2025
Year: 2025

We study deterministic constructions of graphs for which the unique completion of low rank matrices is generically possible regardless of the values of the entries. We relate the completability to the presence of some patterns (particular unions of self-avoiding walks) in the subgraph of the lattice graph generated from the support of the bi-adjacency matrix. The construction makes it possible to design infinite families of graphs on which exact and stable completion is possible for every fixed rank matrix through the sum-of-squares hierarchy.
F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model
Venue: arXiv cs.CV 2025
Year: 2025

With the advent of Generative AI, Single Image Super-Resolution (SISR) quality has seen substantial improvement, as the strong priors learned by Text-2-Image Diffusion (T2IDiff) Foundation Models (FM) can bridge the gap between High-Resolution (HR) and Low-Resolution (LR) images. However, flagship smartphone cameras have been slow to adopt generative models because strong generation can lead to undesirable hallucinations. For substantially degraded LR images, as seen in academia, strong generation is required and hallucinations are more tolerable because of the wide gap between LR and HR images. In contrast, in consumer photography, the LR image has substantially higher fidelity, requiring only minimal hallucination-free generation. We hypothesize that generation in SISR is controlled by the stringency and richness of the FM's conditioning feature. First, text features are high level features, which often cannot describe subtle textures in an image. Additionally, Smartphone LR images are at least $12MP$, whereas SISR networks built on T2IDiff FM are designed to perform inference on much smaller images ($<1MP$). As a result, SISR inference has to be performed on small patches, which often cannot be accurately described by text feature. To address these shortcomings, we introduce an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM). Lower level features provide stricter conditioning while being rich descriptors of even small patches.
Training-Free Color-Aware Adversarial Diffusion Sanitization for Diffusion Stegomalware Defense at Security Gateways
Venue: arXiv cs.CR 2025
Year: 2025

The rapid expansion of generative AI has normalized large-scale synthetic media creation, enabling new forms of covert communication. Recent generative steganography methods, particularly those based on diffusion models, can embed high-capacity payloads without fine-tuning or auxiliary decoders, creating significant challenges for detection and remediation. Coverless diffusion-based techniques are difficult to counter because they generate image carriers directly from secret data, enabling attackers to deliver stegomalware for command-and-control, payload staging, and data exfiltration while bypassing detectors that rely on cover-stego discrepancies. This work introduces Adversarial Diffusion Sanitization (ADS), a training-free defense for security gateways that neutralizes hidden payloads rather than detecting them. ADS employs an off-the-shelf pretrained denoiser as a differentiable proxy for diffusion-based decoders and incorporates a color-aware, quaternion-coupled update rule to reduce artifacts under strict distortion limits. Under a practical threat model and in evaluation against the state-of-the-art diffusion steganography method Pulsar, ADS drives decoder success rates to near zero with minimal perceptual impact. Results demonstrate that ADS provides a favorable security-utility trade-off compared to standard content transformations, offering an effective mitigation strategy against diffusion-driven steganography.
Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech
Venue: arXiv cs.CL 2025
Year: 2025

Automatic speech transcripts are often delivered as unstructured word streams that impede readability and repurposing. We recast paragraph segmentation as the missing structuring step and fill three gaps at the intersection of speech processing and text segmentation. First, we establish TEDPara (human-annotated TED talks) and YTSegPara (YouTube videos with synthetic labels) as the first benchmarks for the paragraph segmentation task. The benchmarks focus on the underexplored speech domain, where paragraph segmentation has traditionally not been part of post-processing, while also contributing to the wider text segmentation field, which still lacks robust and naturalistic benchmarks. Second, we propose a constrained-decoding formulation that lets large language models insert paragraph breaks while preserving the original transcript, enabling faithful, sentence-aligned evaluation. Third, we show that a compact model (MiniSeg) attains state-of-the-art accuracy and, when extended hierarchically, jointly predicts chapters and paragraphs with minimal computational cost. Together, our resources and methods establish paragraph segmentation as a standardized, practical task in speech processing.
Analyzing Airline Alliances through Multi-Attribute Graph Partitioning to Maximize Competition and Market Penetration Capability
Venue: arXiv cs.SI 2025
Year: 2025

