OpenRaiser/Intern-Atlas huggingface dataset:
FaultGPT: Industrial Fault Diagnosis Question Answering System by Vision Language Models
Venue: arXiv cs.ET 2025
Year: 2025

Recently, employing single-modality large language models based on mechanical vibration signals as Tuning Predictors has introduced new perspectives in intelligent fault diagnosis. However, the potential of these methods to leverage multimodal data remains underexploited, particularly in complex mechanical systems where relying on a single data source often fails to capture comprehensive fault information. In this paper, we present FaultGPT, a novel model that generates fault diagnosis reports directly from raw vibration signals. By leveraging large vision-language models (LVLM) and text-based supervision, FaultGPT performs end-to-end fault diagnosis question answering (FDQA), distinguishing itself from traditional classification or regression approaches. Specifically, we construct a large-scale FDQA instruction dataset for instruction tuning of LVLM. This dataset includes vibration time-frequency image-text label pairs and human instruction-ground truth pairs. To enhance the capability in generating high-quality fault diagnosis reports, we design a multi-scale cross-modal image decoder to extract fine-grained fault semantics and conducted instruction tuning without introducing additional training parameters into the LVLM. Extensive experiments, including fault diagnosis report generation, few-shot and zero-shot evaluation across multiple datasets, validate the superior performance and adaptability of FaultGPT in diverse industrial scenarios.
Hiku: Pull-Based Scheduling for Serverless Computing
Venue: arXiv cs.DC 2025
Year: 2025

Serverless computing promises convenient abstractions for developing and deploying functions that execute in response to events. In such Function-as-a-Service (FaaS) platforms, scheduling is an integral task, but current scheduling algorithms often struggle with maintaining balanced loads, minimizing cold starts, and adapting to commonly occurring bursty workloads. In this work, we propose pull-based scheduling as a novel scheduling algorithm for serverless computing. Our key idea is to decouple worker selection from task assignment, with idle workers requesting new tasks proactively. Experimental evaluation on an open-source FaaS platform shows that pull-based scheduling, compared to other existing scheduling algorithms, significantly improves the performance and load balancing of serverless workloads, especially under high concurrency. The proposed algorithm improves response latencies by 14.9% compared to hash-based scheduling, reduces the frequency of cold starts from 43% to 30%, increases throughput by 8.3%, and achieves a more even load distribution by 12.9% measured by the requests assigned per worker.
NPB-Rust: NAS Parallel Benchmarks in Rust
Venue: arXiv cs.DC 2025
Year: 2025

Parallel programming often requires developers to handle complex computational tasks that can yield many errors in its development cycle. Rust is a performant low-level language that promises memory safety guarantees with its compiler, making it an attractive option for HPC application developers. We identified that the Rust ecosystem could benefit from more comprehensive scientific benchmark suites for standardizing comparisons and research. The NAS Parallel Benchmarks (NPB) is a standardized suite for evaluating various hardware aspects and is often used to compare different frameworks for parallelism. Therefore, our contributions are a Rust version of NPB, an analysis of the expressiveness and performance of the language features, and parallelization strategies. We compare our implementation with consolidated sequential and parallel versions of NPB. Experimental results show that Rust's sequential version is 1.23\% slower than Fortran and 5.59\% faster than C++, while Rust with Rayon was slower than both Fortran and C++ with OpenMP.
Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations
Venue: arXiv cs.IR 2025
Year: 2025

With the explosive growth of multimodal content online, pre-trained visual-language models have shown great potential for multimodal recommendation. However, while these models achieve decent performance when applied in a frozen manner, surprisingly, due to significant domain gaps (e.g., feature distribution discrepancy and task objective misalignment) between pre-training and personalized recommendation, adopting a joint training approach instead leads to performance worse than baseline. Existing approaches either rely on simple feature extraction or require computationally expensive full model fine-tuning, struggling to balance effectiveness and efficiency. To tackle these challenges, we propose \textbf{P}arameter-efficient \textbf{T}uning for \textbf{M}ultimodal \textbf{Rec}ommendation (\textbf{PTMRec}), a novel framework that bridges the domain gap between pre-trained models and recommendation systems through a knowledge-guided dual-stage parameter-efficient training strategy. This framework not only eliminates the need for costly additional pre-training but also flexibly accommodates various parameter-efficient tuning methods.
Starlink in Northern Europe: A New Look at Stationary and In-motion Performance
Venue: arXiv cs.NI 2025
Year: 2025

