OpenRaiser/Intern-Atlas huggingface dataset:
Checkstyle+: Reducing Technical Debt Through The Use of Linters with LLMs
Venue: arXiv cs.SE 2025
Year: 2025

Good code style improves program readability, maintainability, and collaboration, and is an integral component of software quality. Developers, however, often cut corners when following style rules, leading to the wide adoption of tools such as linters in professional software development projects. Traditional linters like Checkstyle operate using rigid, rule-based mechanisms that effectively detect many surface-level violations. However, in most programming languages, there is a subset of style rules that require a more nuanced understanding of code, and fall outside the scope of such static analysis. In this paper, we propose Checkstyle+, a hybrid approach that augments Checkstyle with large language model (LLM) capabilities, to identify style violations that elude the conventional rule-based analysis. Checkstyle+ is evaluated on a sample of 380 Java code files, drawn from a broader dataset of 30,800 real-world Java programs sourced from accepted Codeforces submissions. The results show that Checkstyle+ achieves superior performance over standard Checkstyle in detecting violations of the semantically nuanced rules.
Fast-MIA: Efficient and Scalable Membership Inference for LLMs
Venue: arXiv cs.CR 2025
Year: 2025

We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against Large Language Models (LLMs). MIA against LLMs has emerged as a crucial challenge due to growing concerns over copyright, security, and data privacy, and has attracted increasing research attention. However, the progress of this research is significantly hindered by two main obstacles: (1) the high computational cost of inference in LLMs, and (2) the lack of standardized and maintained implementations of MIA methods, which makes large-scale empirical comparison difficult. To address these challenges, our library provides fast batch inference and includes implementations of representative MIA methods under a unified evaluation framework. This library supports easy implementation of reproducible benchmarks with simple configuration and extensibility. We release Fast-MIA as an open-source (Apache License 2.0) tool to support scalable and transparent research on LLMs.
Strategies for Robust Deep Learning Based Deformable Registration
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning based deformable registration methods have become popular in recent years. However, their ability to generalize beyond training data distribution can be poor, significantly hindering their usability. LUMIR brain registration challenge for Learn2Reg 2025 aims to advance the field by evaluating the performance of the registration on contrasts and modalities different from those included in the training set. Here we describe our submission to the challenge, which proposes a very simple idea for significantly improving robustness by transforming the images into MIND feature space before feeding them into the model. In addition, a special ensembling strategy is proposed that shows a small but consistent improvement.
Smaller Models, Smarter Rewards: A Two-Sided Approach to Process and Outcome Rewards
Venue: arXiv cs.AI 2025
Year: 2025

Generating high-quality code remains a challenge for Large Language Models (LLMs). For the evolution of reasoning models on this task, reward models are a necessary intermediate step. These models judge outcomes or intermediate steps. Decoder-only transformer models can be turned into reward models by introducing a regression layer and supervised fine-tuning. While it is known that reflection capabilities generally increase with the size of a model, we want to investigate whether state-of-the-art small language models like the Phi-4 family can be turned into usable reward models blending the consideration of process rewards and outcome rewards. Targeting this goal, we construct a dataset of code samples with correctness labels derived from the APPS coding challenge benchmark. We then train a value-head model to estimate the success probability of intermediate outputs. Our evaluation shows that small LLMs are capable of serving as effective reward models or code evaluation critics, successfully identifying correct solutions among multiple candidates. Using this critic, we achieve over a 20% improvement in the search capability of the most accurate code out of multiple generations.
EndoWave: Rational-Wavelet 4D Gaussian Splatting for Endoscopic Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