The air transportation market is highly competitive and dynamic. Airlines often form alliances to expand their network reach, improve operational efficiency, and enhance customer experience. However, the impact of these alliances on market competition and operational efficiency is not fully understood. In this paper, we propose a novel approach to analyze airline alliances using multi\mfabian{-}attribute graph partitioning. We develop metrics to quantify the competitiveness of flight segments and the market penetration capability of airlines based on their alliance memberships. We formulate a bi\mfabian{-}objective optimization problem to maximize both competition and market penetration simultaneously. We also propose algorithms to solve this optimization problem and demonstrate their effectiveness using real-world flight schedule data. Our results provide insights into the structure of airline alliances and their implications for market competition and operational efficiency.
Correctness of Extended RSA Public Key Cryptosystem
Venue: arXiv cs.CR 2025
Year: 2025

This paper proposes an alternative approach to formally establishing the correctness of the RSA public key cryptosystem. The methodology presented herein deviates slightly from conventional proofs found in existing literature. Specifically, this study explores the conditions under which the choice of the positive integer N, a fundamental component of RSA, can be extended beyond the standard selection criteria. We derive explicit conditions that determine when certain values of N are valid for the encryption scheme and explain why others may fail to satisfy the correctness requirements. The scope of this paper is limited to the mathematical proof of correctness for RSA-like schemes, deliberately omitting issues related to the cryptographic security of RSA.
More Than Bits: Multi-Envelope Double Binary Factorization for Extreme Quantization
Venue: arXiv cs.LG 2025
Year: 2025

For extreme low-bit quantization of large language models (LLMs), Double Binary Factorization (DBF) is attractive as it enables efficient inference without sacrificing accuracy. However, the scaling parameters of DBF are too restrictive; after factoring out signs, all rank components share the same magnitude profile, resulting in performance saturation. We propose Multi-envelope DBF (MDBF), which retains a shared pair of 1-bit sign bases but replaces the single envelope with a rank-$l$ envelope. By sharing sign matrices among envelope components, MDBF effectively maintains a binary carrier and utilizes the limited memory budget for magnitude expressiveness. We also introduce a closed-form initialization and an alternating refinement method to optimize MDBF. Across the LLaMA and Qwen families, MDBF enhances perplexity and zero-shot accuracy over previous binary formats at matched bits per weight while preserving the same deployment-friendly inference primitive.
DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass
Venue: arXiv cs.RO 2025
Year: 2025

In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable grasps due to inadequate contact configurations. To overcome this limitation, we propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility. Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement to improve the alignment between the gripper frame origin and the object Center of Mass (CoM), and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach in simulation across 15 objects under both Known-shape (with clean CAD-derived dataset) and Observed-shape (with YCB object dataset) settings, including cross-platform grasp execution on three robot--gripper platforms. We further validate the method in real-world grasp experiments on a UR3e robot. Overall, DISF reduces CoM misalignment while maintaining geometric compatibility, translating into higher grasp success in both simulation and real-world execution compared to baselines. Additional videos and supplementary results are available on our project page: https://tomoya-yamanokuchi.github.io/disf-ras-project-page/
OCP-LS: An Efficient Algorithm for Visual Localization
Venue: arXiv cs.CV 2025
Year: 2025

This paper proposes a novel second-order optimization algorithm. It aims to address large-scale optimization problems in deep learning because it incorporates the OCP method and appropriately approximating the diagonal elements of the Hessian matrix. Extensive experiments on multiple standard visual localization benchmarks demonstrate the significant superiority of the proposed method. Compared with conventional optimiza tion algorithms, our framework achieves competitive localization accuracy while exhibiting faster convergence, enhanced training stability, and improved robustness to noise interference.
RGBT-Ground Benchmark: Visual Grounding Beyond RGB in Complex Real-World Scenarios
Venue: arXiv cs.CV 2025
Year: 2025

