OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing Build Compromises Through Vulnerability Disclosure Analysis
Venue: arXiv cs.SE 2025
Year: 2025

The software build process transforms source code into deployable artifacts, representing a critical yet vulnerable stage in software development. Build infrastructure security poses unique challenges: the complexity of multi-component systems (source code, dependencies, build tools), the difficulty of detecting intrusions during compilation, and prevalent build non-determinism that masks malicious modifications. Despite these risks, the security community lacks a systematic understanding of build-specific attack vectors, hindering effective defense design. This paper presents an empirically-derived taxonomy of attack vectors targeting the build process, constructed through a large-scale CVE mining (of 621 vulnerability disclosures from the NVD database). We categorize attack vectors by their injection points across the build pipeline, from source code manipulation to compiler compromise. To validate our taxonomy, we analyzed 168 documented software supply chain attacks, identifying 40 incidents specifically targeting build phases. Our analysis reveals that 23.8\% of supply chain attacks exploit build vulnerabilities, with dependency confusion and build script injection representing the most prevalent vectors. Dataset available at: https://anonymous.4open.science/r/Taxonomizing-Build-Attacks-8BB0.
On the Computability of Finding Capacity-Achieving Codes
Venue: arXiv cs.IT 2025
Year: 2025

This work studies the problem of constructing capacity-achieving codes from an algorithmic perspective. Specifically, we prove that there exists a Turing machine which, given a discrete memoryless channel $p_{Y|X}$, a target rate $R$ less than the channel capacity $C(p_{Y|X})$, and an error tolerance $ε> 0$, outputs a block code $\mathcal{C}$ achieving a rate at least $R$ and a maximum block error probability below $ε$. The machine operates in the general case where all transition probabilities of $p_{Y|X}$ are computable real numbers, and the parameters $R$ and $ε$ are rational. The proof builds on Shannon's channel coding theorem and relies on an exhaustive search approach that systematically enumerates all codes of increasing block length until a valid code is found. This construction is formalized using the theory of recursive functions, yielding a $μ$-recursive function $\mathrm{FindCode} : \mathbb{N}^3 \rightharpoonup \mathbb{N}$ that takes as input appropriate encodings of $p_{Y|X}$, $R$, and $ε$, and, whenever $R < C(p_{Y|X})$, outputs an encoding of a valid code. By Kleene's normal form theorem, which establishes the computational equivalence between Turing machines and $μ$-recursive functions, we conclude that the problem is solvable by a Turing machine. This result can also be extended to the case where $ε$ is a computable real number, while we further discuss an analogous generalization of our analysis when $R$ is computable as well. We note that the assumptions that the probabilities of $p_{Y|X}$, as well as $ε$ and $R$, are computable real numbers cannot be further weakened, since computable reals constitute the largest subset of $\mathbb{R}$ representable by algorithmic means.
The Curvature Rate λ: A Scalar Measure of Input-Space Sharpness in Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Curvature influences generalization, robustness, and how reliably neural networks respond to small input perturbations. Existing sharpness metrics are typically defined in parameter space (e.g., Hessian eigenvalues) and can be expensive, sensitive to reparameterization, and difficult to interpret in functional terms. We introduce a scalar curvature measure defined directly in input space: the curvature rate λ, given by the exponential growth rate of higher-order input derivatives. Empirically, λ is estimated as the slope of log ||D^n f|| versus n for small n. This growth-rate perspective unifies classical analytic quantities: for analytic functions, λ corresponds to the inverse radius of convergence, and for bandlimited signals, it reflects the spectral cutoff. The same principle extends to neural networks, where λ tracks the emergence of high-frequency structure in the decision boundary. Experiments on analytic functions and neural networks (Two Moons and MNIST) show that λ evolves predictably during training and can be directly shaped using a simple derivative-based regularizer, Curvature Rate Regularization (CRR). Compared to Sharpness-Aware Minimization (SAM), CRR achieves similar accuracy while yielding flatter input-space geometry and improved confidence calibration. By grounding curvature in differentiation dynamics, λ provides a compact, interpretable, and parameterization-invariant descriptor of functional smoothness in learned models.
Discriminately Treating Motion Components Evolves Joint Depth and Ego-Motion Learning
Venue: arXiv cs.CV 2025
Year: 2025

