OpenRaiser/Intern-Atlas huggingface dataset:
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models
Venue: arXiv cs.AI 2025
Year: 2025

The increasing use of vision-language models (VLMs) in healthcare applications presents great challenges related to hallucinations, in which the models may generate seemingly plausible results that are in fact incorrect. Such hallucinations can jeopardize clinical decision making, potentially harming the diagnosis and treatments. In this work, we propose MedHallTune, a large-scale benchmark designed specifically to evaluate and mitigate hallucinations in medical VLMs. Comprising over 100,000 images and 1,000,000 instruction pairs, MedHallTune includes both hallucination and non-hallucination samples, each with ground-truth annotations. We conduct a comprehensive evaluation of current medical and general VLMs using MedHallTune, assessing their performance across key metrics, including clinical accuracy, relevance, detail level, and risk level. The experimental results show that fine-tuning with MedHallTune successfully improves the ability of several existing models to manage hallucinations and boost their zero-shot performance on downstream visual-question-answering (VQA) tasks, making them more reliable for practical medical applications. Our work contributes to the development of more trustworthy VLMs. Codes and dataset will be available at \href{https://github.com/russellyq/MedHallTune}{MedHallTune}.
GraphCheck: Multipath Fact-Checking with Entity-Relationship Graphs
Venue: arXiv cs.CL 2025
Year: 2025

Automated fact-checking aims to assess the truthfulness of textual claims based on relevant evidence. However, verifying complex claims that require multi-hop reasoning remains a significant challenge. We propose GraphCheck, a novel framework that transforms claims into entity-relationship graphs for structured and systematic fact-checking. By explicitly modeling both explicit and latent entities and exploring multiple reasoning paths, GraphCheck enhances verification robustness. While GraphCheck excels in complex scenarios, it may be unnecessarily elaborate for simpler claims. To address this, we introduce DP-GraphCheck, a variant that employs a lightweight strategy selector to choose between direct prompting and GraphCheck adaptively. This selective mechanism improves both accuracy and efficiency by applying the appropriate level of reasoning to each claim. Experiments on the HOVER and EX-FEVER datasets demonstrate that our approach outperforms existing methods in verification accuracy, while achieving strong computational efficiency despite its multipath exploration. Moreover, the strategy selection mechanism in DP-GraphCheck generalizes well to other fact-checking pipelines, highlighting the broad applicability of our framework.
CyLens: Towards Reinventing Cyber Threat Intelligence in the Paradigm of Agentic Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

The exponential growth of cyber threat knowledge, exemplified by the expansion of databases such as MITRE-CVE and NVD, poses significant challenges for cyber threat analysis. Security professionals are increasingly burdened by the sheer volume and complexity of information, creating an urgent need for effective tools to navigate, synthesize, and act on large-scale data to counter evolving threats proactively. However, conventional threat intelligence tools often fail to scale with the dynamic nature of this data and lack the adaptability to support diverse threat intelligence tasks. In this work, we introduce CYLENS, a cyber threat intelligence copilot powered by large language models (LLMs). CYLENS is designed to assist security professionals throughout the entire threat management lifecycle, supporting threat attribution, contextualization, detection, correlation, prioritization, and remediation. To ensure domain expertise, CYLENS integrates knowledge from 271,570 threat reports into its model parameters and incorporates six specialized NLP modules to enhance reasoning capabilities. Furthermore, CYLENS can be customized to meet the unique needs of different or ganizations, underscoring its adaptability. Through extensive evaluations, we demonstrate that CYLENS consistently outperforms industry-leading LLMs and state-of-the-art cybersecurity agents. By detailing its design, development, and evaluation, this work provides a blueprint for leveraging LLMs to address complex, data-intensive cybersecurity challenges.
Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?
Venue: arXiv cs.CV 2025
Year: 2025