Visual Grounding (VG) aims to localize specific objects in an image according to natural language expressions, serving as a fundamental task in vision-language understanding. However, existing VG benchmarks are mostly derived from datasets collected under clean environments, such as COCO, where scene diversity is limited. Consequently, they fail to reflect the complexity of real-world conditions, such as changes in illumination, weather, etc., that are critical to evaluating model robustness and generalization in safety-critical applications. To address these limitations, we present RGBT-Ground, the first large-scale visual grounding benchmark built for complex real-world scenarios. It consists of spatially aligned RGB and Thermal infrared (TIR) image pairs with high-quality referring expressions, corresponding object bounding boxes, and fine-grained annotations at the scene, environment, and object levels. This benchmark enables comprehensive evaluation and facilitates the study of robust grounding under diverse and challenging conditions. Furthermore, we establish a unified visual grounding framework that supports both uni-modal (RGB or TIR) and multi-modal (RGB-TIR) visual inputs. Based on it, we propose RGBT-VGNet, a simple yet effective baseline for fusing complementary visual modalities to achieve robust grounding. We conduct extensive adaptations to the existing methods on RGBT-Ground. Experimental results show that our proposed RGBT-VGNet significantly outperforms these adapted methods, particularly in nighttime and long-distance scenarios. All resources will be publicly released to promote future research on robust visual grounding in complex real-world environments.
SynRAG: A Large Language Model Framework for Executable Query Generation in Heterogeneous SIEM System
Venue: arXiv cs.CR 2025
Year: 2025

Security Information and Event Management (SIEM) systems are essential for large enterprises to monitor their IT infrastructure by ingesting and analyzing millions of logs and events daily. Security Operations Center (SOC) analysts are tasked with monitoring and analyzing this vast data to identify potential threats and take preventive actions to protect enterprise assets. However, the diversity among SIEM platforms, such as Palo Alto Networks Qradar, Google SecOps, Splunk, Microsoft Sentinel and the Elastic Stack, poses significant challenges. As these systems differ in attributes, architecture, and query languages, making it difficult for analysts to effectively monitor multiple platforms without undergoing extensive training or forcing enterprises to expand their workforce. To address this issue, we introduce SynRAG, a unified framework that automatically generates threat detection or incident investigation queries for multiple SIEM platforms from a platform-agnostic specification. SynRAG can generate platformspecific queries from a single high-level specification written by analysts. Without SynRAG, analysts would need to manually write separate queries for each SIEM platform, since query languages vary significantly across systems. This framework enables seamless threat detection and incident investigation across heterogeneous SIEM environments, reducing the need for specialized training and manual query translation. We evaluate SynRAG against state-of-the-art language models, including GPT, Llama, DeepSeek, Gemma, and Claude, using Qradar and SecOps as representative SIEM systems. Our results demonstrate that SynRAG generates significantly better queries for crossSIEM threat detection and incident investigation compared to the state-of-the-art base models.
Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a reinforcement learning-augmented LLM agent framework that formulates cooperation as a decentralized partially observable Markov decision process (Dec-POMDP) and adopts centralized training with decentralized execution (CTDE). We introduce Group Relative Policy Optimization (GRPO) to jointly optimize agent policies with access to global signals during training, together with a simplified joint reward that balances task quality, speed, and coordination cost. On collaborative writing and coding benchmarks, our framework delivers a 3x increase in task processing speed over single-agent baselines, 98.7% structural/style consistency in writing, and a 74.6% test pass rate in coding. The approach consistently outperforms strong multi-agent LLM baselines and provides a practical path toward reliable collaboration in complex workflows.