In robot-assisted minimally invasive surgery, accurate 3D reconstruction from endoscopic video is vital for downstream tasks and improved outcomes. However, endoscopic scenarios present unique challenges, including photometric inconsistencies, non-rigid tissue motion, and view-dependent highlights. Most 3DGS-based methods that rely solely on appearance constraints for optimizing 3DGS are often insufficient in this context, as these dynamic visual artifacts can mislead the optimization process and lead to inaccurate reconstructions. To address these limitations, we present EndoWave, a unified spatio-temporal Gaussian Splatting framework by incorporating an optical flow-based geometric constraint and a multi-resolution rational wavelet supervision. First, we adopt a unified spatio-temporal Gaussian representation that directly optimizes primitives in a 4D domain. Second, we propose a geometric constraint derived from optical flow to enhance temporal coherence and effectively constrain the 3D structure of the scene. Third, we propose a multi-resolution rational orthogonal wavelet as a constraint, which can effectively separate the details of the endoscope and enhance the rendering performance. Extensive evaluations on two real surgical datasets, EndoNeRF and StereoMIS, demonstrate that our method EndoWave achieves state-of-the-art reconstruction quality and visual accuracy compared to the baseline method.
Sampling from Energy distributions with Target Concrete Score Identity
Venue: arXiv cs.LG 2025
Year: 2025

We introduce the Target Concrete Score Identity Sampler (TCSIS), a method for sampling from unnormalized densities on discrete state spaces by learning the reverse dynamics of a Continuous-Time Markov Chain (CTMC). Our approach builds on a forward in time CTMC with a uniform noising kernel and relies on the proposed Target Concrete Score Identity, which relates the concrete score, the ratio of marginal probabilities of two states, to a ratio of expectations of Boltzmann factors under the forward uniform diffusion kernel. This formulation enables Monte Carlo estimation of the concrete score without requiring samples from the target distribution or computation of the partition function. We approximate the concrete score with a neural network and propose two algorithms: Self-Normalized TCSIS and Unbiased TCSIS. Finally, we demonstrate the effectiveness of TCSIS on problems from statistical physics.
DeepSalt: Bridging Laboratory and Satellite Spectra through Domain Adaptation and Knowledge Distillation for Large-Scale Soil Salinity Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Soil salinization poses a significant threat to both ecosystems and agriculture because it limits plants' ability to absorb water and, in doing so, reduces crop productivity. This phenomenon alters the soil's spectral properties, creating a measurable relationship between salinity and light reflectance that enables remote monitoring. While laboratory spectroscopy provides precise measurements, its reliance on in-situ sampling limits scalability to regional or global levels. Conversely, hyperspectral satellite imagery enables wide-area observation but lacks the fine-grained interpretability of laboratory instruments. To bridge this gap, we introduce DeepSalt, a deep-learning-based spectral transfer framework that leverages knowledge distillation and a novel Spectral Adaptation Unit to transfer high-resolution spectral insights from laboratory-based spectroscopy to satellite-based hyperspectral sensing. Our approach eliminates the need for extensive ground sampling while enabling accurate, large-scale salinity estimation, as demonstrated through comprehensive empirical benchmarks. DeepSalt achieves significant performance gains over methods without explicit domain adaptation, underscoring the impact of the proposed Spectral Adaptation Unit and the knowledge distillation strategy. The model also effectively generalized to unseen geographic regions, explaining a substantial portion of the salinity variance.
Rethinking GSPO: The Perplexity-Entropy Equivalence
Venue: arXiv cs.LG 2025
Year: 2025

We provide a new perspective on GSPO's length-normalized importance ratios by establishing their connection to information-theoretic quantities. We show that GSPO's sequence-level weight $s(θ) = (π_θ/π_{θ_{\text{old}}})^{1/|y|}$ can be equivalently expressed as the inverse perplexity ratio $\text{PPL}_{θ_{\text{old}}}/\text{PPL}_θ$ and as the exponential cross-entropy change $\exp(ΔH)$. While the perplexity-entropy relationship follows from standard definitions, this observation provides a useful lens for understanding GSPO: the algorithm weights policy gradient updates by perplexity ratios, offering an information-theoretic interpretation of the importance weights. This perspective helps explain GSPO's empirical properties, including log-domain variance reduction through geometric averaging and stability in training mixture-of-experts models. We validate the mathematical equivalences and variance predictions through controlled experiments on mathematical reasoning tasks.
Fake scientific journals are here to stay
Venue: arXiv cs.DL 2025
Year: 2025