Unsupervised learning of depth and ego-motion, two fundamental 3D perception tasks, has made significant strides in recent years. However, most methods treat ego-motion as an auxiliary task, either mixing all motion types or excluding depth-independent rotational motions in supervision. Such designs limit the incorporation of strong geometric constraints, reducing reliability and robustness under diverse conditions. This study introduces a discriminative treatment of motion components, leveraging the geometric regularities of their respective rigid flows to benefit both depth and ego-motion estimation. Given consecutive video frames, network outputs first align the optical axes and imaging planes of the source and target cameras. Optical flows between frames are transformed through these alignments, and deviations are quantified to impose geometric constraints individually on each ego-motion component, enabling more targeted refinement. These alignments further reformulate the joint learning process into coaxial and coplanar forms, where depth and each translation component can be mutually derived through closed-form geometric relationships, introducing complementary constraints that improve depth robustness. DiMoDE, a general depth and ego-motion joint learning framework incorporating these designs, achieves state-of-the-art performance on multiple public datasets and a newly collected diverse real-world dataset, particularly under challenging conditions. Our source code will be publicly available at mias.group/DiMoDE upon publication.
BanglaNirTox: A Large-scale Parallel Corpus for Explainable AI in Bengali Text Detoxification
Venue: arXiv cs.CL 2025
Year: 2025

Toxic language in Bengali remains prevalent, especially in online environments, with few effective precautions against it. Although text detoxification has seen progress in high-resource languages, Bengali remains underexplored due to limited resources. In this paper, we propose a novel pipeline for Bengali text detoxification that combines Pareto class-optimized large language models (LLMs) and Chain-of-Thought (CoT) prompting to generate detoxified sentences. To support this effort, we construct BanglaNirTox, an artificially generated parallel corpus of 68,041 toxic Bengali sentences with class-wise toxicity labels, reasonings, and detoxified paraphrases, using Pareto-optimized LLMs evaluated on random samples. The resulting BanglaNirTox dataset is used to fine-tune language models to produce better detoxified versions of Bengali sentences. Our findings show that Pareto-optimized LLMs with CoT prompting significantly enhance the quality and consistency of Bengali text detoxification.
NSYNC: Negative Synthetic Image Generation for Contrastive Training to Improve Stylized Text-To-Image Translation
Venue: arXiv cs.CV 2025
Year: 2025

Current text conditioned image generation methods output realistic looking images, but they fail to capture specific styles. Simply finetuning them on the target style datasets still struggles to grasp the style features. In this work, we present a novel contrastive learning framework to improve the stylization capability of large text-to-image diffusion models. Motivated by the astonishing advance in image generation models that makes synthetic data an intrinsic part of model training in various computer vision tasks, we exploit synthetic image generation in our approach. Usually, the generated synthetic data is dependent on the task, and most of the time it is used to enlarge the available real training dataset. With NSYNC, alternatively, we focus on generating negative synthetic sets to be used in a novel contrastive training scheme along with real positive images. In our proposed training setup, we forward negative data along with positive data and obtain negative and positive gradients, respectively. We then refine the positive gradient by subtracting its projection onto the negative gradient to get the orthogonal component, based on which the parameters are updated. This orthogonal component eliminates the trivial attributes that are present in both positive and negative data and directs the model towards capturing a more unique style. Experiments on various styles of painters and illustrators show that our approach improves the performance over the baseline methods both quantitatively and qualitatively. Our code is available at https://github.com/giddyyupp/NSYNC.
Driving scenario generation and evaluation using a structured layer representation and foundational models
Venue: arXiv cs.CV 2025
Year: 2025

Rare and challenging driving scenarios are critical for autonomous vehicle development. Since they are difficult to encounter, simulating or generating them using generative models is a popular approach. Following previous efforts to structure driving scenario representations in a layer model, we propose a structured five-layer model to improve the evaluation and generation of rare scenarios. We use this model alongside large foundational models to generate new driving scenarios using a data augmentation strategy. Unlike previous representations, our structure introduces subclasses and characteristics for every agent of the scenario, allowing us to compare them using an embedding specific to our layer-model. We study and adapt two metrics to evaluate the relevance of a synthetic dataset in the context of a structured representation: the diversity score estimates how different the scenarios of a dataset are from one another, while the originality score calculates how similar a synthetic dataset is from a real reference set. This paper showcases both metrics in different generation setup, as well as a qualitative evaluation of synthetic videos generated from structured scenario descriptions. The code and extended results can be found at https://github.com/Valgiz/5LMSG.
NOA: a versatile, extensible tool for AI-based organoid analysis
Venue: arXiv cs.CV 2025
Year: 2025

