OpenRaiser/Intern-Atlas huggingface dataset:
Penetrating the Hostile: Detecting DeFi Protocol Exploits through Cross-Contract Analysis
Venue: arXiv cs.CR 2025
Year: 2025

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes that may occur during malicious events. However, this victim-only approaches seldom possess the capability to cover the attacker's interaction intention logic. Furthermore, only a minuscule percentage of DeFi protocols experience attacks in real-world scenarios, which poses a significant challenge for these detection tools to demonstrate practical effectiveness. In this paper, we propose DeFiTail, the first framework that utilizes deep learning technology for access control and flash loan exploit detection. Through feeding the cross-contract static data flow, DeFiTail automatically learns the attack logic in real-world malicious events that occur on DeFi protocols, capturing the threat patterns between attacker and victim contracts. Since the DeFi protocol events involve interactions with multi-account transactions, the execution path with external and internal transactions requires to be unified. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, DeFiTail validates the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Comparative experiment results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset.
MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) show increasing promise in medical applications, but their ability to detect and correct errors in clinical texts -- a prerequisite for safe deployment -- remains under-evaluated, particularly beyond English. We introduce MedRECT, a cross-lingual benchmark (Japanese/English) that formulates medical error handling as three subtasks: error detection, error localization (sentence extraction), and error correction. MedRECT is built with a scalable, automated pipeline from the Japanese Medical Licensing Examinations (JMLE) and a curated English counterpart, yielding MedRECT-ja (663 texts) and MedRECT-en (458 texts) with comparable error/no-error balance. We evaluate 9 contemporary LLMs spanning proprietary, open-weight, and reasoning families. Key findings: (i) reasoning models substantially outperform standard architectures, with up to 13.5% relative improvement in error detection and 51.0% in sentence extraction; (ii) cross-lingual evaluation reveals 5-10% performance gaps from English to Japanese, with smaller disparities for reasoning models; (iii) targeted LoRA fine-tuning yields asymmetric improvements in error correction performance (Japanese: +0.078, English: +0.168) while preserving reasoning capabilities; and (iv) our fine-tuned model exceeds human expert performance on structured medical error correction tasks. To our knowledge, MedRECT is the first comprehensive cross-lingual benchmark for medical error correction, providing a reproducible framework and resources for developing safer medical LLMs across languages.
Leveraging Hierarchical Image-Text Misalignment for Universal Fake Image Detection
Venue: arXiv cs.CV 2025
Year: 2025

With the rapid development of generative models, detecting generated fake images to prevent their malicious use has become a critical issue recently. Existing methods frame this challenge as a naive binary image classification task. However, such methods focus only on visual clues, yielding trained detectors susceptible to overfitting specific image patterns and incapable of generalizing to unseen models. In this paper, we address this issue from a multi-modal perspective and find that fake images cannot be properly aligned with corresponding captions compared to real images. Upon this observation, we propose a simple yet effective detector termed ITEM by leveraging the image-text misalignment in a joint visual-language space as discriminative clues. Specifically, we first measure the misalignment of the images and captions in pre-trained CLIP's space, and then tune a MLP head to perform the usual detection task. Furthermore, we propose a hierarchical misalignment scheme that first focuses on the whole image and then each semantic object described in the caption, which can explore both global and fine-grained local semantic misalignment as clues. Extensive experiments demonstrate the superiority of our method against other state-of-the-art competitors with impressive generalization and robustness on various recent generative models.
Simple and Behavior-Driven Augmentation for Recommendation with Rich Collaborative Signals
Venue: arXiv cs.IR 2025
Year: 2025

