OpenRaiser/Intern-Atlas huggingface dataset:
Role-SynthCLIP: A Role Play Driven Diverse Synthetic Data Approach
Venue: arXiv cs.CV 2025
Year: 2025

The effectiveness of Contrastive Language-Image Pre-training (CLIP) models critically depends on the semantic diversity and quality of their training data. However, while existing synthetic data generation methods primarily focus on increasing data volume, such emphasis often leads to limited semantic diversity and redundant or shallow captions. To address this limitation, we propose Role-SynthCLIP, a novel data synthesis framework that leverages multi-perspective role-playing prompts (e.g., a compositional analyst, an interpreter of image context) to guide Multimodal Large Language Models (MLLMs) in generating semantically diverse captions from distinct viewpoints. This mechanism enhances the semantic diversity and fine-grained image-text alignment of synthetic pairs, thereby improving caption expressiveness and accuracy while keeping the total number of image-text pairs unchanged. Experimental results demonstrate the effectiveness and efficiency of our method. A CLIP-B/16 model trained on only 1 million Role-SynthCLIP pairs achieves a Recall@1 of 64.1% on the MS COCO validation set, surpassing the best existing synthetic data baseline (trained on 5M pairs) by 2.8 percentage points. The code and trained models are released at https://github.com/huangfu170/Role-SynthCLIP.
FM4Com: Foundation Model for Scene-Adaptive Communication Strategy Optimization
Venue: arXiv cs.HC 2025
Year: 2025

The emergence of sixth-generation (6G) networks heralds an intelligent communication ecosystem driven by AI-native air interfaces. However, current physical-layer designs-typically following modular and isolated optimization paradigms-fail to achieve global end-to-end optimality due to neglected inter-module dependencies. Although large language models (LLMs) have recently been applied to communication tasks such as beam prediction and resource allocation, existing studies remain limited to single-task or single-modality scenarios and lack the ability to jointly reason over communication states and user intents for personalized strategy adaptation. To address these limitations, this paper proposes a novel multimodal communication decision-making model based on reinforcement learning. The proposed model semantically aligns channel state information (CSI) and textual user instructions, enabling comprehensive understanding of both physical-layer conditions and communication intents. It then generates physically realizable, user-customized link construction strategies that dynamically adapt to changing environments and preference tendencies. A two-stage reinforcement learning framework is employed: the first stage expands the experience pool via heuristic exploration and behavior cloning to obtain a near-optimal initialization, while the second stage fine-tunes the model through multi-objective reinforcement learning considering bit error rate, throughput, and complexity. Experimental results demonstrate that the proposed model significantly outperforms conventional planning-based algorithms under challenging channel conditions, achieving robust, efficient, and personalized 6G link construction.
Confidentiality in a Card-Based Protocol Under Repeated Biased Shuffles
Venue: arXiv cs.CR 2025
Year: 2025

In this paper, we provide a probabilistic analysis of the confidentiality in a card-based protocol. We focus on Bert den Boer's original Five Card Trick to develop our approach. Five Card Trick was formulated as a secure two-party computation method, where two players use colored cards with identical backs to calculate the logical AND operation on the bits that they choose. In this method, the players first arrange the cards privately, and then shuffle them through a random cut. Finally, they reveal the shuffled arrangement to determine the result of the operation. An unbiased random cut is essential to prevent players from exposing their chosen bits to each other. However, players typically choose to move cards within the deck even though not moving any cards should be equally likely. This unconscious behavior results in a biased, nonuniform shuffling-distribution in the sense that some arrangements of cards are slightly more probable after the cut. Such a nonuniform distribution creates an opportunity for a malicious player to gain advantage in guessing the other player's choice. We provide the conditional probabilities of such guesses as a way to quantify the information leakage. Furthermore, we utilize the eigenstructure of a Markov chain to derive tight bounds on the number of times the biased random cuts must be repeated to reduce the leakage to an acceptable level. We also discuss the generalization of our approach to the setting where shuffling is conducted by a malicious player.
Improving Injection-Throttling Mechanisms for Congestion Control for Data-center and Supercomputer Interconnects
Venue: arXiv cs.NI 2025
Year: 2025