Scientific publishing is facing an alarming proliferation of fraudulent practices that threaten the integrity of research communication. The production and dissemination of fake research have become a profitable business, undermining trust in scientific journals and distorting the evaluation processes that depend on them. This brief piece examines the problem of fake journals through a three-level typology. The first level concerns predatory journals, which prioritise financial gain over scholarly quality by charging authors publication fees while providing superficial or fabricated peer review. The second level analyses hijacked journals, in which counterfeit websites impersonate legitimate titles to deceive authors into submitting and paying for publication. The third level addresses hacked journals, where legitimate platforms are compromised through cyberattacks or internal manipulation, enabling the distortion of review and publication processes. Together, these forms of misconduct expose deep vulnerabilities in the scientific communication ecosystem, exacerbated by the pressure to publish and the marketisation of research outputs. The manuscript concludes that combating these practices requires structural reforms in scientific evaluation and governance. Only by reducing the incentives that sustain the business of fraudulent publishing can the scholarly community restore credibility and ensure that scientific communication fulfils the essential purpose of reliable advancement of knowledge.
DREaM: Drug-Drug Relation Extraction via Transfer Learning Method
Venue: arXiv cs.CL 2025
Year: 2025

Relation extraction between drugs plays a crucial role in identifying drug drug interactions and predicting side effects. The advancement of machine learning methods in relation extraction, along with the development of large medical text databases, has enabled the low cost extraction of such relations compared to other approaches that typically require expert knowledge. However, to the best of our knowledge, there are limited datasets specifically designed for drug drug relation extraction currently available. Therefore, employing transfer learning becomes necessary to apply machine learning methods in this domain. In this study, we propose DREAM, a method that first employs a trained relation extraction model to discover relations between entities and then applies this model to a corpus of medical texts to construct an ontology of drug relationships. The extracted relations are subsequently validated using a large language model. Quantitative results indicate that the LLM agreed with 71 of the relations extracted from a subset of PubMed abstracts. Furthermore, our qualitative analysis indicates that this approach can uncover ambiguities in the medical domain, highlighting the challenges inherent in relation extraction in this field.
PTPP-Aware Adaptation Scaling Laws: Predicting Domain-Adaptation Performance at Unseen Pre-Training Budgets
Venue: arXiv cs.LG 2025
Year: 2025

Continual pre-training (CPT) for domain adaptation must balance target-domain gains with stability on the base domain. Existing CPT scaling laws typically assume a fixed pre-training budget, which limits their ability to forecast adaptation outcomes for models trained at different tokens-per-parameter (PTPP). We present \emph{PTPP-aware} adaptation scaling laws that make the pre-training budget an explicit variable, enabling accurate \emph{prediction} of adaptation loss at unseen \ptpp. On a multilingual setup (English/Arabic $\rightarrow$ French), PTPP-aware formulations trained on early stages (\ptpp{}=\{15,31\}) predict target loss at \ptpp{}=279 and outperform a PTPP-agnostic \dcpt{} transfer baseline on metrics (Huber-on-log, MAE$_\mathrm{rel}$, calibration slope); full diagnostics (RMSE, MAPE) are in the appendix. Beyond forecasting, we show a practical use case: planning replay ratios and adaptation token budgets that satisfy target and forgetting constraints under compute limits.
DRO-Based Computation Offloading and Trajectory Design for Low-Altitude Networks
Venue: arXiv cs.CE 2025
Year: 2025

