OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Discrete-time Model of Information Diffusion on Social Networks Considering Users Behavior
Venue: arXiv cs.SI 2025
Year: 2025

In this paper, we introduce the SDIR (Susceptible-Delayable-Infected-Recovered) model, an extension of the classical SIR epidemic framework, to provide a more explicit characterization of user behavior in online social networks. The newly merged state D (delayable) represents users who have received the information but delayed its spreading and may eventually choose not to share it at all. Based on the mean-field approximation method, we derive the dynamical equations of the model and investigate its convergence and stability conditions. Under these conditions, we further propose an approximation algorithm for the edge-deletion problem, aiming to minimize the influence of information diffusion by identifying approximate solutions.
Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines
Venue: arXiv cs.RO 2025
Year: 2025

In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is a finite-state machine (FSM)-based controller that uses encounter-triggered transitions to create rigid, stable walls. The second integrates FSM states with a Deep Q-Network (DQN), dynamically optimizing separation through emergent "demilitarized zones." In simulation, both controllers reduce mixing between subgroups, with the DQN-enhanced controller improving adaptability and reducing mixing by 40-50% while achieving faster convergence.
Finding the Needle in the Crash Stack: Industrial-Scale Crash Root Cause Localization with AutoCrashFL
Venue: arXiv cs.SE 2025
Year: 2025

Fault Localization (FL) aims to identify root causes of program failures. FL typically targets failures observed from test executions, and as such, often involves dynamic analyses to improve accuracy, such as coverage profiling or mutation testing. However, for large industrial software, measuring coverage for every execution is prohibitively expensive, making the use of such techniques difficult. To address these issues and apply FL in an industrial setting, this paper proposes AutoCrashFL, an LLM agent for the localization of crashes that only requires the crashdump from the Program Under Test (PUT) and access to the repository of the corresponding source code. We evaluate AutoCrashFL against real-world crashes of SAP HANA, an industrial software project consisting of more than 35 million lines of code. Experiments reveal that AutoCrashFL is more effective in localization, as it identified 30% crashes at the top, compared to 17% achieved by the baseline. Through thorough analysis, we find that AutoCrashFL has attractive practical properties: it is relatively more effective for complex bugs, and it can indicate confidence in its results. Overall, these results show the practicality of LLM agent deployment on an industrial scale.
ZK Coprocessor Bridge: Replay-Safe Private Execution from Solana to Aztec via Wormhole
Venue: arXiv cs.CR 2025
Year: 2025

We formalize a cross-domain "ZK coprocessor bridge" that lets Solana programs request private execution on Aztec L2 (via Ethereum) using Wormhole Verifiable Action Approvals (VAAs) as authenticated transport. The system comprises: (i) a Solana program that posts messages to Wormhole Core with explicit finality; (ii) an EVM Portal that verifies VAAs, enforces a replay lock, parses a bound payload secretHash||m from the attested VAA, derives a domain-separated field commitment, and enqueues an L1->L2 message into the Aztec Inbox (our reference implementation v0.1.0 currently uses consumeWithSecret(vaa, secretHash); we provide migration guidance to the payload-bound interface); (iii) a minimal Aztec contract that consumes the message privately; and (iv) an off-chain relayer that ferries VAAs and can record receipts on Solana. We present state machines, message formats, and proof sketches for replay-safety, origin authenticity, finality alignment, parameter binding (no relayer front-running of Aztec parameters), privacy, idempotence, and liveness. Finally, we include a concise Reproducibility note with pinned versions and artifacts to replicate a public testnet run.
SPIRAL: Self-Play Incremental Racing Algorithm for Learning in Multi-Drone Competitions
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces SPIRAL (Self-Play Incremental Racing Algorithm for Learning), a novel approach for training autonomous drones in multi-agent racing competitions. SPIRAL distinctively employs a self-play mechanism to incrementally cultivate complex racing behaviors within a challenging, dynamic environment. Through this self-play core, drones continuously compete against increasingly proficient versions of themselves, naturally escalating the difficulty of competitive interactions. This progressive learning journey guides agents from mastering fundamental flight control to executing sophisticated cooperative multi-drone racing strategies. Our method is designed for versatility, allowing integration with any state-of-the-art Deep Reinforcement Learning (DRL) algorithms within its self-play framework. Simulations demonstrate the significant advantages of SPIRAL and benchmark the performance of various DRL algorithms operating within it. Consequently, we contribute a versatile, scalable, and self-improving learning framework to the field of autonomous drone racing. SPIRAL's capacity to autonomously generate appropriate and escalating challenges through its self-play dynamic offers a promising direction for developing robust and adaptive racing strategies in multi-agent environments. This research opens new avenues for enhancing the performance and reliability of autonomous racing drones in increasingly complex and competitive scenarios.
RoGER-SLAM: A Robust Gaussian Splatting SLAM System for Noisy and Low-light Environment Resilience
Venue: arXiv cs.RO 2025
Year: 2025