Over the past decade, Supercomputers and Data centers have evolved dramatically to cope with the increasing performance requirements of applications and services, such as scientific computing, generative AI, social networks or cloud services. This evolution have led these systems to incorporate high-speed networks using faster links, end nodes using multiple and dedicated accelerators, or a advancements in memory technologies to bridge the memory bottleneck. The interconnection network is a key element in these systems and it must be thoroughly designed so it is not the bottleneck of the entire system, bearing in mind the countless communication operations that generate current applications and services. Congestion is serious threat that spoils the interconnection network performance, and its effects are even more dramatic when looking at the traffic dynamics and bottlenecks generated by the communication operations mentioned above. In this vein, numerous congestion control (CC) techniques have been developed to address congestion negative effects. One popular example is Data Center Quantized Congestion Notification (DCQCN), which allows congestion detection at network switch buffers, then marking congesting packets and notifying about congestion to the sources, which finally apply injection throttling of those packets contributing to congestion. While DCQCN has been widely studied and improved, its main principles for congestion detection, notification and reaction remain largely unchanged, which is an important shortcoming considering congestion dynamics in current high-performance interconnection networks. In this paper, we revisit the DCQCN closed-loop mechanism and refine its design to leverage a more accurate congestion detection, signaling, and injection throttling, reducing control traffic overhead and avoiding unnecessary throttling of non-congesting flows.
SmartSecChain-SDN: A Blockchain-Integrated Intelligent Framework for Secure and Efficient Software-Defined Networks
Venue: arXiv cs.CR 2025
Year: 2025

With more and more existing networks being transformed to Software-Defined Networking (SDN), they need to be more secure and demand smarter ways of traffic control. This work, SmartSecChain-SDN, is a platform that combines machine learning based intrusion detection, blockchain-based storage of logs, and application-awareness-based priority in SDN networks. To detect network intrusions in a real-time, precision and low-false positives setup, the framework utilizes the application of advanced machine learning algorithms, namely Random Forest, XGBoost, CatBoost, and CNN-BiLSTM. SmartSecChain-SDN is based on the Hyperledger Fabric, which is a permissioned blockchain technology, to provide secure, scalable, and privacy-preserving storage and, thus, guarantee that the Intrusion Detection System (IDS) records cannot be altered and can be analyzed comprehensively. The system also has Quality of Service (QoS) rules and traffic shaping based on applications, which enables prioritization of critical services, such as VoIP, video conferencing, and business applications, as well as de-prioritization of non-essential traffic, such as downloads and updates. Mininet can simulate real-time SDN scenarios because it is used to prototype whole architectures. It is also compatible with controllers OpenDaylight and Ryu. It has tested the framework using the InSDN dataset and proved that it can identify different kinds of cyberattacks and handle bandwidth allocation efficiently under circumstances of resource constraints. SmartSecChain-SDN comprehensively addresses SDN system protection, securing and enhancing. The proposed study offers an innovative, extensible way to improve cybersecurity, regulatory compliance, and the administration of next-generation programmable networks.
Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy
Venue: arXiv cs.LG 2025
Year: 2025

Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patients. Predicting progression-free survival (PFS) could support individualized treatment planning. This study evaluates laboratory, imaging, and multimodal deep learning models for PFS prediction in PRRT-treated patients. In this retrospective, single-center study 116 patients with metastatic NETs undergoing 177Lu-DOTATOC were included. Clinical characteristics, laboratory values, and pretherapeutic somatostatin receptor positron emission tomography/computed tomographies (SR-PET/CT) were collected. Seven models were trained to classify low- vs. high-PFS groups, including unimodal (laboratory, SR-PET, or CT) and multimodal fusion approaches. Explainability was evaluated by feature importance analysis and gradient maps. Forty-two patients (36%) had short PFS (< 1 year), 74 patients long PFS (>1 year). Groups were similar in most characteristics, except for higher baseline chromogranin A (p = 0.003), elevated gamma-GT (p = 0.002), and fewer PRRT cycles (p < 0.001) in short-PFS patients. The Random Forest model trained only on laboratory biomarkers reached an AUROC of 0.59 +- 0.02. Unimodal three-dimensional convolutional neural networks using SR-PET or CT performed worse (AUROC 0.42 +- 0.03 and 0.54 +- 0.01, respectively). A multimodal fusion model laboratory values, SR-PET, and CT -augmented with a pretrained CT branch - achieved the best results (AUROC 0.72 +- 0.01, AUPRC 0.80 +- 0.01). Multimodal deep learning combining SR-PET, CT, and laboratory biomarkers outperformed unimodal approaches for PFS prediction after PRRT. Upon external validation, such models may support risk-adapted follow-up strategies.
Model Merging Improves Zero-Shot Generalization in Bioacoustic Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