The emergence of foundation models in computational pathology has transformed histopathological image analysis, with whole slide imaging (WSI) diagnosis being a core application. Traditionally, weakly supervised fine-tuning via multiple instance learning (MIL) has been the primary method for adapting foundation models to WSIs. However, in this work we present a key experimental finding: a simple nonlinear mapping strategy combining mean pooling and a multilayer perceptron, called SiMLP, can effectively adapt patch-level foundation models to slide-level tasks without complex MIL-based learning. Through extensive experiments across diverse downstream tasks, we demonstrate the superior performance of SiMLP with state-of-the-art methods. For instance, on a large-scale pan-cancer classification task, SiMLP surpasses popular MIL-based methods by 3.52%. Furthermore, SiMLP shows strong learning ability in few-shot classification and remaining highly competitive with slide-level foundation models pretrained on tens of thousands of slides. Finally, SiMLP exhibits remarkable robustness and transferability in lung cancer subtyping. Overall, our findings challenge the conventional MIL-based fine-tuning paradigm, demonstrating that a task-agnostic representation strategy alone can effectively adapt foundation models to WSI analysis. These insights offer a unique and meaningful perspective for future research in digital pathology, paving the way for more efficient and broadly applicable methodologies.
VLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representation
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in vision-language models (VLMs) have shown remarkable potential in bridging visual and textual modalities. In computational pathology, domain-specific VLMs, which are pre-trained on extensive histopathology image-text datasets, have succeeded in various downstream tasks. However, existing research has primarily focused on the pre-training process and direct applications of VLMs on the patch level, leaving their great potential for whole slide image (WSI) applications unexplored. In this study, we hypothesize that pre-trained VLMs inherently capture informative and interpretable WSI representations through quantitative feature extraction. To validate this hypothesis, we introduce Vision and Language Embeddings for Explainable WSI Representation (VLEER), a novel method designed to leverage VLMs for WSI representation. We systematically evaluate VLEER on three pathological WSI datasets, proving its better performance in WSI analysis compared to conventional vision features. More importantly, VLEER offers the unique advantage of interpretability, enabling direct human-readable insights into the results by leveraging the textual modality for detailed pathology annotations, providing clear reasoning for WSI-level pathology downstream tasks.
A Faster Algorithm for Maximum Weight Matching on Unrestricted Bipartite Graphs
Venue: arXiv cs.DS 2025
Year: 2025

Given a weighted bipartite graph $G = (L, R, E, w)$, the maximum weight matching (MWM) problem seeks to find a matching $M \subseteq E$ that maximizes the total weight $\sum_{e \in M} w(e)$. This paper presents a novel algorithm with a time complexity of $O(\min(X^3 + E, XE + X^2\log X))$, where $X = \min(|L|, |R|)$. Unlike many existing algorithms, our approach supports real-valued weights without additional constraints. Under this condition, our result improves upon the previous best-known bound of $O(VE + V^2\log V)$, or more strictly $O(XE + XV\log V)$, where $V = L \cup R$. The suggested implementation code is simplified and publicly available at https://github.com/ShawxingKwok/Kwok-algorithm, with the average-case time complexity of $O(E^{1.4} + LR)$ estimated from experimental results on random graphs.
The Effect of Hop-count Modification Attack on Random Walk-based SLP Schemes Developed forWSNs: a Study
Venue: arXiv cs.CR 2025
Year: 2025

Source location privacy (SLP) has been of great concern in WSNs when deployed for habitat monitoring applications. The issue is taken care of by employing privacy-preserving routing schemes. In the existing works, the attacker is assumed to be passive in nature and backtracks to the source of information by eavesdropping the message signals. In this work, we try to understand the impact of active attacks by proposing a new hybrid attack model consisting of both active and passive attacks. The proposed model is then applied to three existing TTL-based random walk SLP solutions: phantom routing scheme (PRS), source location privacy using randomized routes (SLP-R), and position-independent section-based scheme (PSSLP). The performance of the algorithms in terms of privacy metrics is compared in the case of pure passive attack and hybrid attack of varying intensity. The results indicate a significant degradation in the privacy protection performance of the reference algorithms in the face of the proposed hybrid attack model indicating the importance and relevance of such attacks. It is further observed that the hybrid attack can be optimized to increase the vulnerability of the existing solutions.
WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval
Venue: arXiv cs.CL 2025
Year: 2025

We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from FAQ-style schema.org annotations. In total, the data collection consists of 96 million natural question-answer (QA) pairs across 75 languages, including 47 million (49%) non-English samples. WebFAQ further serves as the foundation for 20 monolingual retrieval benchmarks with a total size of 11.2 million QA pairs (5.9 million non-English). These datasets are carefully curated through refined filtering and near-duplicate detection, yielding high-quality resources for training and evaluating multilingual dense retrieval models. To empirically confirm WebFAQ's efficacy, we use the collected QAs to fine-tune an in-domain pretrained XLM-RoBERTa model. Through this process of dataset-specific fine-tuning, the model achieves significant retrieval performance gains, which generalize - beyond WebFAQ - to other multilingual retrieval benchmarks evaluated in zero-shot setting. Last but not least, we utilize WebFAQ to construct a set of QA-aligned bilingual corpora spanning over 1000 language pairs using state-of-the-art bitext mining and automated LLM-assessed translation evaluation. Due to our advanced, automated method of bitext dataset generation, the resulting bilingual corpora demonstrate higher translation quality compared to similar datasets. WebFAQ and all associated resources are publicly available on GitHub and HuggingFace.
MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics
Venue: arXiv cs.CV 2025
Year: 2025