The reliability of Simultaneous Localization and Mapping (SLAM) is severely constrained in environments where visual inputs suffer from noise and low illumination. Although recent 3D Gaussian Splatting (3DGS) based SLAM frameworks achieve high-fidelity mapping under clean conditions, they remain vulnerable to compounded degradations that degrade mapping and tracking performance. A key observation underlying our work is that the original 3DGS rendering pipeline inherently behaves as an implicit low-pass filter, attenuating high-frequency noise but also risking over-smoothing. Building on this insight, we propose RoGER-SLAM, a robust 3DGS SLAM system tailored for noise and low-light resilience. The framework integrates three innovations: a Structure-Preserving Robust Fusion (SP-RoFusion) mechanism that couples rendered appearance, depth, and edge cues; an adaptive tracking objective with residual balancing regularization; and a Contrastive Language-Image Pretraining (CLIP)-based enhancement module, selectively activated under compounded degradations to restore semantic and structural fidelity. Comprehensive experiments on Replica, TUM, and real-world sequences show that RoGER-SLAM consistently improves trajectory accuracy and reconstruction quality compared with other 3DGS-SLAM systems, especially under adverse imaging conditions.
End-to-end Learning of Probabilistic and Geometric Constellation Shaping with Iterative Receivers
Venue: arXiv cs.IT 2025
Year: 2025

An end-to-end learning method for constellation shaping with a shaping-encoder assisted transceiver architecture is presented. The shaping encoder, which produces shaping bits with a higher probability of zeros, is used to produce an efficient symbol probability distribution. Both the probability distribution and the constellation geometry are jointly optimized, using end-to-end learning. Optimized constellations are evaluated using two iterative receiver architectures. Bit error rate (BER) performance gain is quantified against standard amplitude phase-shift keying (APSK) and quadrature amplitude modulation (QAM) constellations. A maximum BER gain of 0.3 dB and 0.15 dB are observed under two receivers for the learned constellations compared to standard APSK or QAM. The basic approach is extended to incorporate the full iterative detection and decoding loop, using the deep unfolding technique. A bit error rate gain of 0.1 dB is observed for the iterative scheme with learned constellations under block fading channel conditions, when compared to standard APSK.
DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection
Venue: arXiv cs.CR 2025
Year: 2025

