OpenRaiser/Intern-Atlas huggingface dataset:
Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services
Venue: arXiv cs.LG 2025
Year: 2025

Workload forecasting is pivotal in cloud service applications, such as auto-scaling and scheduling, with profound implications for operational efficiency. Although Transformer-based forecasting models have demonstrated remarkable success in general tasks, their computational efficiency often falls short of the stringent requirements in large-scale cloud environments. Given that most workload series exhibit complicated periodic patterns, addressing these challenges in the frequency domain offers substantial advantages. To this end, we propose Fremer, an efficient and effective deep forecasting model. Fremer fulfills three critical requirements: it demonstrates superior efficiency, outperforming most Transformer-based forecasting models; it achieves exceptional accuracy, surpassing all state-of-the-art (SOTA) models in workload forecasting; and it exhibits robust performance for multi-period series. Furthermore, we collect and open-source four high-quality, open-source workload datasets derived from ByteDance's cloud services, encompassing workload data from thousands of computing instances. Extensive experiments on both our proprietary datasets and public benchmarks demonstrate that Fremer consistently outperforms baseline models, achieving average improvements of 5.5% in MSE, 4.7% in MAE, and 8.6% in SMAPE over SOTA models, while simultaneously reducing parameter scale and computational costs. Additionally, in a proactive auto-scaling test based on Kubernetes, Fremer improves average latency by 18.78% and reduces resource consumption by 2.35%, underscoring its practical efficacy in real-world applications.
Architectural Backdoors in Deep Learning: A Survey of Vulnerabilities, Detection, and Defense
Venue: arXiv cs.CR 2025
Year: 2025

Architectural backdoors pose an under-examined but critical threat to deep neural networks, embedding malicious logic directly into a model's computational graph. Unlike traditional data poisoning or parameter manipulation, architectural backdoors evade standard mitigation techniques and persist even after clean retraining. This survey systematically consolidates research on architectural backdoors, spanning compiler-level manipulations, tainted AutoML pipelines, and supply-chain vulnerabilities. We assess emerging detection and defense strategies, including static graph inspection, dynamic fuzzing, and partial formal verification, and highlight their limitations against distributed or stealth triggers. Despite recent progress, scalable and practical defenses remain elusive. We conclude by outlining open challenges and proposing directions for strengthening supply-chain security, cryptographic model attestations, and next-generation benchmarks. This survey aims to guide future research toward comprehensive defenses against structural backdoor threats in deep learning systems.
Improving DAPO from a Mixed-Policy Perspective
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces two novel modifications to the Dynamic sAmpling Policy Optimization (DAPO) algorithm [1], approached from a mixed-policy perspective. Standard policy gradient methods can suffer from instability and sample inefficiency, particularly in sparse reward settings. To address this, we first propose a method that incorporates a pre-trained, stable guiding policy ($\piphi$) to provide off-policy experience, thereby regularizing the training of the target policy ($\pion$). This approach improves training stability and convergence speed by adaptively adjusting the learning step size. Secondly, we extend this idea to re-utilize zero-reward samples, which are often discarded by dynamic sampling strategies like DAPO's. By treating these samples as a distinct batch guided by the expert policy, we further enhance sample efficiency. We provide a theoretical analysis for both methods, demonstrating that their objective functions converge to the optimal solution within the established theoretical framework of reinforcement learning. The proposed mixed-policy framework effectively balances exploration and exploitation, promising more stable and efficient policy optimization.
Enterprise Security Incident Analysis and Countermeasures Based on the T-Mobile Data Breach
Venue: arXiv cs.CR 2025
Year: 2025

This paper presents a comprehensive analysis of T-Mobile's critical data breaches in 2021 and 2023, alongside a full-spectrum security audit targeting its systems, infrastructure, and publicly exposed endpoints. By combining case-based vulnerability assessments with active ethical hacking techniques--including Shodan reconnaissance, API misuse simulations, VNC brute-forcing, firmware reverse engineering, and web application scans--we uncover structural weaknesses persisting beyond the initial breach events. Building on these findings, we propose a multi-layered defensive strategy encompassing Zero Trust Architecture, granular role-based access control, network segmentation, firmware encryption using AES with integrity checks, and API rate limiting and token lifecycle control. Financial modelling demonstrates that a five-year investment yields less than 1.1% of expected breach losses, validating the cost-effectiveness of proactive security measures. Our work bridges post-incident forensic analysis with hands-on security evaluation, providing an actionable blueprint for large-scale telecoms seeking operational resilience, regulatory compliance, and cross-domain threat readiness.
LoViC: Efficient Long Video Generation with Context Compression
Venue: arXiv cs.CV 2025
Year: 2025