The low-altitude networks (LANs) integrating unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) have become a promising solution for the rising computation demands. However, the uncertain task sizes and high mobility of UAVs pose great challenges to guarantee the quality of service. To address these issues, we propose an LAN architecture where UAVs and HAPs collaboratively provide computation offloading for ground users. Moreover, the uncertainty sets are constructed to characterize the uncertain task size, and a distributionally robust optimization problem is formulated to minimize the worst-case delay by jointly optimizing the offloading decisions and UAV trajectories. To solve the mixed-integer min-max optimization problem, we design the distributionally robust computation offloading and trajectories optimization algorithm. Specifically, the original problem is figured out by iteratively solving the outerlayer and inner-layer problems. The convex outer-layer problem with probability distributions is solved by the optimization toolkit. As for the inner-layer mixed-integer problem, we employ the Benders decomposition. The decoupled master problem concerning the binary offloading decisions is solved by the integer solver, and UAV trajectories in the sub-problem are optimized via the successive convex approximation. Simulation results show the proposed algorithm outperforms traditional optimization methods in balancing the worst-case delay and robustness.
Through the Lens: Benchmarking Deepfake Detectors Against Moiré-Induced Distortions
Venue: arXiv cs.CV 2025
Year: 2025

Deepfake detection remains a pressing challenge, particularly in real-world settings where smartphone-captured media from digital screens often introduces Moiré artifacts that can distort detection outcomes. This study systematically evaluates state-of-the-art (SOTA) deepfake detectors on Moiré-affected videos, an issue that has received little attention. We collected a dataset of 12,832 videos, spanning 35.64 hours, from the Celeb-DF, DFD, DFDC, UADFV, and FF++ datasets, capturing footage under diverse real-world conditions, including varying screens, smartphones, lighting setups, and camera angles. To further examine the influence of Moiré patterns on deepfake detection, we conducted additional experiments using our DeepMoiréFake, referred to as (DMF) dataset and two synthetic Moiré generation techniques. Across 15 top-performing detectors, our results show that Moiré artifacts degrade performance by as much as 25.4%, while synthetically generated Moiré patterns lead to a 21.4% drop in accuracy. Surprisingly, demoiréing methods, intended as a mitigation approach, instead worsened the problem, reducing accuracy by up to 17.2%. These findings underscore the urgent need for detection models that can robustly handle Moiré distortions alongside other realworld challenges, such as compression, sharpening, and blurring. By introducing the DMF dataset, we aim to drive future research toward closing the gap between controlled experiments and practical deepfake detection.
Workspace Registration and Collision Detection for Industrial Robotics Applications
Venue: arXiv cs.RO 2025
Year: 2025

Motion planning for robotic manipulators relies on precise knowledge of the environment in order to be able to define restricted areas and to take collision objects into account. To capture the workspace, point clouds of the environment are acquired using various sensors. The collision objects are identified by region growing segmentation and VCCS algorithm. Subsequently the point clusters are approximated. The aim of the present paper is to compare different sensors, to illustrate the process from detection to the finished collision environment and to detect collisions between the robot and this environment.
On the use of information fusion techniques to improve information quality: Taxonomy, opportunities and challenges
Venue: arXiv cs.IT 2025
Year: 2025

The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of computer systems, and especially decision support systems, is to assure that the quality of the information they process is high. There are many different approaches for this purpose, including information fusion. Information fusion is currently one of the most promising methods. It is particularly useful under circumstances where quality might be compromised, for example, either intrinsically due to imperfect information (vagueness, uncertainty) or because of limited resources (energy, time). In response to this goal, a wide range of research has been undertaken over recent years. To date, the literature reviews in this field have focused on problem-specific issues and have been circumscribed to certain system types. Therefore, there is no holistic and systematic knowledge of the state of the art to help establish the steps to be taken in the future. In particular, aspects like what impact different information fusion methods have on information quality, how information quality is characterised, measured and evaluated in different application domains depending on the problem data type or whether fusion is designed as a flexible process capable of adapting to changing system circumstances and their intrinsically limited resources have not been addressed. This paper aims precisely to review the literature on research into the use of information fusion techniques specifically to improve information quality, analysing the above issues in order to identify a series of challenges and research directions, which are presented in this paper.
Mubeen AI: A Specialized Arabic Language Model for Heritage Preservation and User Intent Understanding
Venue: arXiv cs.CL 2025
Year: 2025