The misuse of advanced generative AI models has resulted in the widespread proliferation of falsified data, particularly forged human-centric audiovisual content, which poses substantial societal risks (e.g., financial fraud and social instability). In response to this growing threat, several works have preliminarily explored countermeasures. However, the lack of sufficient and diverse training data, along with the absence of a standardized benchmark, hinder deeper exploration. To address this challenge, we first build Mega-MMDF, a large-scale, diverse, and high-quality dataset for multimodal deepfake detection. Specifically, we employ 21 forgery pipelines through the combination of 10 audio forgery methods, 12 visual forgery methods, and 6 audio-driven face reenactment methods. Mega-MMDF currently contains 0.1 million real samples and 1.1 million forged samples, making it one of the largest and most diverse multimodal deepfake datasets, with plans for continuous expansion. Building on it, we present DeepfakeBench-MM, the first unified benchmark for multimodal deepfake detection. It establishes standardized protocols across the entire detection pipeline and serves as a versatile platform for evaluating existing methods as well as exploring novel approaches. DeepfakeBench-MM currently supports 5 datasets and 11 multimodal deepfake detectors. Furthermore, our comprehensive evaluations and in-depth analyses uncover several key findings from multiple perspectives (e.g., augmentation, stacked forgery). We believe that DeepfakeBench-MM, together with our large-scale Mega-MMDF, will serve as foundational infrastructures for advancing multimodal deepfake detection.
Robust Atypical Mitosis Classification with DenseNet121: Stain-Aware Augmentation and Hybrid Loss for Domain Generalization
Venue: arXiv cs.CV 2025
Year: 2025

Atypical mitotic figures are important biomarkers of tumor aggressiveness in histopathology, yet reliable recognition remains challenging due to severe class imbalance and variability across imaging domains. We present a DenseNet-121-based framework tailored for atypical mitosis classification in the MIDOG 2025 (Track 2) setting. Our method integrates stain-aware augmentation (Macenko), geometric and intensity transformations, and imbalance-aware learning via weighted sampling with a hybrid objective combining class-weighted binary cross-entropy and focal loss. Trained end-to-end with AdamW and evaluated across multiple independent domains, the model demonstrates strong generalization under scanner and staining shifts, achieving balanced accuracy 85.0%, AUROC 0.927, sensitivity 89.2%, and specificity 80.9% on the official test set. These results indicate that combining DenseNet-121 with stain-aware augmentation and imbalance-adaptive objectives yields a robust, domain-generalizable framework for atypical mitosis classification suitable for real-world computational pathology workflows.
Culturally Grounded Physical Commonsense Reasoning in Italian and English: A Submission to the MRL 2025 Shared Task
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents our submission to the MRL 2025 Shared Task on Multilingual Physical Reasoning Datasets. The objective of the shared task is to create manually-annotated evaluation data in the physical commonsense reasoning domain, for languages other than English, following a format similar to PIQA. Our contribution, FormaMentis, is a novel benchmark for physical commonsense reasoning that is grounded in Italian language and culture. The data samples in FormaMentis are created by expert annotators who are native Italian speakers and are familiar with local customs and norms. The samples are additionally translated into English, while preserving the cultural elements unique to the Italian context.
Influence of Network Topology and Vaccination Strategies on HPV Dynamics: A Simulation Study Using the SeCoNet Growth Model
Venue: arXiv cs.SI 2025
Year: 2025

This study examines how contact network topology influences the effectiveness of vaccination programs in the context of human papillomavirus (HPV) transmission. Using the SeCoNet sexual contact network growth model, we evaluate age based, ring based, and several centrality based vaccination strategies across the overall, male, and female cohorts, focusing on peak incidence, timing of peak prevalence, and cumulative incidence. The simulations show that degree, betweenness, and percolation centrality based strategies are generally the most effective, while ring vaccination achieves the greatest reduction in cumulative incidence among females. Network topology also plays a critical role: higher average degree reduces vaccination effectiveness, whereas higher power-law exponent, longer average shortest path length, and stronger clustering improve vaccination outcomes. The results highlight the importance of incorporating network structure into the design of HPV vaccination programs.
A Critical Study on Tea Leaf Disease Detection using Deep Learning Techniques
Venue: arXiv cs.CV 2025
Year: 2025

