OpenRaiser/Intern-Atlas huggingface dataset:
The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Sampling in score-based diffusion models can be performed by solving either a reverse-time stochastic differential equation (SDE) parameterized by an arbitrary time-dependent stochasticity parameter or a probability flow ODE, corresponding to the stochasticity parameter set to zero. In this work, we study the effect of this stochasticity on the generation process through bounds on the Kullback-Leibler (KL) divergence, complementing the analysis with numerical and analytical examples. Our main results apply to linear forward SDEs with additive noise and Lipschitz-continuous score functions, and quantify how errors from the prior distribution and score approximation propagate under different choices of the stochasticity parameter. The theoretical bounds are derived using log-Sobolev inequalities for the marginals of the forward process, which enable a more effective control of the KL divergence decay along sampling. For exact score functions, we find that stochasticity acts as an error-correcting mechanism, decreasing KL divergence along the sampling trajectory. For an approximate score function, there is a trade-off between error correction and score error amplification, so that stochasticity can either improve or worsen the performance, depending on the structure of the score error. Numerical experiments on simple datasets and a fully analytical example are included to illustrate and enlighten the theoretical results.
The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
Venue: arXiv cs.CY 2025
Year: 2025

This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data sovereignty and governance considerations unique to healthcare, strategic competitive advantages afforded by proprietary AI infrastructure, and the transformative potential of healthcare-specific foundation models for patient care and organizational operations. Through analysis of empirical evidence, economic frameworks, and organizational case studies, we demonstrate that proprietary multimodal foundation models enable healthcare organizations to achieve superior clinical performance, maintain robust data governance, create sustainable competitive advantages, and accelerate innovation pathways. While acknowledging implementation challenges, we present evidence showing organizations with proprietary AI capabilities demonstrate measurably improved outcomes, faster innovation cycles, and stronger strategic positioning in the evolving healthcare ecosystem. This analysis provides healthcare leaders with a comprehensive framework for evaluating build-versus-buy decisions regarding foundation model implementation, positioning proprietary foundation model development as a cornerstone capability for forward-thinking healthcare organizations.
Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Venue: arXiv cs.IR 2025
Year: 2025

With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined set of modeling- and system-level acceleration and optimization strategies. At the model level, we dramatically reduce parameter counts and compute requirements through lightweight network design, structured pruning, and weight quantization. At the system level, we integrate multiple heterogeneous compute platforms and high-performance inference libraries, and we design elastic inference scheduling and load-balancing mechanisms based on real-time load characteristics. Experiments show that, while maintaining the original recommendation accuracy, our methods cut latency to less than 30% of the baseline and more than double system throughput, offering a practical solution for deploying large-scale online recommendation services.
Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks
Venue: arXiv cs.LG 2025
Year: 2025

AI/ML research has predominantly been driven by domains such as computer vision, natural language processing, and video analysis. In contrast, the application of AI/ML to wireless networks, particularly at the air interface, remains in its early stages. Although there are emerging efforts to explore this intersection, fully realizing the potential of AI/ML in wireless communications requires a deep interdisciplinary understanding of both fields. We provide an overview of AI/ML-related discussions in 3GPP standardization, highlighting key use cases, architectural considerations, and technical requirements. We outline open research challenges and opportunities where academic and industrial communities can contribute to shaping the future of AI-enabled wireless systems.
VulStamp: Vulnerability Assessment using Large Language Model
Venue: arXiv cs.SE 2025
Year: 2025

Although modern vulnerability detection tools enable developers to efficiently identify numerous security flaws, indiscriminate remediation efforts often lead to superfluous development expenses. This is particularly true given that a substantial portion of detected vulnerabilities either possess low exploitability or would incur negligible impact in practical operational environments. Consequently, vulnerability severity assessment has emerged as a critical component in optimizing software development efficiency. Existing vulnerability assessment methods typically rely on manually crafted descriptions associated with source code artifacts. However, due to variability in description quality and subjectivity in intention interpretation, the performance of these methods is seriously limited. To address this issue, this paper introduces VulStamp, a novel intention-guided framework, to facilitate description-free vulnerability assessment. Specifically, VulStamp adopts static analysis together with Large Language Model (LLM) to extract the intention information of vulnerable code. Based on the intention information, VulStamp uses a prompt-tuned model for vulnerability assessment. Furthermore, to mitigate the problem of imbalanced data associated with vulnerability types, VulStamp integrates a Reinforcement Learning (RL)-based prompt-tuning method to train the assessment model.
Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency
Venue: arXiv cs.LG 2025
Year: 2025

Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, which introduces variability in diagnosis. This study investigates the use of machine learning to improve diagnostic consistency by analyzing voiding cystourethrogram (VCUG) images. A total of 113 VCUG images were reviewed, with expert grading of VUR severity. Nine image-based features were selected to train six predictive models: Logistic Regression, Decision Tree, Gradient Boosting, Neural Network, and Stochastic Gradient Descent. The models were evaluated using leave-one-out cross-validation. Analysis identified deformation patterns in the renal calyces as key indicators of high-grade VUR. All models achieved accurate classifications with no false positives or negatives. High sensitivity to subtle image patterns characteristic of different VUR grades was confirmed by substantial Area Under the Curve (AUC) values. The results suggest that machine learning can offer an objective and standardized alternative to current subjective VUR assessments. These findings highlight renal calyceal deformation as a strong predictor of severe cases. Future research should aim to expand the dataset, refine imaging features, and improve model generalizability for broader clinical use.
Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models
Venue: arXiv cs.CR 2025
Year: 2025

Multimodal large language models (MLLMs), which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computational resources necessitates the utilization of third-party data and open-source MLLMs, a trend that is increasingly observed in contemporary research. This prosperity masks significant security risks. Empirical studies demonstrate that the latest MLLMs can be manipulated to produce malicious or harmful content. This manipulation is facilitated exclusively through instructions or inputs, including adversarial perturbations and malevolent queries, effectively bypassing the internal security mechanisms embedded within the models. To gain a deeper comprehension of the inherent security vulnerabilities associated with audio-visual-based multimodal models, a series of surveys investigates various types of attacks, including adversarial and backdoor attacks. While existing surveys on audio-visual attacks provide a comprehensive overview, they are limited to specific types of attacks, which lack a unified review of various types of attacks. To address this issue and gain insights into the latest trends in the field, this paper presents a comprehensive and systematic review of audio-visual attacks, which include adversarial attacks, backdoor attacks, and jailbreak attacks. Furthermore, this paper also reviews various types of attacks in the latest audio-visual-based MLLMs, a dimension notably absent in existing surveys. Drawing upon comprehensive insights from a substantial review, this paper delineates both challenges and emergent trends for future research on audio-visual attacks and defense.
Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics
Venue: arXiv cs.LG 2025
Year: 2025

Predicting time-series is of great importance in various scientific and engineering fields. However, in the context of limited and noisy data, accurately predicting dynamics of all variables in a high-dimensional system is a challenging task due to their nonlinearity and also complex interactions. Current methods including deep learning approaches often perform poorly for real-world systems under such circumstances. This study introduces the Delayformer framework for simultaneously predicting dynamics of all variables, by developing a novel multivariate spatiotemporal information (mvSTI) transformation that makes each observed variable into a delay-embedded state (vector) and further cross-learns those states from different variables. From dynamical systems viewpoint, Delayformer predicts system states rather than individual variables, thus theoretically and computationally overcoming such nonlinearity and cross-interaction problems. Specifically, it first utilizes a single shared Visual Transformer (ViT) encoder to cross-represent dynamical states from observed variables in a delay embedded form and then employs distinct linear decoders for predicting next states, i.e. equivalently predicting all original variables parallelly. By leveraging the theoretical foundations of delay embedding theory and the representational capabilities of Transformers, Delayformer outperforms current state-of-the-art methods in forecasting tasks on both synthetic and real-world datasets. Furthermore, the potential of Delayformer as a foundational time-series model is demonstrated through cross-domain forecasting tasks, highlighting its broad applicability across various scenarios.
GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residuals
Venue: arXiv cs.CV 2025
Year: 2025