Mubeen is a proprietary Arabic language model developed by MASARAT SA, optimized for deep understanding of Arabic linguistics, Islamic studies, and cultural heritage. Trained on an extensive collection of authentic Arabic sources significantly expanded by digitizing historical manuscripts via a proprietary Arabic OCR engine, the model incorporates seminal scholarly works in linguistics, jurisprudence, hadith, and Quranic exegesis, alongside thousands of academic theses and peer-reviewed research papers. Conditioned through a deep linguistic engineering framework, Mubeen masters not just the meaning but the eloquence of Arabic, enabling precise understanding across classical texts, contemporary writing, and regional dialects with focus on comprehending user intent and delivering accurate, contextually relevant responses. Unlike other Arabic models relying on translated English data that often fail in intent detection or retrieval-augmented generation (RAG), Mubeen uses native Arabic sources to ensure cultural authenticity and accuracy. Its core innovation is the Practical Closure Architecture, designed to solve the "Utility Gap Crisis" where factually correct answers fail to resolve users' core needs, forcing them into frustrating cycles of re-prompting. By prioritizing clarity and decisive guidance, Mubeen transforms from an information repository into a decisive guide, aligning with Saudi Vision 2030. The model's architecture combines deep heritage specialization with multi-disciplinary expert modules, enabling robust performance across both cultural preservation and general knowledge domains.
Network Intrusion Detection: Evolution from Conventional Approaches to LLM Collaboration and Emerging Risks
Venue: arXiv cs.CR 2025
Year: 2025

This survey systematizes the evolution of network intrusion detection systems (NIDS), from conventional methods such as signature-based and neural network (NN)-based approaches to recent integrations with large language models (LLMs). It clearly and concisely summarizes the current status, strengths, and limitations of conventional techniques, and explores the practical benefits of integrating LLMs into NIDS. Recent research on the application of LLMs to NIDS in diverse environments is reviewed, including conventional network infrastructures, autonomous vehicle environments and IoT environments. From this survey, readers will learn that: 1) the earliest methods, signature-based IDSs, continue to make significant contributions to modern systems, despite their well-known weaknesses; 2) NN-based detection, although considered promising and under development for more than two decades, and despite numerous related approaches, still faces significant challenges in practical deployment; 3) LLMs are useful for NIDS in many cases, and a number of related approaches have been proposed; however, they still face significant challenges in practical applications. Moreover, they can even be exploited as offensive tools, such as for generating malware, crafting phishing messages, or launching cyberattacks. Recently, several studies have been proposed to address these challenges, which are also reviewed in this survey; and 4) strategies for constructing domain-specific LLMs have been proposed and are outlined in this survey, as it is nearly impossible to train a NIDS-specific LLM from scratch.
Adaptive Blockwise Search: Inference-Time Alignment for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

LLM alignment remains a critical challenge. Inference-time methods provide a flexible alternative to fine-tuning, but their uniform computational effort often yields suboptimal alignment. We hypothesize that for many alignment tasks, the initial tokens of a response are disproportionately more critical. To leverage this principle, we introduce AdaSearch, a novel blockwise search strategy. It adaptively allocates a fixed computational budget using a sampling schedule, focusing search effort on these critical tokens. We apply AdaSearch to sequential decoding and introduce its tree-search counterpart, AdaBeam. Our comprehensive evaluation across eight LLMs demonstrates that AdaSearch outperforms strong Best-of-N and fine-tuning baselines. Specifically, win-rates improve by over 10% for harmlessness generation, controlled sentiment generation, and for mathematical reasoning tasks relative to Best-of-N.