Contrastive learning (CL) has been widely used for enhancing the performance of graph collaborative filtering (GCF) for personalized recommendation. Since data augmentation plays a crucial role in the success of CL, previous works have designed augmentation methods to remove noisy interactions between users and items in order to generate effective augmented views. However, the ambiguity in defining ''noisiness'' presents a persistent risk of losing core information and generating unreliable data views, while increasing the overall complexity of augmentation. In this paper, we propose Simple Collaborative Augmentation for Recommendation (SCAR), a novel and intuitive augmentation method designed to maximize the effectiveness of CL for GCF. Instead of removing information, SCAR leverages collaborative signals extracted from user-item interactions to generate pseudo-interactions, which are then either added to or used to replace existing interactions. This results in more robust representations while avoiding the pitfalls of overly complex augmentation modules. We conduct experiments on four benchmark datasets and show that SCAR outperforms previous CL-based GCF methods as well as other state-of-the-art self-supervised learning approaches across key evaluation metrics. SCAR exhibits strong robustness across different hyperparameter settings and is particularly effective in sparse data scenarios.
Proactive DDoS Detection and Mitigation in Decentralized Software-Defined Networking via Port-Level Monitoring and Zero-Training Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Centralized Software-Defined Networking (cSDN) offers flexible and programmable control of networks but suffers from scalability and reliability issues due to its reliance on centralized controllers. Decentralized SDN (dSDN) alleviates these concerns by distributing control across multiple local controllers, yet this architecture remains highly vulnerable to Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a novel detection and mitigation framework tailored for dSDN environments. The framework leverages lightweight port-level statistics combined with prompt engineering and in-context learning, enabling the DeepSeek-v3 Large Language Model (LLM) to classify traffic as benign or malicious without requiring fine-tuning or retraining. Once an anomaly is detected, mitigation is enforced directly at the attacker's port, ensuring that malicious traffic is blocked at their origin while normal traffic remains unaffected. An automatic recovery mechanism restores normal operation after the attack inactivity, ensuring both security and availability. Experimental evaluation under diverse DDoS attack scenarios demonstrates that the proposed approach achieves near-perfect detection, with 99.99% accuracy, 99.97% precision, 100% recall, 99.98% F1-score, and an AUC of 1.0. These results highlight the effectiveness of combining distributed monitoring with zero-training LLM inference, providing a proactive and scalable defense mechanism for securing dSDN infrastructures against DDoS threats.
Longitudinal Vestibular Schwannoma Dataset with Consensus-based Human-in-the-loop Annotations
Venue: arXiv cs.CV 2025
Year: 2025

Accurate segmentation of vestibular schwannoma (VS) on Magnetic Resonance Imaging (MRI) is essential for patient management but often requires time-intensive manual annotations by experts. While recent advances in deep learning (DL) have facilitated automated segmentation, challenges remain in achieving robust performance across diverse datasets and complex clinical cases. We present an annotated dataset stemming from a bootstrapped DL-based framework for iterative segmentation and quality refinement of VS in MRI. We combine data from multiple centres and rely on expert consensus for trustworthiness of the annotations. We show that our approach enables effective and resource-efficient generalisation of automated segmentation models to a target data distribution. The framework achieved a significant improvement in segmentation accuracy with a Dice Similarity Coefficient (DSC) increase from 0.9125 to 0.9670 on our target internal validation dataset, while maintaining stable performance on representative external datasets. Expert evaluation on 143 scans further highlighted areas for model refinement, revealing nuanced cases where segmentation required expert intervention. The proposed approach is estimated to enhance efficiency by approximately 37.4% compared to the conventional manual annotation process. Overall, our human-in-the-loop model training approach achieved high segmentation accuracy, highlighting its potential as a clinically adaptable and generalisable strategy for automated VS segmentation in diverse clinical settings. The dataset includes 190 patients, with tumour annotations available for 534 longitudinal contrast-enhanced T1-weighted (T1CE) scans from 184 patients, and non-annotated T2-weighted scans from 6 patients. This dataset is publicly accessible on The Cancer Imaging Archive (TCIA) (https://doi.org/10.7937/bq0z-xa62).
Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus
Venue: arXiv cs.CL 2025
Year: 2025

The linguistic diversity of India poses significant machine translation challenges, especially for underrepresented tribal languages like Bhili, which lack high-quality linguistic resources. This paper addresses the gap by introducing Bhili-Hindi-English Parallel Corpus (BHEPC), the first and largest parallel corpus worldwide comprising 110,000 meticulously curated sentences across Bhili, Hindi, and English. The corpus was created with the assistance of expert human translators. BHEPC spans critical domains such as education, administration, and news, establishing a valuable benchmark for research in low resource machine translation. To establish a comprehensive Bhili Machine Translation benchmark, we evaluated a wide range of proprietary and open-source Multilingual Large Language Models (MLLMs) on bidirectional translation tasks between English/Hindi and Bhili. Comprehensive evaluation demonstrates that the fine-tuned NLLB-200 distilled 600M variant model outperforms others, highlighting the potential of multilingual models in low resource scenarios. Furthermore, we investigated the generative translation capabilities of multilingual LLMs on BHEPC using in-context learning, assessing performance under cross-domain generalization and quantifying distributional divergence. This work bridges a critical resource gap and promotes inclusive natural language processing technologies for low-resource and marginalized languages globally.
Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models
Venue: arXiv cs.CV 2025
Year: 2025

