OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive Multi-Vocal Empirical Study of ML Cloud Service Misuses
Venue: arXiv cs.SE 2025
Year: 2025

Machine Learning (ML) models are widely used across various domains, including medical diagnostics and autonomous driving. To support this growth, cloud providers offer ML services to ease the integration of ML components in software systems. The evolving business requirements and the popularity of ML services have led practitioners of all skill levels to implement, and maintain ML service-based systems. However, they may not always adhere to optimal design and usage practices for ML cloud services, resulting in common misuse which could significantly degrade the quality of ML service-based systems and adversely affect their maintenance and evolution. Though much research has been conducted on ML service misuse, a consistent terminology and specification for these misuses remain absent. We therefore conduct in this paper a comprehensive, multi-vocal empirical study exploring the prevalence of ML cloud service misuses in practice. We propose a catalog of 20 ML cloud service misuses, most of which have not been studied in prior research. To achieve this, we conducted a) a systematic literature review of studies on ML misuses, b) a gray literature review of the official documentation provided by major cloud providers, c) an empirical analysis of a curated set of 377 ML service-based systems on GitHub, and d) a survey with 50 ML practitioners. Our results show that ML service misuses are common in both open-source projects and industry, often stemming from a lack of understanding of service capabilities, and insufficient documentation. This emphasizes the importance of ongoing education in best practices for ML services, which is the focus of this paper, while also highlighting the need for tools to automatically detect and refactor ML misuses.
Temporal Difference Flows
Venue: arXiv cs.LG 2025
Year: 2025

Predictive models of the future are fundamental for an agent's ability to reason and plan. A common strategy learns a world model and unrolls it step-by-step at inference, where small errors can rapidly compound. Geometric Horizon Models (GHMs) offer a compelling alternative by directly making predictions of future states, avoiding cumulative inference errors. While GHMs can be conveniently learned by a generative analog to temporal difference (TD) learning, existing methods are negatively affected by bootstrapping predictions at train time and struggle to generate high-quality predictions at long horizons. This paper introduces Temporal Difference Flows (TD-Flow), which leverages the structure of a novel Bellman equation on probability paths alongside flow-matching techniques to learn accurate GHMs at over 5x the horizon length of prior methods. Theoretically, we establish a new convergence result and primarily attribute TD-Flow's efficacy to reduced gradient variance during training. We further show that similar arguments can be extended to diffusion-based methods. Empirically, we validate TD-Flow across a diverse set of domains on both generative metrics and downstream tasks including policy evaluation. Moreover, integrating TD-Flow with recent behavior foundation models for planning over pre-trained policies demonstrates substantial performance gains, underscoring its promise for long-horizon decision-making.
SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey
Venue: arXiv cs.RO 2025
Year: 2025

Recent advances in deep learning and Transformers have driven major breakthroughs in robotics by employing techniques such as imitation learning, reinforcement learning, and LLM-based multimodal perception and decision-making. However, conventional deep learning and Transformer models often struggle to process data with inherent symmetries and invariances, typically relying on large datasets or extensive data augmentation. Equivariant neural networks overcome these limitations by explicitly integrating symmetry and invariance into their architectures, leading to improved efficiency and generalization. This tutorial survey reviews a wide range of equivariant deep learning and control methods for robotics, from classic to state-of-the-art, with a focus on SE(3)-equivariant models that leverage the natural 3D rotational and translational symmetries in visual robotic manipulation and control design. Using unified mathematical notation, we begin by reviewing key concepts from group theory, along with matrix Lie groups and Lie algebras. We then introduce foundational group-equivariant neural network design and show how the group-equivariance can be obtained through their structure. Next, we discuss the applications of SE(3)-equivariant neural networks in robotics in terms of imitation learning and reinforcement learning. The SE(3)-equivariant control design is also reviewed from the perspective of geometric control. Finally, we highlight the challenges and future directions of equivariant methods in developing more robust, sample-efficient, and multi-modal real-world robotic systems.
Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Developing robust and versatile deep-learning models is essential for enhancing diagnostic accuracy and guiding clinical interventions in medical imaging, but it requires a large amount of annotated data. The advancement of deep learning has facilitated the creation of numerous medical datasets with diverse expert-level annotations. Aggregating these datasets can maximize data utilization and address the inadequacy of labeled data. However, the heterogeneity of expert-level annotations across tasks such as classification, localization, and segmentation presents a significant challenge for learning from these datasets. To this end, we introduce nFoundation X, an end-to-end framework that utilizes diverse expert-level annotations from numerous public datasets to train a foundation model capable of multiple tasks including classification, localization, and segmentation. To address the challenges of annotation and task heterogeneity, we propose a Lock-Release pretraining strategy to enhance the cyclic learning from multiple datasets, combined with the student-teacher learning paradigm, ensuring the model retains general knowledge for all tasks while preventing overfitting to any single task. To demonstrate the effectiveness of Foundation X, we trained a model using 11 chest X-ray datasets, covering annotations for classification, localization, and segmentation tasks. Our experimental results show that Foundation X achieves notable performance gains through extensive annotation utilization, excels in cross-dataset and cross-task learning, and further enhances performance in organ localization and segmentation tasks. All code and pretrained models are publicly accessible at https://github.com/jlianglab/Foundation_X.
Introducing MareNostrum5: A European pre-exascale energy-efficient system designed to serve a broad spectrum of scientific workloads
Venue: arXiv cs.DC 2025
Year: 2025