Despite recent advances in diffusion transformers (DiTs) for text-to-video generation, scaling to long-duration content remains challenging due to the quadratic complexity of self-attention. While prior efforts -- such as sparse attention and temporally autoregressive models -- offer partial relief, they often compromise temporal coherence or scalability. We introduce LoViC, a DiT-based framework trained on million-scale open-domain videos, designed to produce long, coherent videos through a segment-wise generation process. At the core of our approach is FlexFormer, an expressive autoencoder that jointly compresses video and text into unified latent representations. It supports variable-length inputs with linearly adjustable compression rates, enabled by a single query token design based on the Q-Former architecture. Additionally, by encoding temporal context through position-aware mechanisms, our model seamlessly supports prediction, retradiction, interpolation, and multi-shot generation within a unified paradigm. Extensive experiments across diverse tasks validate the effectiveness and versatility of our approach.
A Spectral Interpretation of Redundancy in a Graph Reservoir
Venue: arXiv cs.LG 2025
Year: 2025

Reservoir computing has been successfully applied to graphs as a preprocessing method to improve the training efficiency of Graph Neural Networks (GNNs). However, a common issue that arises when repeatedly applying layer operators on graphs is over-smoothing, which consists in the convergence of graph signals toward low-frequency components of the graph Laplacian. This work revisits the definition of the reservoir in the Multiresolution Reservoir Graph Neural Network (MRGNN), a spectral reservoir model, and proposes a variant based on a Fairing algorithm originally introduced in the field of surface design in computer graphics. This algorithm provides a pass-band spectral filter that allows smoothing without shrinkage, and it can be adapted to the graph setting through the Laplacian operator. Given its spectral formulation, this method naturally connects to GNN architectures for tasks where smoothing, when properly controlled, can be beneficial,such as graph classification. The core contribution of the paper lies in the theoretical analysis of the algorithm from a random walks perspective. In particular, it shows how tuning the spectral coefficients can be interpreted as modulating the contribution of redundant random walks. Exploratory experiments based on the MRGNN architecture illustrate the potential of this approach and suggest promising directions for future research.
MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents our approach for the IberLEF 2025 Task PRESTA: Preguntas y Respuestas sobre Tablas en Español (Questions and Answers about Tables in Spanish). Our solution obtains answers to the questions by implementing Python code generation with LLMs that is used to filter and process the table. This solution evolves from the MRT implementation for the Semeval 2025 related task. The process consists of multiple steps: analyzing and understanding the content of the table, selecting the useful columns, generating instructions in natural language, translating these instructions to code, running it, and handling potential errors or exceptions. These steps use open-source LLMs and fine-grained optimized prompts for each step. With this approach, we achieved an accuracy score of 85\% in the task.
Disjoint Tours and the Price of Diversity
Venue: arXiv cs.DS 2025
Year: 2025

We study a variant of the Traveling Salesman Problem, where instead of finding a single tour, we want to find a pair of two edge-disjoint tours whose longer tour is as short as possible. We investigate the Price of Diversity (PoD) for this problem, which is the ratio of the cost of the longer of the two tours and the cost of a single optimal tour, in the worst case over all possible instances. We prove (almost) tight bounds on this quantity for a special 1-dimensional scenario and for general metric spaces. We believe that the Price-of-Diversity framework that we introduce is interesting in its own right, and may lead to follow-up work on other problems as well.
From Paranoia to Compliance: The Bumpy Road of System Hardening Practices on Stack Exchange
Venue: arXiv cs.CR 2025
Year: 2025

Hardening computer systems against cyberattacks is crucial for security. However, past incidents illustrated, that many system operators struggle with effective system hardening. Hence, many computer systems and applications remain insecure. So far, the research community lacks an in-depth understanding of system operators motivation, practices, and challenges around system hardening. With a focus on practices and challenges, we qualitatively analyzed 316 Stack Exchange (SE) posts related to system hardening. We find that access control and deployment-related issues are the most challenging, and system operators suffer from misconceptions and unrealistic expectations. Most frequently, posts focused on operating systems and server applications. System operators were driven by the fear of their systems getting attacked or by compliance reasons. Finally, we discuss our research questions, make recommendations for future system hardening, and illustrate the implications of our work.
Maintaining Routing Structures under Deletions via Self-Pruning
Venue: arXiv cs.DS 2025
Year: 2025