Foundation models capable of generalizing across species and tasks represent a promising new frontier in bioacoustics, with NatureLM being one of the most prominent examples. While its domain-specific fine-tuning yields strong performance on bioacoustic benchmarks, we observe that it also introduces trade-offs in instruction-following flexibility. For instance, NatureLM achieves high accuracy when prompted for either the common or scientific name individually, but its accuracy drops significantly when both are requested in a single prompt. We address this by applying a simple model merging strategy that interpolates NatureLM with its base language model, recovering instruction-following capabilities with minimal loss of domain expertise. Finally, we show that the merged model exhibits markedly stronger zero-shot generalization, achieving over a 200% relative improvement and setting a new state-of-the-art in closed-set zero-shot classification of unseen species.
Deterministic list decoding of Reed-Solomon codes
Venue: arXiv cs.CC 2025
Year: 2025

We show that Reed-Solomon codes of dimension $k$ and block length $n$ over any finite field $\mathbb{F}$ can be deterministically list decoded from agreement $\sqrt{(k-1)n}$ in time $\text{poly}(n, \log |\mathbb{F}|)$. Prior to this work, the list decoding algorithms for Reed-Solomon codes, from the celebrated results of Sudan and Guruswami-Sudan, were either randomized with time complexity $\text{poly}(n, \log |\mathbb{F}|)$ or were deterministic with time complexity depending polynomially on the characteristic of the underlying field. In particular, over a prime field $\mathbb{F}$, no deterministic algorithms running in time $\text{poly}(n, \log |\mathbb{F}|)$ were known for this problem. Our main technical ingredient is a deterministic algorithm for solving the bivariate polynomial factorization instances that appear in the algorithm of Sudan and Guruswami-Sudan with only a $\text{poly}(\log |\mathbb{F}|)$ dependence on the field size in its time complexity for every finite field $\mathbb{F}$. While the question of obtaining efficient deterministic algorithms for polynomial factorization over finite fields is a fundamental open problem even for univariate polynomials of degree $2$, we show that additional information from the received word can be used to obtain such an algorithm for instances that appear in the course of list decoding Reed-Solomon codes.
Evaluating Spatio-Temporal Forecasting Trade-offs Between Graph Neural Networks and Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

Modern IoT deployments for environmental sensing produce high volume spatiotemporal data to support downstream tasks such as forecasting, typically powered by machine learning models. While existing filtering and strategic deployment techniques optimize collected data volume at the edge, they overlook how variations in sampling frequencies and spatial coverage affect downstream model performance. In many forecasting models, incorporating data from additional sensors denoise predictions by providing broader spatial contexts. This interplay between sampling frequency, spatial coverage and different forecasting model architectures remain underexplored. This work presents a systematic study of forecasting models - classical models (VAR), neural networks (GRU, Transformer), spatio-temporal graph neural networks (STGNNs), and time series foundation models (TSFMs: Chronos Moirai, TimesFM) under varying spatial sensor nodes density and sampling intervals using real-world temperature data in a wireless sensor network. Our results show that STGNNs are effective when sensor deployments are sparse and sampling rate is moderate, leveraging spatial correlations via encoded graph structure to compensate for limited coverage. In contrast, TSFMs perform competitively at high frequencies but degrade when spatial coverage from neighboring sensors is reduced. Crucially, the multivariate TSFM Moirai outperforms all models by natively learning cross-sensor dependencies. These findings offer actionable insights for building efficient forecasting pipelines in spatio-temporal systems. All code for model configurations, training, dataset, and logs are open-sourced for reproducibility: https://github.com/UIUC-MONET-Projects/Benchmarking-Spatiotemporal-Forecast-Models
Autonomous generation of different courses of action in mechanized combat operations
Venue: arXiv cs.AI 2025
Year: 2025

In this paper, we propose a methodology designed to support decision-making during the execution phase of military ground combat operations, with a focus on one's actions. This methodology generates and evaluates recommendations for various courses of action for a mechanized battalion, commencing with an initial set assessed by their anticipated outcomes. It systematically produces thousands of individual action alternatives, followed by evaluations aimed at identifying alternative courses of action with superior outcomes. These alternatives are appraised in light of the opponent's status and actions, considering unit composition, force ratios, types of offense and defense, and anticipated advance rates. Field manuals evaluate battle outcomes and advancement rates. The processes of generation and evaluation work concurrently, yielding a variety of alternative courses of action. This approach facilitates the management of new course generation based on previously evaluated actions. As the combat unfolds and conditions evolve, revised courses of action are formulated for the decision-maker within a sequential decision-making framework.
Effectiveness of Chain-of-Thought in Distilling Reasoning Capability from Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Chain-of-Thought (CoT) prompting is a widely used method to improve the reasoning capability of Large Language Models (LLMs). More recently, CoT has been leveraged in Knowledge Distillation (KD) to transfer reasoning capability from a larger LLM to a smaller one. This paper examines the role of CoT in distilling the reasoning capability from larger LLMs to smaller LLMs using white-box KD, analysing its effectiveness in improving the performance of the distilled models for various natural language reasoning and understanding tasks. We conduct white-box KD experiments using LLMs from the Qwen and Llama2 families, employing CoT data from the CoT-Collection dataset. The distilled models are then evaluated on natural language reasoning and understanding tasks from the BIG-Bench-Hard (BBH) benchmark, which presents complex challenges for smaller LLMs. Experimental results demonstrate the role of CoT in improving white-box KD effectiveness, enabling the distilled models to achieve better average performance in natural language reasoning and understanding tasks from BBH.
Procedimiento de auditoría de ciberseguridad para sistemas autónomos: metodología, amenazas y mitigaciones
Venue: arXiv cs.RO 2025
Year: 2025