Initializing the state of a sensorized platform can be challenging, as a limited set of measurements often provide low-informative constraints that are in addition highly non-linear. This may lead to poor initial estimates that may converge to local minima during subsequent non-linear optimization. We propose an adaptive GNSS-inertial initialization strategy that delays the incorporation of global GNSS constraints until they become sufficiently informative. In the initial stage, our method leverages inter-epoch baseline vector residuals between consecutive GNSS fixes to mitigate inertial drift. To determine when to activate global constraints, we introduce a general criterion based on the evolution of the Hessian matrix's singular values, effectively quantifying system observability. Experiments on EuRoC, GVINS and MARS-LVIG datasets show that our approach consistently outperforms the naive strategy of fusing all measurements from the outset, yielding more accurate and robust initializations.
OCPQ: Object-Centric Process Querying & Constraints
Venue: arXiv cs.DB 2025
Year: 2025

Process querying is used to extract information and insights from process execution data. Similarly, process constraints can be checked against input data, yielding information on which process instances violate them. Traditionally, such process mining techniques use case-centric event data as input. However, with the uptake of Object-Centric Process Mining (OCPM), existing querying and constraint checking techniques are no longer applicable. Object-Centric Event Data (OCED) removes the requirement to pick a single case notion (i.e., requiring that events belong to exactly one case) and can thus represent many real-life processes much more accurately. In this paper, we present a novel highly-expressive approach for object-centric process querying, called OCPQ. It supports a wide variety of applications, including OCED-based constraint checking and filtering. The visual representation of nested queries in OCPQ allows users to intuitively read and create queries and constraints. We implemented our approach using (1) a high-performance execution engine backend and (2) an easy-to-use editor frontend. Additionally, we evaluated our approach on a real-life dataset, showing the lack in expressiveness of prior work and runtime performance significantly better than the general querying solutions SQLite and Neo4j, as well as comparable to the performance-focused DuckDB.
Augmenting the Generality and Performance of Large Language Models for Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.
Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study
Venue: arXiv cs.SE 2025
Year: 2025

Recent advancements in large language models (LLMs) have shown promise for automated vulnerability detection and repair in software systems. This paper investigates the performance of GPT-4o in repairing Java vulnerabilities from a widely used dataset (Vul4J), exploring how different contextual information affects automated vulnerability repair (AVR) capabilities. We compare the latest GPT-4o's performance against previous results with GPT-4 using identical prompts. We evaluated nine additional prompts crafted by us that contain various contextual information such as CWE or CVE information, and manually extracted code contexts. Each prompt was executed three times on 42 vulnerabilities, and the resulting fix candidates were validated using Vul4J's automated testing framework. Our results show that GPT-4o performed 11.9\% worse on average than GPT-4 with the same prompt, but was able to fix 10.5\% more distinct vulnerabilities in the three runs together. CVE information significantly improved repair rates, while the length of the task description had minimal impact. Combining CVE guidance with manually extracted code context resulted in the best performance. Using our \textsc{Top}-3 prompts together, GPT-4o repaired 26 (62\%) vulnerabilities at least once, outperforming both the original baseline (40\%) and its reproduction (45\%), suggesting that ensemble prompt strategies could improve vulnerability repair in zero-shot settings.
Learn to Preserve Personality: Federated Foundation Models in Recommendations
Venue: arXiv cs.LG 2025
Year: 2025

A core learning challenge for existed Foundation Models (FM) is striking the tradeoff between generalization with personalization, which is a dilemma that has been highlighted by various parameter-efficient adaptation techniques. Federated foundation models (FFM) provide a structural means to decouple shared knowledge from individual specific adaptations via decentralized processes. Recommendation systems offer a perfect testbed for FFMs, given their reliance on rich implicit feedback reflecting unique user characteristics. This position paper discusses a novel learning paradigm where FFMs not only harness their generalization capabilities but are specifically designed to preserve the integrity of user personality, illustrated thoroughly within the recommendation contexts. We envision future personal agents, powered by personalized adaptive FMs, guiding user decisions on content. Such an architecture promises a user centric, decentralized system where individuals maintain control over their personalized agents.
GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
Venue: arXiv cs.IR 2025
Year: 2025