Expanders are powerful algorithmic structures with two key properties: they are a) routable: for any multi-commodity flow unit demand, there exists a routing with low congestion over short paths, where a demand is unit if the amount of demand sent / received by any vertex is at most the number of edges adjacent to it. b) stable / prunable: for any (sequence of) edge failures, there exists a proportionally small subset of vertices that can be disabled, such that the graph induced on the remaining vertices is an expander. Two natural algorithmic problems correspond to these two existential guarantees: expander routing, i.e. computing a low-congestion routing for a unit multi-commodity demand on an expander, and expander pruning, i.e., maintaining the subset of disabled vertices under a sequence of edge failures. This paper considers the combination of the two problems: maintaining a routing for a unit multi-commodity demand under pruning steps. This is done through the introduction of a family of expander graphs that, like hypercubes, are easy to route in, and are self-pruning: for an online sequence of edge deletions, a simple self-contained algorithm can find a few vertices to prune with each edge deletion, such that the remaining graph always remains an easy-to-route-in expander in the family. Notably, and with considerable technical work, this self-pruning can be made worst-case, i.e., such that every single adversarial deletion only causes a small number of additional deletions. Our results also allow tight constant-factor control over the length of routing paths (with the usual trade-offs in congestion and pruning ratio) and therefore extend to constant-hop and length-constrained expanders in which routing over constant length paths is crucial.
On statistical learning of graphs
Venue: arXiv cs.LG 2025
Year: 2025

We study PAC and online learnability of hypothesis classes formed by copies of a countably infinite graph G, where each copy is induced by permuting G's vertices. This corresponds to learning a graph's labeling, knowing its structure and label set. We consider classes where permutations move only finitely many vertices. Our main result shows that PAC learnability of all such finite-support copies implies online learnability of the full isomorphism type of G, and is equivalent to the condition of automorphic triviality. We also characterize graphs where copies induced by swapping two vertices are not learnable, using a relaxation of the extension property of the infinite random graph. Finally, we show that, for all G and k>2, learnability for k-vertex permutations is equivalent to that for 2-vertex permutations, yielding a four-class partition of infinite graphs, whose complexity we also determine using tools coming from both descriptive set theory and computability theory.
Probabilistic algorithm for computing all local minimizers of Morse functions on a compact domain
Venue: arXiv cs.SC 2025
Year: 2025

Let K be the unit-cube in Rn and f\,: K $\rightarrow$ R^n be a Morse function. We assume that the function f is given by an evaluation program $Γ$ in the noisy model, i.e., the evaluation program $Γ$ takes an extra parameter $η$ as input and returns an approximation that is $η$-close to the true value of f . In this article, we design an algorithm able to compute all local minimizers of f on K . Our algorithm takes as input $Γ$, $η$, a numerical accuracy parameter $ε$ as well as some extra regularity parameters which are made explicit. Under assumptions of probabilistic nature -- related to the choice of the evaluation points used to feed $Γ$ --, it returns finitely many rational points of K , such that the set of balls of radius $ε$ centered at these points contains and separates the set of all local minimizers of f . Our method is based on approximation theory, yielding polynomial approximants for f , combined with computer algebra techniques for solving systems of polynomial equations. We provide bit complexity estimates for our algorithm when all regularity parameters are known. Practical experiments show that our implementation of this algorithm in the Julia package Globtim can tackle examples that were not reachable until now.
Decoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Open World Object Detection (OWOD) is a challenging computer vision task that extends standard object detection by (1) detecting and classifying unknown objects without supervision, and (2) incrementally learning new object classes without forgetting previously learned ones. The absence of ground truths for unknown objects makes OWOD tasks particularly challenging. Many methods have addressed this by using pseudo-labels for unknown objects. The recently proposed Probabilistic Objectness transformer-based open-world detector (PROB) is a state-of-the-art model that does not require pseudo-labels for unknown objects, as it predicts probabilistic objectness. However, this method faces issues with learning conflicts between objectness and class predictions. To address this issue and further enhance performance, we propose a novel model, Decoupled PROB. Decoupled PROB introduces Early Termination of Objectness Prediction (ETOP) to stop objectness predictions at appropriate layers in the decoder, resolving the learning conflicts between class and objectness predictions in PROB. Additionally, we introduce Task-Decoupled Query Initialization (TDQI), which efficiently extracts features of known and unknown objects, thereby improving performance. TDQI is a query initialization method that combines query selection and learnable queries, and it is a module that can be easily integrated into existing DETR-based OWOD models. Extensive experiments on OWOD benchmarks demonstrate that Decoupled PROB surpasses all existing methods across several metrics, significantly improving performance.
Formal Verification for JavaScript Regular Expressions: a Proven Semantics and its Applications (Extended Version)
Venue: arXiv cs.PL 2025
Year: 2025