The proposed solution is Deep Learning Technique that will be able classify three types of tea leaves diseases from which two diseases are caused by the pests and one due to pathogens (infectious organisms) and environmental conditions and also show the area damaged by a disease in leaves. Namely Red Rust, Helopeltis and Red spider mite respectively. In this paper we have evaluated two models namely SSD MobileNet V2 and Faster R-CNN ResNet50 V1 for the object detection. The SSD MobileNet V2 gave precision of 0.209 for IOU range of 0.50:0.95 with recall of 0.02 on IOU 0.50:0.95 and final mAP of 20.9%. While Faster R-CNN ResNet50 V1 has precision of 0.252 on IOU range of 0.50:0.95 and recall of 0.044 on IOU of 0.50:0.95 with a mAP of 25%, which is better than SSD. Also used Mask R-CNN for Object Instance Segmentation where we have implemented our custom method to calculate the damaged diseased portion of leaves. Keywords: Tea Leaf Disease, Deep Learning, Red Rust, Helopeltis and Red Spider Mite, SSD MobileNet V2, Faster R-CNN ResNet50 V1 and Mask RCNN.
If You Want to Be Robust, Be Wary of Initialization
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) have demonstrated remarkable performance across a spectrum of graph-related tasks, however concerns persist regarding their vulnerability to adversarial perturbations. While prevailing defense strategies focus primarily on pre-processing techniques and adaptive message-passing schemes, this study delves into an under-explored dimension: the impact of weight initialization and associated hyper-parameters, such as training epochs, on a model's robustness. We introduce a theoretical framework bridging the connection between initialization strategies and a network's resilience to adversarial perturbations. Our analysis reveals a direct relationship between initial weights, number of training epochs and the model's vulnerability, offering new insights into adversarial robustness beyond conventional defense mechanisms. While our primary focus is on GNNs, we extend our theoretical framework, providing a general upper-bound applicable to Deep Neural Networks. Extensive experiments, spanning diverse models and real-world datasets subjected to various adversarial attacks, validate our findings. We illustrate that selecting appropriate initialization not only ensures performance on clean datasets but also enhances model robustness against adversarial perturbations, with observed gaps of up to 50\% compared to alternative initialization approaches.
Synthesis of State-Attack Strategies for Anonymity and Opacity Violation in Discrete Event Systems
Venue: arXiv cs.FL 2025
Year: 2025

Attacks, including the manipulation of sensor readings and the modification of actuator commands, pose a significant challenge to the security and privacy of automated systems. This paper considers discrete event systems that can be modeled with nondeterministic finite state automata that are susceptible to state attacks. A state attack allows an intruder to learn whether or not the current state of a system falls into certain subsets of states. The intruder has a limited total number of state attacks at its disposal, but can launch state attacks at arbitrary instants of its choosing. We are interested on violations of current-state anonymity (resp. opacity), i.e., situations where the intruder, based on the sequence of observations generated by the system and the outcome of any performed state attacks, can ascertain the exact current state of the system (resp. that the current state of the system definitely resides in a subset of secret states). When the system violates current-state anonymity (resp. opacity) under a bounded number of state attacks, a subsequent question is whether the intruder can design an attack strategy such that anonymity-violating (resp. opacity-violating) situations will always be reached. In this latter case, we also design an attack strategy that guarantees that the system will reach a violating situation regardless of system actions. We provide pertinent complexity analysis of the corresponding verification algorithms and examples to illustrate the proposed methods.
RejSCore: Rejection Sampling Core for Multivariate-based Public key Cryptography
Venue: arXiv cs.CR 2025
Year: 2025

Post-quantum multivariate public key cryptography (MPKC) schemes resist quantum threats but require heavy operations, such as rejection sampling, which challenge resource-limited devices. Prior hardware designs have addressed various aspects of MPKC signature generation. However, rejection sampling remains largely unexplored in such contexts. This paper presents RejSCore, a lightweight hardware accelerator for rejection sampling in post-quantum cryptography. It specifically targets the QR-UOV scheme, which is a prominent candidate under the second-round of the National Institute of Standards and Technology (NIST) additional digital signature standardization process. The architecture includes an AES-CTR-128-based pseudorandom number generator. Moreover, a lightweight iterative method is employed in rejection sampling, offering reduced resource consumption and area overhead while slightly increasing latency. The performance of RejSCore is comprehensively evaluated on Artix-7 FPGAs and 65 nm CMOS technology using the Area-Delay Product (ADP) and Power-Delay Product (PDP). On Artix-7 and 65 nm CMOS, RejSCore achieves an area of 2042 slices and 464,866~$μm^2$, with operating frequencies of 222 MHz and 565 MHz, respectively. Using the QR-UOV parameters for security level I ($q = 127$, $v = 156$, $m = 54$, $l = 3$), the core completes its operation in 8525 clock cycles. The ADP and PDP evaluations confirm RejSCore's suitability for deployment in resource-constrained and security-critical environments.
SARVLM: A Vision Language Foundation Model for Semantic Understanding and Target Recognition in SAR Imagery
Venue: arXiv cs.CV 2025
Year: 2025