The deployment of autonomous systems has experienced remarkable growth in recent years, driven by their integration into sectors such as industry, medicine, logistics, and domestic environments. This expansion is accompanied by a series of security issues that entail significant risks due to the critical nature of autonomous systems, especially those operating in human-interaction environments. Furthermore, technological advancement and the high operational and architectural complexity of autonomous systems have resulted in an increased attack surface. This article presents a specific security auditing procedure for autonomous systems, based on a layer-structured methodology, a threat taxonomy adapted to the robotic context, and a set of concrete mitigation measures. The validity of the proposed approach is demonstrated through four practical case studies applied to representative robotic platforms: the Vision 60 military quadruped from Ghost Robotics, the A1 robot from Unitree Robotics, the UR3 collaborative arm from Universal Robots, and the Pepper social robot from Aldebaran Robotics.
BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services
Venue: arXiv cs.CR 2025
Year: 2025

With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using multiple network flows. To transcend this limitation, we propose a novel unsupervised traffic anomaly detection system, BLADE, capable of detecting not only flow-level but also behavior-level attacks in web services. Our key observation is that application-layer operations of web services exhibit distinctive communication patterns at the network layer from a multi-flow perspective. BLADE first exploits a flow autoencoder to learn a latent feature representation and calculates its reconstruction losses per flow. Then, the latent representation is assigned a pseudo operation label using an unsupervised clustering method. Next, an anomaly score is computed based on the reconstruction losses. Finally, the triplets of timestamps, pseudo labels, and anomaly scores from multiple flows are aggregated and fed into a one-class classifier to characterize the behavior patterns of legitimate web operations, enabling the detection of flow-level and behavior-level anomalies. BLADE is extensively evaluated on both the custom dataset and the CIC-IDS2017 dataset. The experimental results demonstrate BLADE's superior performance, achieving high F1 scores of 0.9732 and 0.9801, respectively, on the two datasets, and outperforming traditional single-flow anomaly detection baselines.
ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy
Venue: arXiv cs.LG 2025
Year: 2025

Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $α$-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they become inoperable without a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features characterizing activity patterns. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC = 0.95). Generalization was confirmed on a blinded local test set (n = 31, AUROC = 0.86) and on two independent external cohorts (n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess real-world robustness, leave-one-dataset-out cross-validation across the internal and external cohorts demonstrated consistent performance (AUROC range = 0.84-0.89). A complementary stability analysis showed that key predictive features remained reproducible across datasets, supporting the final pooled multi-center model as a robust pre-trained resource for broader deployment. By being open-source and easy to use, our tool promotes widespread adoption and facilitates independent validation and collaborative improvements, thereby advancing the field toward a unified and generalizable RBD detection model using wearable devices.
What Are the Facts? Automated Extraction of Court-Established Facts from Criminal-Court Opinions
Venue: arXiv cs.CL 2025
Year: 2025

Criminal justice administrative data contain only a limited amount of information about the committed offense. However, there is an unused source of extensive information in continental European courts' decisions: descriptions of criminal behaviors in verdicts by which offenders are found guilty. In this paper, we study the feasibility of extracting these descriptions from publicly available court decisions from Slovakia. We use two different approaches for retrieval: regular expressions and large language models (LLMs). Our baseline was a simple method employing regular expressions to identify typical words occurring before and after the description. The advanced regular expression approach further focused on "sparing" and its normalization (insertion of spaces between individual letters), typical for delineating the description. The LLM approach involved prompting the Gemini Flash 2.0 model to extract the descriptions using predefined instructions. Although the baseline identified descriptions in only 40.5% of verdicts, both methods significantly outperformed it, achieving 97% with advanced regular expressions and 98.75% with LLMs, and 99.5% when combined. Evaluation by law students showed that both advanced methods matched human annotations in about 90% of cases, compared to just 34.5% for the baseline. LLMs fully matched human-labeled descriptions in 91.75% of instances, and a combination of advanced regular expressions with LLMs reached 92%.