MareNostrum5 is a pre-exascale supercomputer at the Barcelona Supercomputing Center (BSC), part of the EuroHPC Joint Undertaking. With a peak performance of 314 petaflops, MareNostrum5 features a hybrid architecture comprising Intel Sapphire Rapids CPUs, NVIDIA Hopper GPUs, and DDR5 and high-bandwidth memory (HBM), organized into four partitions optimized for diverse workloads. This document evaluates MareNostrum5 through micro-benchmarks (floating-point performance, memory bandwidth, interconnect throughput), HPC benchmarks (HPL and HPCG), and application studies using Alya, OpenFOAM, and IFS. It highlights MareNostrum5's scalability, efficiency, and energy performance, utilizing the EAR (Energy Aware Runtime) framework to assess power consumption and the effects of direct liquid cooling. Additionally, HBM and DDR5 configurations are compared to examine memory performance trade-offs. Designed to complement standard technical documentation, this study provides insights to guide both new and experienced users in optimizing their workloads and maximizing MareNostrum5's computational capabilities.
PanoGen++: Domain-Adapted Text-Guided Panoramic Environment Generation for Vision-and-Language Navigation
Venue: arXiv cs.CV 2025
Year: 2025

Vision-and-language navigation (VLN) tasks require agents to navigate three-dimensional environments guided by natural language instructions, offering substantial potential for diverse applications. However, the scarcity of training data impedes progress in this field. This paper introduces PanoGen++, a novel framework that addresses this limitation by generating varied and pertinent panoramic environments for VLN tasks. PanoGen++ incorporates pre-trained diffusion models with domain-specific fine-tuning, employing parameter-efficient techniques such as low-rank adaptation to minimize computational costs. We investigate two settings for environment generation: masked image inpainting and recursive image outpainting. The former maximizes novel environment creation by inpainting masked regions based on textual descriptions, while the latter facilitates agents' learning of spatial relationships within panoramas. Empirical evaluations on room-to-room (R2R), room-for-room (R4R), and cooperative vision-and-dialog navigation (CVDN) datasets reveal significant performance enhancements: a 2.44% increase in success rate on the R2R test leaderboard, a 0.63% improvement on the R4R validation unseen set, and a 0.75-meter enhancement in goal progress on the CVDN validation unseen set. PanoGen++ augments the diversity and relevance of training environments, resulting in improved generalization and efficacy in VLN tasks.
TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness
Venue: arXiv cs.CV 2025
Year: 2025

3D semantic occupancy has rapidly become a research focus in the fields of robotics and autonomous driving environment perception due to its ability to provide more realistic geometric perception and its closer integration with downstream tasks. By performing occupancy prediction of the 3D space in the environment, the ability and robustness of scene understanding can be effectively improved. However, existing occupancy prediction tasks are primarily modeled using voxel or point cloud-based approaches: voxel-based network structures often suffer from the loss of spatial information due to the voxelization process, while point cloud-based methods, although better at retaining spatial location information, face limitations in representing volumetric structural details. To address this issue, we propose a dual-modal prediction method based on 3D Gaussian sets and sparse points, which balances both spatial location and volumetric structural information, achieving higher accuracy in semantic occupancy prediction. Specifically, our method adopts a Transformer-based architecture, taking 3D Gaussian sets, sparse points, and queries as inputs. Through the multi-layer structure of the Transformer, the enhanced queries and 3D Gaussian sets jointly contribute to the semantic occupancy prediction, and an adaptive fusion mechanism integrates the semantic outputs of both modalities to generate the final prediction results. Additionally, to further improve accuracy, we dynamically refine the point cloud at each layer, allowing for more precise location information during occupancy prediction. We conducted experiments on the Occ3DnuScenes dataset, and the experimental results demonstrate superior performance of the proposed method on IoU based metrics.
Take Off the Training Wheels Progressive In-Context Learning for Effective Alignment
Venue: arXiv cs.CL 2025
Year: 2025