GraphRAG-Causal introduces an innovative framework that combines graph-based retrieval with large language models to enhance causal reasoning in news analysis. Traditional NLP approaches often struggle with identifying complex, implicit causal links, especially in low-data scenarios. Our approach addresses these challenges by transforming annotated news headlines into structured causal knowledge graphs. It then employs a hybrid retrieval system that merges semantic embeddings with graph-based structural cues leveraging Neo4j to accurately match and retrieve relevant events. The framework is built on a three-stage pipeline: First, during Data Preparation, news sentences are meticulously annotated and converted into causal graphs capturing cause, effect, and trigger relationships. Next, the Graph Retrieval stage stores these graphs along with their embeddings in a Neo4j database and utilizes hybrid Cypher queries to efficiently identify events that share both semantic and structural similarities with a given query. Finally, the LLM Inference stage utilizes these retrieved causal graphs in a few-shot learning setup with XML-based prompting, enabling robust classification and tagging of causal relationships. Experimental evaluations demonstrate that GraphRAG-Causal achieves an impressive F1-score of 82.1% on causal classification using just 20 few-shot examples. This approach significantly boosts accuracy and consistency, making it highly suitable for real-time applications in news reliability assessment, misinformation detection, and policy analysis.
"If we misunderstand the client, we misspend 100 hours": Exploring conversational AI and response types for information elicitation
Venue: arXiv cs.HC 2025
Year: 2025

Client-designer alignment is crucial to the success of design projects, yet little research has explored how digital technologies might influence this alignment. To address this gap, this paper presents a three-phase study investigating how digital systems can support requirements elicitation in professional design practice. Specifically, it examines how integrating a conversational agent and choice-based response formats into a digital elicitation tool affects early-stage client-designer collaboration. The first phase of the study inquired into the current practices of 10 design companies through semi-structured interviews, informing the system's design. The second phase evaluated the system using a 2x2 factorial design with 50 mock clients, quantifying the effects of conversational AI and response type on user experience and perceived preparedness. In phase three, the system was presented to seven of the original 10 companies to gather reflections on its value, limitations, and potential integration into practice. Findings show that both conversational AI and choice-based responses lead to lower dependability scores on the User Experience Questionnaire, yet result in client input with greater clarity. We contribute design implications for integrating conversational AI and choice-based responses into elicitation tools to support mutual understanding in early-stage client-designer collaboration.
Physically-informed change-point kernels for structural dynamics
Venue: arXiv cs.LG 2025
Year: 2025

The relative balance between physics and data within any physics-informed machine learner is an important modelling consideration to ensure that the benefits of both physics and data-based approaches are maximised. An over reliance on physical knowledge can be detrimental, particularly when the physics-based component of a model may not accurately represent the true underlying system. An underutilisation of physical knowledge potentially wastes a valuable resource, along with benefits in model interpretability and reduced demand for expensive data collection. Achieving an optimal physics-data balance is a challenging aspect of model design, particularly if the level varies through time; for example, one might have a physical approximation, only valid within particular regimes, or a physical phenomenon may be known to only occur when given conditions are met (e.g. at high temperatures). This paper develops novel, physically-informed, change-point kernels for Gaussian processes, capable of dynamically varying the reliance upon available physical knowledge. A high level of control is granted to a user, allowing for the definition of conditions in which they believe a phenomena should occur and the rate at which the knowledge should be phased in and out of a model. In circumstances where users may be less certain, the switching reliance upon physical knowledge may be automatically learned and recovered from the model in an interpretable and intuitive manner. Variation of the modelled noise based on the physical phenomena occurring is also implemented to provide a more representative capture of uncertainty alongside predictions. The capabilities of the new kernel structures are explored through the use of two engineering case studies: the directional wind loading of a cable-stayed bridge and the prediction of aircraft wing strain during in-flight manoeuvring.
Two Tiling is Undecidable
Venue: arXiv cs.CG 2025
Year: 2025

We show that the following problem is undecidable: given two polygonal prototiles, determine whether the plane can be tiled with rotated and translated copies of them. This improves a result of Demaine and Langerman [SoCG 2025], who showed undecidability for three tiles. Along the way, we show that tiling with one prototile is undecidable if there can be edge-to-edge matching rules. This is the first result to show undecidability for monotiling with only local matching constraints.