Starlink has introduced the Flat High Performance (FHP) terminal, specifically designed to support the vehicles and the vessels in motion as well as the high-demand stationary users. The research on FHP terminal throughput analysis remains limited, only a few existing studies evaluate FHP, focusing on the limited parameters and scenarios. This paper evaluates the FHP terminal's performance in Finland, Northern Europe. We examine round-trip time (RTT), uplink, and downlink throughput for both stationary and in-motion use. We measure network efficiency across six geographically diverse servers and get insights of network routing strategies. Our results show that Starlink provides high-speed, low-RTT connectivity, however, the throughput experiences fluctuations with slight degradation when in motion. Additionally, we compare Starlink and terrestrial network RTT and possible routing paths.
FLARE: Fault Attack Leveraging Address Reconfiguration Exploits in Multi-Tenant FPGAs
Venue: arXiv cs.CR 2025
Year: 2025

Modern FPGAs are increasingly supporting multi-tenancy to enable dynamic reconfiguration of user modules. While multi-tenant FPGAs improve utilization and flexibility, this paradigm introduces critical security threats. In this paper, we present FLARE, a fault attack that exploits vulnerabilities in the partial reconfiguration process, specifically while a user bitstream is being uploaded to the FPGA by a reconfiguration manager. Unlike traditional fault attacks that operate during module runtime, FLARE injects faults in the bitstream during its reconfiguration, altering the configuration address and redirecting it to unintended partial reconfigurable regions (PRRs). This enables the overwriting of pre-configured co-tenant modules, disrupting their functionality. FLARE leverages power-wasters that activate briefly during the reconfiguration process, making the attack stealthy and more challenging to detect with existing countermeasures. Experimental results on a Xilinx Pynq FPGA demonstrate the effectiveness of FLARE in compromising multiple user bitstreams during the reconfiguration process.
Bugfix: a standard language, database schema and repository for research on bugs and automatic program repair
Venue: arXiv cs.SE 2025
Year: 2025

Automatic Program Repair (APR) is a brilliant idea: when detecting a bug, also provide suggestions for correcting the program. Progress towards that goal is hindered by the absence of a common frame of reference for the multiplicity of APR ideas, methods, tools, programming languages and environments. Bugfix is an effort at providing such a framework: a standardized set of notations, tools and interfaces, as well as a database of bugs and fixes, for use by the APR research community to try out ideas and compare results. The most directly visible component of the Bugfix effort is the Bugfix language, a human-readable formalism making it possible to describe elements of the following kinds: a bug (described abstractly, for example the permutation of two arguments in a call); a bug example (an actual occurrence of a bug, in a specific code written in a specific programming language, and usually recorded in some repository); a fix (a particular correction of a bug, obtained for example by reversing the misplaced arguments); an application (an entity that demonstrates how a actual code example matches with a fix); a construct (the abstract description of a programming mechanism, for example a ``while'' loop, independently of its realization in a programming language; and a language (a description of how a particular programming language includes certain constructs and provides specific concrete syntax for each of them -- for example Java includes loop, assignment etc. and has a defined format for each of them). A JSON API provides it in a form accessible to tools. Bugfix includes a repository containing a considerable amount of bugs, examples and fixes. Note: An early step towards this article was a short contribution (Ref [1]) to the 2024 ICSE. The present text reuses a few elements of introduction and motivation but is otherwise thoroughly reworked and extended.
Logit Disagreement: OoD Detection with Bayesian Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Bayesian neural networks (BNNs), which estimate the full posterior distribution over model parameters, are well-known for their role in uncertainty quantification and its promising application in out-of-distribution detection (OoD). Amongst other uncertainty measures, BNNs provide a state-of-the art estimation of predictive entropy (total uncertainty) which can be decomposed as the sum of mutual information and expected entropy. In the context of OoD detection the estimation of predictive uncertainty in the form of the predictive entropy score confounds aleatoric and epistemic uncertainty, the latter being hypothesized to be high for OoD points. Despite these justifications, the mutual information score has been shown to perform worse than predictive entropy. Taking inspiration from Bayesian variational autoencoder (BVAE) literature, this work proposes to measure the disagreement between a corrected version of the pre-softmax quantities, otherwise known as logits, as an estimate of epistemic uncertainty for Bayesian NNs under mean field variational inference. The three proposed epistemic uncertainty scores demonstrate marked improvements over mutual information on a range of OoD experiments, with equal performance otherwise. Moreover, the epistemic uncertainty scores perform on par with the Bayesian benchmark predictive entropy on a range of MNIST and CIFAR10 experiments.
FLEKE: Federated Locate-then-Edit Knowledge Editing
Venue: arXiv cs.CL 2025
Year: 2025

Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns. To address these challenges, we introduce Federated Locate-then-Edit Knowledge Editing (FLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse. In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations. Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FLEKE task with our FedEdit framework. Our code is available at https://github.com/zongkaiz/FLEKE.
ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

The objective in this paper is to improve the performance of text-to-image retrieval. To this end, we introduce a new framework that can boost the performance of large-scale pre-trained vision-language models, so that they can be used for text-to-image re-ranking. The approach, Enhanced Language-Image Pre-training (ELIP), uses the text query, via a simple MLP mapping network, to predict a set of visual prompts to condition the ViT image encoding. ELIP can easily be applied to the commonly used CLIP, SigLIP and BLIP-2 networks. To train the architecture with limited computing resources, we develop a 'student friendly' best practice, involving global hard sample mining, and curation of a large-scale dataset. On the evaluation side, we set up two new out-of-distribution (OOD) benchmarks, Occluded COCO and ImageNet-R, to assess the zero-shot generalisation of the models to different domains. The results demonstrate that ELIP significantly boosts CLIP/SigLIP/SigLIP-2 text-to-image retrieval performance and outperforms BLIP-2 on several benchmarks, as well as providing an easy means to adapt to OOD datasets.
EMK-KEN: A High-Performance Approach for Assessing Knowledge Value in Citation Network
Venue: arXiv cs.IR 2025
Year: 2025

With the explosive growth of academic literature, effectively evaluating the knowledge value of literature has become quite essential. However, most of the existing methods focus on modeling the entire citation network, which is structurally complex and often suffers from long sequence dependencies when dealing with text embeddings. Thus, they might have low efficiency and poor robustness in different fields. To address these issues, a novel knowledge evaluation method is proposed, called EMK-KEN. The model consists of two modules. Specifically, the first module utilizes MetaFP and Mamba to capture semantic features of node metadata and text embeddings to learn contextual representations of each paper. The second module utilizes KAN to further capture the structural information of citation networks in order to learn the differences in different fields of networks. Extensive experiments based on ten benchmark datasets show that our method outperforms the state-of-the-art competitors in effectiveness and robustness.
A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions
Venue: arXiv cs.IR 2025
Year: 2025

Acquiring valuable data from the rapidly expanding information on the internet has become a significant concern, and recommender systems have emerged as a widely used and effective tool for helping users discover items of interest. The essence of recommender systems lies in their ability to predict users' ratings or preferences for various items and subsequently recommend the most relevant ones based on historical interaction data and publicly available information. With the advent of diverse multimedia services, including text, images, video, and audio, humans can perceive the world through multiple modalities. Consequently, a recommender system capable of understanding and interpreting different modal data can more effectively refer to individual preferences. Multimodal Recommender Systems (MRS) not only capture implicit interaction information across multiple modalities but also have the potential to uncover hidden relationships between these modalities. The primary objective of this survey is to comprehensively review recent research advancements in MRS and to analyze the models from a technical perspective. Specifically, we aim to summarize the general process and main challenges of MRS from a technical perspective. We then introduce the existing MRS models by categorizing them into four key areas: Feature Extraction, Encoder, Multimodal Fusion, and Loss Function. Finally, we further discuss potential future directions for developing and enhancing MRS. This survey serves as a comprehensive guide for researchers and practitioners in MRS field, providing insights into the current state of MRS technology and identifying areas for future research. We hope to contribute to developing a more sophisticated and effective multimodal recommender system. To access more details of this paper, we open source a repository: https://github.com/Jinfeng-Xu/Awesome-Multimodal-Recommender-Systems.
Making Sense of Data in the Wild: Data Analysis Automation at Scale
Venue: arXiv cs.IR 2025
Year: 2025

As the volume of publicly available data continues to grow, researchers face the challenge of limited diversity in benchmarking machine learning tasks. Although thousands of datasets are available in public repositories, the sheer abundance often complicates the search for suitable data, leaving many valuable datasets underexplored. This situation is further amplified by the fact that, despite longstanding advocacy for improving data curation quality, current solutions remain prohibitively time-consuming and resource-intensive. In this paper, we propose a novel approach that combines intelligent agents with retrieval augmented generation to automate data analysis, dataset curation and indexing at scale. Our system leverages multiple agents to analyze raw, unstructured data across public repositories, generating dataset reports and interactive visual indexes that can be easily explored. We demonstrate that our approach results in more detailed dataset descriptions, higher hit rates and greater diversity in dataset retrieval tasks. Additionally, we show that the dataset reports generated by our method can be leveraged by other machine learning models to improve the performance on specific tasks, such as improving the accuracy and realism of synthetic data generation. By streamlining the process of transforming raw data into machine-learning-ready datasets, our approach enables researchers to better utilize existing data resources.
InnerSource Circumplex Model: Mapping Cross-organizational Developer Collaboration Patterns with Insights from Japanese Corporate Experience
Venue: arXiv cs.SE 2025
Year: 2025