AI tools can greatly enhance the analysis of organoid microscopy images, from detection and segmentation to feature extraction and classification. However, their limited accessibility to biologists without programming experience remains a major barrier, resulting in labor-intensive and largely manual workflows. Although a few AI models for organoid analysis have been developed, most existing tools remain narrowly focused on specific tasks. In this work, we introduce the Napari Organoid Analyzer (NOA), a general purpose graphical user interface to simplify AI-based organoid analysis. NOA integrates modules for detection, segmentation, tracking, feature extraction, custom feature annotation and ML-based feature prediction. It interfaces multiple state-of-the-art algorithms and is implemented as an open-source napari plugin for maximal flexibility and extensibility. We demonstrate the versatility of NOA through three case studies, involving the quantification of morphological changes during organoid differentiation, assessment of phototoxicity effects, and prediction of organoid viability and differentiation state. Together, these examples illustrate how NOA enables comprehensive, AI-driven organoid image analysis within an accessible and extensible framework.
Math anxiety and associative knowledge structure are entwined in psychology students but not in Large Language Models like GPT-3.5 and GPT-4o
Venue: arXiv cs.CL 2025
Year: 2025

Math anxiety poses significant challenges for university psychology students, affecting their career choices and overall well-being. This study employs a framework based on behavioural forma mentis networks (i.e. cognitive models that map how individuals structure their associative knowledge and emotional perceptions of concepts) to explore individual and group differences in the perception and association of concepts related to math and anxiety. We conducted 4 experiments involving psychology undergraduates from 2 samples (n1 = 70, n2 = 57) compared against GPT-simulated students (GPT-3.5: n2 = 300; GPT-4o: n4 = 300). Experiments 1, 2, and 3 employ individual-level network features to predict psychometric scores for math anxiety and its facets (observational, social and evaluational) from the Math Anxiety Scale. Experiment 4 focuses on group-level perceptions extracted from human students, GPT-3.5 and GPT-4o's networks. Results indicate that, in students, positive valence ratings and higher network degree for "anxiety", together with negative ratings for "math", can predict higher total and evaluative math anxiety. In contrast, these models do not work on GPT-based data because of differences in simulated networks and psychometric scores compared to humans. These results were also reconciled with differences found in the ways that high/low subgroups of simulated and real students framed semantically and emotionally STEM concepts. High math-anxiety students collectively framed "anxiety" in an emotionally polarising way, absent in the negative perception of low math-anxiety students. "Science" was rated positively, but contrasted against the negative perception of "math". These findings underscore the importance of understanding concept perception and associations in managing students' math anxiety.
ExplicitLM: Decoupling Knowledge from Parameters via Explicit Memory Banks
Venue: arXiv cs.AI 2025
Year: 2025

Large language models suffer from knowledge staleness and lack of interpretability due to implicit knowledge storage across entangled network parameters, preventing targeted updates and reasoning transparency. We propose ExplicitLM, a novel architecture featuring a million-scale external memory bank storing human-readable knowledge as token sequences, enabling direct inspection and modification. We design a differentiable two-stage retrieval mechanism with efficient coarse-grained filtering via product key decomposition (reducing complexity from $\mathcal{O}(N \cdot |I|)$ to $\mathcal{O}(\sqrt{N} \cdot |I|)$) and fine-grained Gumbel-Softmax matching for end-to-end training. Inspired by dual-system cognitive theory, we partition knowledge into frozen explicit facts (20%) and learnable implicit patterns (80%), maintained through Exponential Moving Average updates for stability. ExplicitLM achieves up to 43.67% improvement on knowledge-intensive tasks versus standard Transformers, with 3.62$\times$ gains in low-data regimes (10k samples). Analysis shows strong correlations between memory retrieval and performance, with correct predictions achieving 49% higher hit rates. Unlike RAG systems with frozen retrieval, our jointly optimized architecture demonstrates that interpretable, updatable models can maintain competitive performance while providing unprecedented knowledge transparency.
Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems
Venue: arXiv cs.CR 2025
Year: 2025

Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence (AI) detectors requires diverse datasets, which are often distributed across multiple organizations, making centralization necessary. However, centralized learning is often impractical due to security, privacy regulations, data ownership issues, and legal barriers to cross-organizational sharing. Compounding this challenge, ransomware evolves rapidly, demanding models that are both robust and adaptable. In this paper, we evaluate Federated Learning (FL) using the Sherpa.ai FL platform, which enables multiple organizations to collaboratively train a ransomware detection model while keeping raw data local and secure. This paradigm is particularly relevant for cybersecurity companies (including both software and hardware vendors) that deploy ransomware detection or firewall systems across millions of endpoints. In such environments, data cannot be transferred outside the customer's device due to strict security, privacy, or regulatory constraints. Although FL applies broadly to malware threats, we validate the approach using the Ransomware Storage Access Patterns (RanSAP) dataset. Our experiments demonstrate that FL improves ransomware detection accuracy by a relative 9% over server-local models and achieves performance comparable to centralized training. These results indicate that FL offers a scalable, high-performing, and privacy-preserving framework for proactive ransomware detection across organizational and regulatory boundaries.
EV-NVC: Efficient Variable bitrate Neural Video Compression
Venue: arXiv cs.MM 2025
Year: 2025