Synthetic Aperture Radar (SAR) is a crucial imaging modality thanks to its all-weather capability. Although recent advances in self-supervised learning and masked image modeling (MIM) have enabled SAR foundation models, these methods largely emphasize low-level visual features and often overlook multimodal alignment and zero-shot target recognition in SAR imagery. To address this, we construct SARVLM-1M, a large-scale vision-language dataset with over one million image-text pairs aggregated from existing datasets. We further propose a domain transfer training strategy to mitigate the large gap between natural and SAR imagery. Building on this, we develop SARVLM, the first vision language foundation model (VLM) tailored to SAR, comprising SARCLIP and SARCap. SARVLM is trained with a vision-language contrastive objective under the proposed domain transfer strategy, bridging SAR imagery and textual descriptions. Extensive experiments on image text retrieval, zero-shot classification, semantic localization, and imagery captioning demonstrate that SARVLM delivers superior feature extraction and interpretation, outperforming state-of-the-art VLMs and advancing SAR semantic understanding. Code and datasets will be released soon.
Graph-Theoretic Characterization of Noise Capacity of Conditional Disclosure of Secrets
Venue: arXiv cs.IT 2025
Year: 2025

In the Conditional Disclosure of Secrets (CDS) problem, Alice and Bob hold inputs $x\in \mathcal{X}$ and $y\in \mathcal{Y}$ and share a secret. Let $f:\mathcal{X}\times\mathcal{Y}\to\{0,1\}$ be a function such that the secret is revealed to a third party, Carol, if and only if $f(x,y)=1$. To protect the secret when $f(x,y)=0$, Alice and Bob share a common noise variable unknown to Carol. We study the \emph{noise capacity} of CDS, defined as the maximum number of secret bits that can be securely revealed per noise bit. We first derive necessary and sufficient conditions on $f$, represented by a CDS graph, for the extremal case where the noise capacity equals $1$. We then develop converse bounds on the noise rate for all linear schemes: $\frac{(ρ-1)(d-1)}{ρd-1}$ if $ρ$ is finite, and $\frac{d-1}{d}$ if $ρ$ is infinite, where $ρ$ is the covering parameter of the CDS graph and $d$ is the number of unqualified edges in an unqualified path. Under maximal communication efficiency (message size equals secret size), we refine these bounds by analyzing qualified components and their connections. Achievability is shown for CDS instances with cyclic qualified edges and a single unqualified path. This graph-theoretic framework links noise efficiency limits to the unqualified path distance and covering parameter, providing a systematic method to analyze CDS under arbitrary graph topologies.
SALSA: Single-pass Autoregressive LLM Structured Classification
Venue: arXiv cs.CL 2025
Year: 2025

Despite their impressive generalization capabilities, instruction-tuned Large Language Models often underperform on text classification benchmarks. We introduce SALSA, a coherent pipeline that combines structured prompting, class-to-token mapping, and parameter-efficient fine-tuning, thereby avoiding cold-start training. Each class label is mapped to a distinct output token, and prompts are constructed to elicit a single-token response. During inference, the model's output is projected only onto the logits of the relevant class tokens, enabling efficient and accurate classification in a single forward pass. SALSA achieves state-of-the-art results across diverse benchmarks, demonstrating its robustness and scalability for LLM-based classification applications.