We introduce Diff4Splat, a feed-forward method that synthesizes controllable and explicit 4D scenes from a single image. Our approach unifies the generative priors of video diffusion models with geometry and motion constraints learned from large-scale 4D datasets. Given a single input image, a camera trajectory, and an optional text prompt, Diff4Splat directly predicts a deformable 3D Gaussian field that encodes appearance, geometry, and motion, all in a single forward pass, without test-time optimization or post-hoc refinement. At the core of our framework lies a video latent transformer, which augments video diffusion models to jointly capture spatio-temporal dependencies and predict time-varying 3D Gaussian primitives. Training is guided by objectives on appearance fidelity, geometric accuracy, and motion consistency, enabling Diff4Splat to synthesize high-quality 4D scenes in 30 seconds. We demonstrate the effectiveness of Diff4Splatacross video generation, novel view synthesis, and geometry extraction, where it matches or surpasses optimization-based methods for dynamic scene synthesis while being significantly more efficient.
Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-Augmented Generation has shown remarkable results to address Large Language Models' hallucinations, which usually uses a large external corpus to supplement knowledge to LLMs. However, with the development of LLMs, the internal knowledge of LLMs has expanded significantly, thus causing significant knowledge redundancy between the external corpus and LLMs. On the one hand, the indexing cost of dense retrieval is highly related to the corpus size and thus significant redundant knowledge intensifies the dense retrieval's workload. On the other hand, the redundant knowledge in the external corpus is not helpful to LLMs and our exploratory analysis shows that it instead hurts the RAG performance on those questions which the LLM can answer by itself. To address these issues, we propose Zero-RAG to tackle these challenges. Specifically, we first propose the Mastery-Score metric to identify redundant knowledge in the RAG corpus to prune it. After pruning, answers to "mastered" questions rely primarily on internal knowledge of the LLM. To better harness the internal capacity, we propose Query Router and Noise-Tolerant Tuning to avoid the irrelevant documents' distraction and thus further improve the LLM's utilization of internal knowledge with pruned corpus. Experimental results show that Zero-RAG prunes the Wikipedia corpus by 30\% and accelerates the retrieval stage by 22\%, without compromising RAG's performance.
Red-teaming Activation Probes using Prompted LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Activation probes are attractive monitors for AI systems due to low cost and latency, but their real-world robustness remains underexplored. We ask: What failure modes arise under realistic, black-box adversarial pressure, and how can we surface them with minimal effort? We present a lightweight black-box red-teaming procedure that wraps an off-the-shelf LLM with iterative feedback and in-context learning (ICL), and requires no fine-tuning, gradients, or architectural access. Running a case study with probes for high-stakes interactions, we show that our approach can help discover valuable insights about a SOTA probe. Our analysis uncovers interpretable brittleness patterns (e.g., legalese-induced FPs; bland procedural tone FNs) and reduced but persistent vulnerabilities under scenario-constraint attacks. These results suggest that simple prompted red-teaming scaffolding can anticipate failure patterns before deployment and might yield promising, actionable insights to harden future probes.
4D Neural Voxel Splatting: Dynamic Scene Rendering with Voxelized Guassian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Although 3D Gaussian Splatting (3D-GS) achieves efficient rendering for novel view synthesis, extending it to dynamic scenes still results in substantial memory overhead from replicating Gaussians across frames. To address this challenge, we propose 4D Neural Voxel Splatting (4D-NVS), which combines voxel-based representations with neural Gaussian splatting for efficient dynamic scene modeling. Instead of generating separate Gaussian sets per timestamp, our method employs a compact set of neural voxels with learned deformation fields to model temporal dynamics. The design greatly reduces memory consumption and accelerates training while preserving high image quality. We further introduce a novel view refinement stage that selectively improves challenging viewpoints through targeted optimization, maintaining global efficiency while enhancing rendering quality for difficult viewing angles. Experiments demonstrate that our method outperforms state-of-the-art approaches with significant memory reduction and faster training, enabling real-time rendering with superior visual fidelity.
CueBench: Advancing Unified Understanding of Context-Aware Video Anomalies in Real-World
Venue: arXiv cs.CV 2025
Year: 2025

How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize on detecting unexpected occurrences deviated from normal patterns or comprehending anomalous events with interpretable descriptions. However, they exhibit only a superficial comprehension of real-world anomalies, with limited breadth in complex principles and subtle context that distinguish the anomalies from normalities, e.g., climbing cliffs with safety gear vs. without it. To this end, we introduce CueBench, the first of its kind Benchmark, devoted to Context-aware video anomalies within a Unified Evaluation framework. We comprehensively establish an event-centric hierarchical taxonomy that anchors two core event types: 14 conditional and 18 absolute anomaly events, defined by their refined semantics from diverse contexts across 174 scenes and 198 attributes. Based on this, we propose to unify and benchmark context-aware VAU with various challenging tasks across recognition, temporal grounding, detection, and anticipation. This also serves as a rigorous and fair probing evaluation suite for generative-discriminative as well as generalized-specialized vision-language models (VLMs). To address the challenges underlying CueBench, we further develop Cue-R1 based on R1-style reinforcement fine-tuning with verifiable, task-aligned, and hierarchy-refined rewards in a unified generative manner. Extensive results on CueBench reveal that, existing VLMs are still far from satisfactory real-world anomaly understanding, while our Cue-R1 surpasses these state-of-the-art approaches by over 24% on average.
GDPR-Bench-Android: A Benchmark for Evaluating Automated GDPR Compliance Detection in Android
Venue: arXiv cs.SE 2025
Year: 2025