Recent studies have explored the working mechanisms of In-Context Learning (ICL). However, they mainly focus on classification and simple generation tasks, limiting their broader application to more complex generation tasks in practice. To address this gap, we investigate the impact of demonstrations on token representations within the practical alignment tasks. We find that the transformer embeds the task function learned from demonstrations into the separator token representation, which plays an important role in the generation of prior response tokens. Once the prior response tokens are determined, the demonstrations become redundant.Motivated by this finding, we propose an efficient Progressive In-Context Alignment (PICA) method consisting of two stages. In the first few-shot stage, the model generates several prior response tokens via standard ICL while concurrently extracting the ICL vector that stores the task function from the separator token representation. In the following zero-shot stage, this ICL vector guides the model to generate responses without further demonstrations.Extensive experiments demonstrate that our PICA not only surpasses vanilla ICL but also achieves comparable performance to other alignment tuning methods. The proposed training-free method reduces the time cost (e.g., 5.45+) with improved alignment performance (e.g., 6.57+). Consequently, our work highlights the application of ICL for alignment and calls for a deeper understanding of ICL for complex generations. The code will be available at https://github.com/HITsz-TMG/PICA.
Impact of buckypaper on the mechanical properties and failure modes of composites
Venue: arXiv cs.LG 2025
Year: 2025

Recently, there has been an interest in the incorporation of buckypaper (BP), or carbon nanotube (CNT) membranes, in composite laminates. Research has shown that using BP in contrast to nanotube doped resin enables the introduction of a higher CNT weight fraction which offers multiple benefits including higher piezo resistivity for health monitoring applications and enhanced mechanical response for structural applications. However, their impact on the deformation and failure mechanisms of composite laminates has not been investigated thoroughly. Understanding these issues experimentally would require a carefully executed test plan involving a multitude of design parameters such as BP geometry and placement, material anisotropy and variability, and laminate stacking sequence. This paper presents a deep learning (DL)-based surrogate model for studying the mechanical response of hybrid carbon fiber reinforced polymer (CFRP) composite laminates with BP interleaves under various mechanical loads. The surrogate model utilizes a long short-term memory architecture implemented within a DL framework and predicts the laminate global response for a given configuration, geometry, and loading condition. The DL framework training and cross-validation are performed via data acquisition from a series of three-point bend tests conducted through finite element analysis (FEA) and in-house experiments, respectively. The model predictions show good agreement with FEA simulations and experimental results, where CFRP with two BP interleaves showed enhanced flexural strength and modulus over pristine samples. This enhancement can be attributed to the excellent crack retardation capabilities of CNTs, particularly in the interlaminar region.
Demoting Security via Exploitation of Cache Demote Operation in Intel's Latest ISA Extension
Venue: arXiv cs.CR 2025
Year: 2025

ISA extensions are increasingly adopted to boost the performance of specialized workloads without requiring an entire architectural redesign. However, these enhancements can inadvertently expose new attack surfaces in the microarchitecture. In this paper, we investigate Intel's recently introduced cldemote extension, which promotes efficient data sharing by transferring cache lines from upper-level caches to the Last Level Cache (LLC). Despite its performance benefits, we uncover critical properties-unprivileged access, inter-cache state transition, and fault suppression-that render cldemote exploitable for microarchitectural attacks. We propose two new attack primitives, Flush+Demote and Demote+Time, built on our analysis. Flush+Demote constructs a covert channel with a bandwidth of 2.84 Mbps and a bit error rate of 0.018%, while Demote+Time derandomizes the kernel base address in 2.49 ms on Linux. Furthermore, we show that leveraging cldemote accelerates eviction set construction in non-inclusive LLC designs by obviating the need for helper threads or extensive cache conflicts, thereby reducing construction time by 36% yet retaining comparable success rates. Finally, we examine how ISA extensions contribute to broader microarchitectural attacks, identifying five key exploitable characteristics and categorizing four distinct attack types. We also discuss potential countermeasures, highlighting the far-reaching security implications of emerging ISA extensions.
Enhanced Route Planning with Calibrated Uncertainty Set
Venue: arXiv cs.LG 2025
Year: 2025