The rapid development of spatial transcriptomics (ST) offers new opportunities to explore the gene expression patterns within the spatial microenvironment. Current research integrates pathological images to infer gene expression, addressing the high costs and time-consuming processes to generate spatial transcriptomics data. However, as spatial transcriptomics resolution continues to improve, existing methods remain primarily focused on gene expression prediction at low-resolution spot levels. These methods face significant challenges, especially the information bottleneck, when they are applied to high-resolution HD data. To bridge this gap, this paper introduces MagNet, a multi-level attention graph network designed for accurate prediction of high-resolution HD data. MagNet employs cross-attention layers to integrate features from multi-resolution image patches hierarchically and utilizes a GAT-Transformer module to aggregate neighborhood information. By integrating multilevel features, MagNet overcomes the limitations posed by low-resolution inputs in predicting high-resolution gene expression. We systematically evaluated MagNet and existing ST prediction models on both a private spatial transcriptomics dataset and a public dataset at three different resolution levels. The results demonstrate that MagNet achieves state-of-the-art performance at both spot level and high-resolution bin levels, providing a novel methodology and benchmark for future research and applications in high-resolution HD-level spatial transcriptomics. Code is available at https://github.com/Junchao-Zhu/MagNet.
Artemis: Toward Accurate Detection of Server-Side Request Forgeries through LLM-Assisted Inter-Procedural Path-Sensitive Taint Analysis
Venue: arXiv cs.CR 2025
Year: 2025

Server-side request forgery (SSRF) vulnerabilities are inevitable in PHP web applications. Existing static tools in detecting vulnerabilities in PHP web applications neither contain SSRF-related features to enhance detection accuracy nor consider PHP's dynamic type features. In this paper, we present Artemis, a static taint analysis tool for detecting SSRF vulnerabilities in PHP web applications. First, Artemis extracts both PHP built-in and third-party functions as candidate source and sink functions. Second, Artemis constructs both explicit and implicit call graphs to infer functions' relationships. Third, Artemis performs taint analysis based on a set of rules that prevent over-tainting and pauses when SSRF exploitation is impossible. Fourth, Artemis analyzes the compatibility of path conditions to prune false positives. We have implemented a prototype of Artemis and evaluated it on 250 PHP web applications. Artemis reports 207 true vulnerable paths (106 true SSRFs) with 15 false positives. Of the 106 detected SSRFs, 35 are newly found and reported to developers, with 24 confirmed and assigned CVE IDs.
A RISC-V Multicore and GPU SoC Platform with a Qualifiable Software Stack for Safety Critical Systems
Venue: arXiv cs.AR 2025
Year: 2025

In the context of the Horizon Europe project, METASAT, a hardware platform was developed as a prototype of future space systems. The platform is based on a multiprocessor NOEL-V, an established space-grade processor, which is integrated with the SPARROW AI accelerator and connected to a GPU, Vortex. Both processing systems follow the RISC-V specification. This is a novel hardware architecture for the space domain as the use of massive parallel processing units, such as GPUs, is starting to be considered for upcoming space missions due to the increased performance required to future space-related workloads, in particular, related to AI. However, such solutions are only currently adopted for New Space, since their limitations come not only from the hardware, but also from the software, which needs to be qualified before being deployed on an institutional mission. For this reason, the METASAT platform is one of the first endeavors towards enabling the use of high performance hardware in a qualifiable environment for safety critical systems. The software stack is based on baremetal, RTEMS and the XtratuM hypervisor, providing different options for applications of various degrees of criticality.
The amplifier effect of artificial agents in social contagion
Venue: arXiv cs.SI 2025
Year: 2025

Recent advances in artificial intelligence have led to the proliferation of artificial agents in social contexts, ranging from education to online social media and financial markets, among many others. The increasing rate at which artificial and human agents interact makes it urgent to understand the consequences of human-machine interactions for the propagation of new ideas, products, and behaviors in society. Across two distinct empirical contexts, we find here that artificial agents lead to significantly faster and wider social contagion. To this end, we replicate a choice experiment previously conducted with human subjects by using artificial agents powered by large language models (LLMs). We use the experiment's results to measure the adoption thresholds of artificial agents and their impact on the spread of social contagion. We find that artificial agents tend to exhibit lower adoption thresholds than humans, which leads to wider network-based social contagions. Our findings suggest that the increased presence of artificial agents in real-world networks may accelerate behavioral shifts, potentially in unforeseen ways.
HoloMine: A Synthetic Dataset for Buried Landmines Recognition using Microwave Holographic Imaging
Venue: arXiv cs.CV 2025
Year: 2025