Automating the detection of EU General Data Protection Regulation (GDPR) violations in source code is a critical but underexplored challenge. We introduce \textbf{GDPR-Bench-Android}, the first comprehensive benchmark for evaluating diverse automated methods for GDPR compliance detection in Android applications. It contains \textbf{1951} manually annotated violation instances from \textbf{15} open-source repositories, covering 23 GDPR articles at file-, module-, and line-level granularities. To enable a multi-paradigm evaluation, we contribute \textbf{Formal-AST}, a novel, source-code-native formal method that serves as a deterministic baseline. We define two tasks: (1) \emph{multi-granularity violation localization}, evaluated via Accuracy@\textit{k}; and (2) \emph{snippet-level multi-label classification}, assessed by macro-F1 and other classification metrics. We benchmark 11 methods, including eight state-of-the-art LLMs, our Formal-AST analyzer, a retrieval-augmented (RAG) method, and an agentic (ReAct) method. Our findings reveal that no single paradigm excels across all tasks. For Task 1, the ReAct agent achieves the highest file-level Accuracy@1 (17.38%), while the Qwen2.5-72B LLM leads at the line level (61.60%), in stark contrast to the Formal-AST method's 1.86%. For the difficult multi-label Task 2, the Claude-Sonnet-4.5 LLM achieves the best Macro-F1 (5.75%), while the RAG method yields the highest Macro-Precision (7.10%). These results highlight the task-dependent strengths of different automated approaches and underscore the value of our benchmark in diagnosing their capabilities. All resources are available at: https://github.com/Haoyi-Zhang/GDPR-Bench-Android.
Multi-Sensor Distributed Hypothesis Testing in the Low-Power Regime
Venue: arXiv cs.IT 2025
Year: 2025

We characterize the Stein-exponent of a distributed hypothesis testing scenario where two sensors transmit information through a memoryless multiple access channel (MAC) subject to a sublinear input cost constraint with respect to the number of channel uses and where the decision center has access to an additional local observation. Our main theorem provides conditions on the channel and cost functions for which the Stein-exponent of this distributed setup is no larger than the Stein-exponent of the local test at the decision center. Under these conditions, communication from the sensors to the decision center is thus useless in terms of Stein-exponent. The conditions are satisfied for additive noise MACs with generalized Gaussian noise under a p-th moment constraint (including the Gaussian channel with second-moment constraint) and for the class of fully-connected (where all inputs can induce all outputs) discrete memoryless multiple-access channels (DMMACs) under arbitrary cost constraints. We further show that for DMMACs that are not fully-connected, the Stein-exponent is larger and coincides with that of a setup with zero-rate noiseless communication links from either both sensors or only one sensor, as studied in [1].
An Empirical Investigation of the Experiences of Dyslexic Software Engineers
Venue: arXiv cs.SE 2025
Year: 2025

Dyslexia is a common learning disorder that primarily impairs an individual's reading and writing abilities. In adults, dyslexia can affect both professional and personal lives, often leading to mental challenges and difficulties acquiring and keeping work. In Software Engineering (SE), reading and writing difficulties appear to pose substantial challenges for core tasks such as programming. However, initial studies indicate that these challenges may not significantly affect their performance compared to non-dyslexic colleagues. Conversely, strengths associated with dyslexia could be particularly valuable in areas like programming and design. However, there is currently no work that explores the experiences of dyslexic software engineers, and puts their strengths into relation with their difficulties. To address this, we present a qualitative study of the experiences of dyslexic individuals in SE. We followed the basic stage of the Socio-Technical Grounded Theory method and base our findings on data collected through 10 interviews with dyslexic software engineers, 3 blog posts and 153 posts on the social media platform Reddit. We find that dyslexic software engineers especially struggle at the programming learning stage, but can succeed and indeed excel at many SE tasks once they master this step. Common SE-specific support tools, such as code completion and linters are especially useful to these individuals and mitigate many of the experienced difficulties. Finally, dyslexic software engineers exhibit strengths in areas such as visual thinking and creativity. Our findings have implications to SE practice and motivate several areas of future research in SE, such as investigating what makes code less/more understandable to dyslexic individuals.