This paper investigates the application of probabilistic prediction methodologies in route planning within a road network context. Specifically, we introduce the Conformalized Quantile Regression for Graph Autoencoders (CQR-GAE), which leverages the conformal prediction technique to offer a coverage guarantee, thus improving the reliability and robustness of our predictions. By incorporating uncertainty sets derived from CQR-GAE, we substantially improve the decision-making process in route planning under a robust optimization framework. We demonstrate the effectiveness of our approach by applying the CQR-GAE model to a real-world traffic scenario. The results indicate that our model significantly outperforms baseline methods, offering a promising avenue for advancing intelligent transportation systems.
AgentDAO: Synthesis of Proposal Transactions Via Abstract DAO Semantics
Venue: arXiv cs.SE 2025
Year: 2025

While the trend of decentralized governance is obvious (cryptocurrencies and blockchains are widely adopted by multiple sovereign countries), initiating governance proposals within Decentralized Autonomous Organizations (DAOs) is still challenging, i.e., it requires providing a low-level transaction payload, therefore posing significant barriers to broad community participation. To address these challenges, we propose a multi-agent system powered by Large Language Models with a novel Label-Centric Retrieval algorithm to automate the translation from natural language inputs into executable proposal transactions. The system incorporates DAOLang, a Domain-Specific Language to simplify the specification of various governance proposals. The key optimization achieved by DAOLang is a semantic-aware abstraction of user input that reliably secures proposal generation with a low level of token demand. A preliminary evaluation on real-world applications reflects the potential of DAOLang in terms of generating complicated types of proposals with existing foundation models, e.g. GPT-4o.
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
Venue: arXiv cs.LG 2025
Year: 2025

Fine-tuning plays a crucial role in enabling pre-trained LLMs to evolve from general language comprehension to task-specific expertise. To preserve user data privacy, federated fine-tuning is often employed and has emerged as the de facto paradigm. However, federated fine-tuning is prohibitively inefficient due to the tension between LLM complexity and the resource constraint of end devices, incurring unaffordable fine-tuning overhead. Existing literature primarily utilizes parameter-efficient fine-tuning techniques to mitigate communication costs, yet computational and memory burdens continue to pose significant challenges for developers. This work proposes DropPEFT, an innovative federated PEFT framework that employs a novel stochastic transformer layer dropout method, enabling devices to deactivate a considerable fraction of LLMs layers during training, thereby eliminating the associated computational load and memory footprint. In DropPEFT, a key challenge is the proper configuration of dropout ratios for layers, as overhead and training performance are highly sensitive to this setting. To address this challenge, we adaptively assign optimal dropout-ratio configurations to devices through an exploration-exploitation strategy, achieving efficient and effective fine-tuning. Extensive experiments show that DropPEFT can achieve a 1.3-6.3\times speedup in model convergence and a 40%-67% reduction in memory footprint compared to state-of-the-art methods.
Flows on convex polytopes
Venue: arXiv cs.LG 2025
Year: 2025

We present a framework for modeling complex, high-dimensional distributions on convex polytopes by leveraging recent advances in discrete and continuous normalizing flows on Riemannian manifolds. We show that any full-dimensional polytope is homeomorphic to a unit ball, and our approach harnesses flows defined on the ball, mapping them back to the original polytope. Furthermore, we introduce a strategy to construct flows when only the vertex representation of a polytope is available, employing maximum entropy barycentric coordinates and Aitchison geometry. Our experiments take inspiration from applications in metabolic flux analysis and demonstrate that our methods achieve competitive density estimation, sampling accuracy, as well as fast training and inference times.
ARLED: Leveraging LED-based ARMAN Model for Abstractive Summarization of Persian Long Documents
Venue: arXiv cs.CL 2025
Year: 2025

The increasing volume of textual data poses challenges in reading and comprehending large documents, particularly for scholars who need to extract useful information from research articles. Automatic text summarization has emerged as a powerful tool to condense lengthy documents into concise and informative summaries. Depending on the approach used, text summarization can be categorized as either extractive or abstractive. While extractive methods are commonly used due to their simplicity, they often miss important information. On the other hand, Abstractive Summarization can generate more coherent and informative summaries by understanding the underlying meaning of the text. Abstractive techniques have gained attention in various languages, and recent advancements have been achieved through pre-training models such as BERT, BART, and T5. However, the challenge of summarizing long documents remains, and alternative models like Longformer have been introduced to address this limitation. In this context, this paper focuses on abstractive summarization in the Persian language. The authors introduce a new dataset of 300,000 full-text Persian papers obtained from the Ensani website and apply the ARMAN model, based on the Longformer architecture, to generate summaries. The experimental results demonstrate promising performance in Persian text summarization. The paper provides a comprehensive overview of related work, discusses the methodology, presents the experimental results, and concludes with future research directions.