The detection and removal of landmines is a complex and risky task that requires advanced remote sensing techniques to reduce the risk for the professionals involved in this task. In this paper, we propose a novel synthetic dataset for buried landmine detection to provide researchers with a valuable resource to observe, measure, locate, and address issues in landmine detection. The dataset consists of 41,800 microwave holographic images (2D) and their holographic inverted scans (3D) of different types of buried objects, including landmines, clutter, and pottery objects, and is collected by means of a microwave holography sensor. We evaluate the performance of several state-of-the-art deep learning models trained on our synthetic dataset for various classification tasks. While the results do not yield yet high performances, showing the difficulty of the proposed task, we believe that our dataset has significant potential to drive progress in the field of landmine detection thanks to the accuracy and resolution obtainable using holographic radars. To the best of our knowledge, our dataset is the first of its kind and will help drive further research on computer vision methods to automatize mine detection, with the overall goal of reducing the risks and the costs of the demining process.
FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have become powerful and widely adopted in some practical applications. However, recent research has revealed their vulnerability to multimodal jailbreak attacks, whereby the model can be induced to generate harmful content, leading to safety risks. Although most MLLMs have undergone safety alignment, recent research shows that the visual modality is still vulnerable to jailbreak attacks. In our work, we discover that by using flowcharts with partially harmful information, MLLMs can be induced to provide additional harmful details. Based on this, we propose a jailbreak attack method based on auto-generated flowcharts, FC-Attack. Specifically, FC-Attack first fine-tunes a pre-trained LLM to create a step-description generator based on benign datasets. The generator is then used to produce step descriptions corresponding to a harmful query, which are transformed into flowcharts in 3 different shapes (vertical, horizontal, and S-shaped) as visual prompts. These flowcharts are then combined with a benign textual prompt to execute the jailbreak attack on MLLMs. Our evaluations on Advbench show that FC-Attack attains an attack success rate of up to 96% via images and up to 78% via videos across multiple MLLMs. Additionally, we investigate factors affecting the attack performance, including the number of steps and the font styles in the flowcharts. We also find that FC-Attack can improve the jailbreak performance from 4% to 28% in Claude-3.5 by changing the font style. To mitigate the attack, we explore several defenses and find that AdaShield can largely reduce the jailbreak performance but with the cost of utility drop.
Efficient Transformer-based Decoder for Varshamov-Tenengolts Codes
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, the rise of DNA data storage technology has brought significant attention to the challenge of correcting insertion, deletion, and substitution (IDS) errors. Among various coding methods for IDS correction, Varshamov-Tenengolts (VT) codes, primarily designed for single-error correction, have emerged as a central research focus. While existing decoding methods achieve high accuracy in correcting a single error, they often fail to correct multiple IDS errors. In this work, we observe that VT codes retain some capability for addressing multiple errors by introducing a transformer-based VT decoder (TVTD) along with symbol- and statistic-based codeword embedding. Experimental results demonstrate that the proposed TVTD achieves perfect correction of a single error. Furthermore, when decoding multiple errors across various codeword lengths, the bit error rate and frame error rate are significantly improved compared to existing hard decision and soft-in soft-out algorithms. Additionally, through model architecture optimization, the proposed method reduces time consumption by an order of magnitude compared to other soft decoders.
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
Venue: arXiv cs.CV 2025
Year: 2025

Badminton, known for having the fastest ball speeds among all sports, presents significant challenges to the field of computer vision, including player identification, court line detection, shuttlecock trajectory tracking, and player stroke-type classification. In this paper, we introduce a novel video clipping strategy to extract frames of each player's racket swing in a badminton broadcast match. These clipped frames are then processed by three existing models: one for Human Pose Estimation to obtain human skeletal joints, another for shuttlecock trajectory tracking, and the other for court line detection to determine player positions on the court. Leveraging these data as inputs, we propose Badminton Stroke-type Transformer (BST) to classify player stroke-types in singles. To the best of our knowledge, experimental results demonstrate that our method outperforms the previous state-of-the-art on the largest publicly available badminton video dataset (ShuttleSet), another badminton dataset (BadmintonDB), and a tennis dataset (TenniSet). These results suggest that effectively leveraging ball trajectory is a promising direction for action recognition in racket sports.
Cryptis: Cryptographic Reasoning in Separation Logic
Venue: arXiv cs.CR 2025
Year: 2025

We introduce Cryptis, an extension of the Iris separation logic that can be used to verify cryptographic components using the symbolic model of cryptography. The combination of separation logic and cryptographic reasoning allows us to prove the correctness of a protocol and later reuse this result to verify larger systems that rely on the protocol. To make this integration possible, we propose novel specifications for authentication protocols that allow agents in a network to agree on the use of system resources. We evaluate our approach by verifying various authentication protocols and a key-value store server that uses these authentication protocols to connect to clients. Our results are formalized in Coq.