We present the first mechanized, succinct, practical, complete, and proven-faithful semantics for a modern regular expression language with backtracking semantics. We ensure its faithfulness by proving it equivalent to a preexisting line-by-line embedding of the official ECMAScript specification of JavaScript regular expressions. We demonstrate its practicality by presenting two real-world applications. First, a new notion of contextual equivalence for modern regular expressions, which we use to prove or disprove rewrites drawn from previous work. Second, the first formal proof of the PikeVM algorithm used in many real-world engines. In contrast with the specification and other formalization work, our semantics captures not only the top-priority match, but a full backtracking tree recording all possible matches and their respective priority. All our definitions and results have been mechanized in the Rocq proof assistant.
Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces
Venue: arXiv cs.LG 2025
Year: 2025

Electroencephalography (EEG) is a fundamental modality for cognitive state monitoring in brain-computer interfaces (BCIs). However, it is highly susceptible to intrinsic signal errors and human-induced labeling errors, which lead to label noise and ultimately degrade model performance. To enhance EEG learning, multimodal knowledge distillation (KD) has been explored to transfer knowledge from visual models with rich representations to EEG-based models. Nevertheless, KD faces two key challenges: modality gap and soft label misalignment. The former arises from the heterogeneous nature of EEG and visual feature spaces, while the latter stems from label inconsistencies that create discrepancies between ground truth labels and distillation targets. This paper addresses semantic uncertainty caused by ambiguous features and weakly defined labels. We propose a novel cross-modal knowledge distillation framework that mitigates both modality and label inconsistencies. It aligns feature semantics through a prototype-based similarity module and introduces a task-specific distillation head to resolve label-induced inconsistency in supervision. Experimental results demonstrate that our approach improves EEG-based emotion regression and classification performance, outperforming both unimodal and multimodal baselines on a public multimodal dataset. These findings highlight the potential of our framework for BCI applications.
A Conceptual Framework for Requirements Engineering of Pretrained-Model-Enabled Systems
Venue: arXiv cs.SE 2025
Year: 2025

Recent advances in large pretrained models have led to their widespread integration as core components in modern software systems. The trend is expected to continue in the foreseeable future. Unlike traditional software systems governed by deterministic logic, systems powered by pretrained models exhibit distinctive and emergent characteristics, such as ambiguous capability boundaries, context-dependent behavior, and continuous evolution. These properties fundamentally challenge long-standing assumptions in requirements engineering, including functional decomposability and behavioral predictability. This paper investigates this problem and advocates for a rethinking of existing requirements engineering methodologies. We propose a conceptual framework tailored to requirements engineering of pretrained-model-enabled software systems and outline several promising research directions within this framework. This vision helps provide a guide for researchers and practitioners to tackle the emerging challenges in requirements engineering of pretrained-model-enabled systems.
R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning
Venue: arXiv cs.CV 2025
Year: 2025

Enabling large-scale generative models to continuously learn new visual concepts is essential for personalizing pre-trained models to meet individual user preferences. Existing approaches for continual visual concept learning are constrained by two fundamental challenges: catastrophic forgetting and parameter expansion. In this paper, we propose Redundancy-Removal Mixture of Experts (R^2MoE), a parameter-efficient framework for lifelong visual concept learning that effectively learns new concepts while incurring minimal parameter overhead. Our framework includes three key innovative contributions: First, we propose a mixture-of-experts framework with a routing distillation mechanism that enables experts to acquire concept-specific knowledge while preserving the gating network's routing capability, thereby effectively mitigating catastrophic forgetting. Second, we propose a strategy for eliminating redundant layer-wise experts that reduces the number of expert parameters by fully utilizing previously learned experts. Third, we employ a hierarchical local attention-guided inference approach to mitigate interference between generated visual concepts. Extensive experiments have demonstrated that our method generates images with superior conceptual fidelity compared to the state-of-the-art (SOTA) method, achieving an impressive 87.8\% reduction in forgetting rates and 63.3\% fewer parameters on the CustomConcept 101 dataset. Our code is available at {https://github.com/learninginvision/R2MoE}
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering
Venue: arXiv cs.CV 2025
Year: 2025

High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significant challenges, including high computational cost, sensitivity to spatial misalignment, and difficulty in capturing localized structural differences. This paper introduces a registration-based anomaly detection framework that combines multi-prototype alignment with cluster-wise discrepancy analysis to enable precise 3D anomaly localization. Specifically, each test sample is first registered to multiple normal prototypes to enable direct structural comparison. To evaluate anomalies at a local level, clustering is performed over the point cloud, and similarity is computed between features from the test sample and the prototypes within each cluster. Rather than selecting cluster centroids randomly, a keypoint-guided strategy is employed, where geometrically informative points are chosen as centroids. This ensures that clusters are centered on feature-rich regions, enabling more meaningful and stable distance-based comparisons. Extensive experiments on the Real3D-AD benchmark demonstrate that the proposed method achieves state-of-the-art performance in both object-level and point-level anomaly detection, even using only raw features.