Training neural video codec (NVC) with variable rate is a highly challenging task due to its complex training strategies and model structure. In this paper, we train an efficient variable bitrate neural video codec (EV-NVC) with the piecewise linear sampler (PLS) to improve the rate-distortion performance in high bitrate range, and the long-short-term feature fusion module (LSTFFM) to enhance the context modeling. Besides, we introduce mixed-precision training and discuss the different training strategies for each stage in detail to fully evaluate its effectiveness. Experimental results show that our approach reduces the BD-rate by 30.56% compared to HM-16.25 within low-delay mode.
Evaluation of compliance with democratic and technical standards of i-voting in elections to academic senates in Czech higher education
Venue: arXiv cs.CY 2025
Year: 2025

The shift towards increased remote work and digital communication, driven by recent global developments, has led to the widespread adoption of i-voting systems, including in academic institutions. This paper critically evaluates the use of i-voting platforms for elections to academic senates at Czech public universities, focusing on the democratic and technical challenges they present. A total of 18 out of 26 Czech public universities have implemented remote electronic voting for these elections. Yet, the systems often lack the necessary transparency, raising significant concerns regarding their adherence to democratic norms, such as election security, voter privacy, and the integrity of the process. Through interviews with system developers and administrators, along with a survey of potential voters, the study underscores the critical need for transparency. Without it, a comprehensive assessment of the technical standards and the overall legitimacy of the i-voting systems remains unattainable, potentially undermining the credibility of the electoral outcomes.
Imperfect Language, Artificial Intelligence, and the Human Mind: An Interdisciplinary Approach to Linguistic Errors in Native Spanish Speakers
Venue: arXiv cs.CL 2025
Year: 2025

Linguistic errors are not merely deviations from normative grammar; they offer a unique window into the cognitive architecture of language and expose the current limitations of artificial systems that seek to replicate them. This project proposes an interdisciplinary study of linguistic errors produced by native Spanish speakers, with the aim of analyzing how current large language models (LLM) interpret, reproduce, or correct them. The research integrates three core perspectives: theoretical linguistics, to classify and understand the nature of the errors; neurolinguistics, to contextualize them within real-time language processing in the brain; and natural language processing (NLP), to evaluate their interpretation against linguistic errors. A purpose-built corpus of authentic errors of native Spanish (+500) will serve as the foundation for empirical analysis. These errors will be tested against AI models such as GPT or Gemini to assess their interpretative accuracy and their ability to generalize patterns of human linguistic behavior. The project contributes not only to the understanding of Spanish as a native language but also to the development of NLP systems that are more cognitively informed and capable of engaging with the imperfect, variable, and often ambiguous nature of real human language.
Prompt Injection as an Emerging Threat: Evaluating the Resilience of Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) are increasingly used in intelligent systems that perform reasoning, summarization, and code generation. Their ability to follow natural-language instructions, while powerful, also makes them vulnerable to a new class of attacks known as prompt injection. In these attacks, hidden or malicious instructions are inserted into user inputs or external content, causing the model to ignore its intended task or produce unsafe responses. This study proposes a unified framework for evaluating how resistant Large Language Models (LLMs) are to prompt injection attacks. The framework defines three complementary metrics such as the Resilience Degradation Index (RDI), Safety Compliance Coefficient (SCC), and Instructional Integrity Metric (IIM) to jointly measure robustness, safety, and semantic stability. We evaluated four instruction-tuned models (GPT-4, GPT-4o, LLaMA-3 8B Instruct, and Flan-T5-Large) on five common language tasks: question answering, summarization, translation, reasoning, and code generation. Results show that GPT-4 performs best overall, while open-weight models remain more vulnerable. The findings highlight that strong alignment and safety tuning are more important for resilience than model size alone. Results show that all models remain partially vulnerable, especially to indirect and direct-override attacks. GPT-4 achieved the best overall resilience (RDR = 9.8 %, SCR = 96.4 %), while open-source models exhibited higher performance degradation and lower safety scores. The findings demonstrate that alignment strength and safety tuning play a greater role in resilience than model size alone. The proposed framework offers a structured, reproducible approach for assessing model robustness and provides practical insights for improving LLM safety and reliability.