This paper presents a comprehensive analysis of InnerSource adoption processes and their evolution within enterprises. First, a comparative analysis of Japanese and global enterprises highlights differences in the state of software sharing, perceptions of its importance, and barriers to implementation. Next, this study demonstrates that InnerSource adoption involves multi-layered, topological evolution beyond conventional staged models of program evolution. The research proposes three theoretical frameworks: InnerSource Topologies, which conceptualizes collaborative structures and categorizes internal collaboration levels; the Multi-layered Incentive Model, which combines monetary and non-monetary rewards at individual and project levels; and the InnerSource Circumplex Model, which helps organizations define InnerSource forms based on their specific needs. By mapping InnerSource evolution as a circumplex rather than simple staged progression, leaders can better adjust their focus during implementation. These frameworks help refine the previously ambiguous concept of InnerSource from the perspectives of sharing scope and community growth. These findings reaffirm that successful InnerSource adoption requires the parallel pursuit of top-down program structuring and bottom-up voluntary collaboration. They also contribute to fostering a sustainable innovation culture and enhancing software-sharing practices within enterprises. Furthermore, the newly proposed frameworks, particularly the Circumplex Model, offer versatile guidelines for organizations of varying cultural backgrounds and scales, enabling them to flexibly redefine and introduce InnerSource. This research is thus expected to advance corporate software sharing and spur innovation in diverse industrial contexts.
Physics-consistent machine learning: output projection onto physical manifolds
Venue: arXiv cs.LG 2025
Year: 2025

Data-driven machine learning models often require extensive datasets, which can be costly or inaccessible, and their predictions may fail to comply with established physical laws. Current approaches for incorporating physical priors mitigate these issues by penalizing deviations from known physical laws, as in physics-informed neural networks, or by designing architectures that automatically satisfy specific invariants. However, penalization approaches do not guarantee compliance with physical constraints for unseen inputs, and invariant-based methods lack flexibility and generality. We propose a novel physics-consistent machine learning method that directly enforces compliance with physical principles by projecting model outputs onto the manifold defined by these laws. This procedure ensures that predictions inherently adhere to the chosen physical constraints, improving reliability and interpretability. Our method is demonstrated on two systems: a spring-mass system and a low-temperature reactive plasma. Compared to purely data-driven models, our approach significantly reduces errors in physical law compliance, enhances predictive accuracy of physical quantities, and outperforms alternatives when working with simpler models or limited datasets. The proposed projection-based technique is versatile and can function independently or in conjunction with existing physics-informed neural networks, offering a powerful, general, and scalable solution for developing fast and reliable surrogate models of complex physical systems, particularly in resource-constrained scenarios.
TRKM: Twin Restricted Kernel Machines for Classification and Regression
Venue: arXiv cs.LG 2025
Year: 2025

Restricted kernel machines (RKMs) have considerably improved generalization in machine learning. Recent advancements explored various techniques within the RKM framework, integrating kernel functions with least squares support vector machines (LSSVM) to mirror the energy function of restricted Boltzmann machines (RBM), leading to enhanced performance. However, RKMs may face challenges in generalization when dealing with unevenly distributed or complexly clustered data. Additionally, as the dataset size increases, the computational burden of managing high-dimensional feature spaces can become substantial, potentially hindering performance in large-scale datasets. To address these challenges, we propose twin restricted kernel machine (TRKM). TRKM combines the benefits of twin models with the robustness of the RKM framework to enhance classification and regression tasks. By leveraging the Fenchel-Young inequality, we introduce a novel conjugate feature duality, allowing the formulation of classification and regression problems in terms of dual variables. This duality provides an upper bound to the objective function of the TRKM problem, resulting in a new methodology under the RKM framework. The model uses an energy function similar to that of RBM, incorporating both visible and hidden variables corresponding to both classes. Additionally, the kernel trick is employed to map data into a high-dimensional feature space, where the model identifies an optimal separating hyperplane using a regularized least squares approach. Experiments on UCI and KEEL datasets confirm TRKM's superiority over baselines, showcasing its robustness and efficiency in handling complex data. Furthermore, We implemented the TRKM model on the brain age dataset, demonstrating its efficacy in predicting brain age.