OpenRaiser/Intern-Atlas huggingface dataset:
Niyama : Breaking the Silos of LLM Inference Serving
Venue: arXiv cs.LG 2025
Year: 2025

The widespread adoption of Large Language Models (LLMs) has enabled diverse applications with very different latency requirements. Existing LLM serving frameworks rely on siloed infrastructure with coarse-grained workload segregation -- interactive and batch -- leading to inefficient resource utilization and limited support for fine-grained Quality-of-Service (QoS) differentiation. This results in operational inefficiencies, over-provisioning and poor load management during traffic surges. We present Niyama, a novel QoS-driven inference serving system that enables efficient co-scheduling of diverse workloads on shared infrastructure. Niyama introduces fine-grained QoS classification allowing applications to specify precise latency requirements, and dynamically adapts scheduling decisions based on real-time system state. Leveraging the predictable execution characteristics of LLM inference, Niyama implements a dynamic chunking mechanism to improve overall throughput while maintaining strict QoS guarantees. Additionally, Niyama employs a hybrid prioritization policy that balances fairness and efficiency, and employs selective request relegation that enables graceful service degradation during overload conditions. Our evaluation demonstrates that Niyama increases serving capacity by 32% compared to current siloed deployments, while maintaining QoS guarantees. Notably, under extreme load, our system reduces SLO violations by an order of magnitude compared to current strategies.
Do Researchers Benefit Career-wise from Involvement in International Policy Guideline Development?
Venue: arXiv cs.SI 2025
Year: 2025

Researchers are no longer limited to producing knowledge; in today's complex world, they also address societal challenges by engaging in policymaking. Although involvement in policymaking has expanded, direct empirical evidence of its career benefits remains underexplored. Prior survey-based studies suggest potential advantages-such as broader professional networks and enhanced opportunities-yet raise concerns about insufficient institutional support. Here, we examine the 2021 WHO global air quality guideline-a science-based regulatory guideline-as a case study. To evaluate the impact of guideline development on research outcomes, we match guideline researchers with a control group of peers sharing similar research topics and prior performance. Our analysis reveals that guideline researchers attain higher future citation counts in both academic and policy domains. New collaborations formed during development yield publications with higher citation impact and the disruptive index. Moreover, about half the guideline's references are derived from guideline researchers' papers, highlighting their central role in shaping the evidence base. These results provide empirical support for the career benefits of policy engagement. Our findings indicate that engaging in international guideline development offers tangible career incentives for researchers, and that institutions can enhance research impact and promote innovative scientific progress by actively supporting their researchers' participation in such initiatives.
On the Alignment of Post-Publication Reviews & Bibliometric and Altmetric Impact -- A Case Study on Expert Statements from the Science Media Center Germany
Venue: arXiv cs.DL 2025
Year: 2025

In the context of academic publishing and peer review, this study investigates the relationship between post-publication expert evaluations, their agreement levels, and the subsequent scientific and public recognition of the reviewed research. Using expert statements from the Science Media Center Germany as a dataset, we analyze Research in Context reviews to examine the alignment between qualitative post-publication assessments and bibliometric as well as altmetric indicators. We employ a Large Language Model to translate unstructured expert reviews into a structured rating scheme. Furthermore, we correlate these evaluations with citation counts from the Web of Science and alternative impact metrics such as the Altmetric Attention Score, news mentions, and Mendeley readership statistics from the Altmetric Explorer. We investigate the alignment of positive or critical post-publication reviews and high or low citation or altmetric counts.
Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Multi-view or 4D video generation has emerged as a significant research topic. Nonetheless, recent approaches to 4D generation still struggle with fundamental limitations, as they primarily rely on harnessing multiple video diffusion models with additional training or compute-intensive training of a full 4D diffusion model with limited real-world 4D data and large computational costs. To address these challenges, here we propose the first training-free 4D video generation method that leverages the off-the-shelf video diffusion models to generate multi-view videos from a single input video. Our approach consists of two key steps: (1) By designating the edge frames in the spatio-temporal sampling grid as key frames, we first synthesize them using a video diffusion model, leveraging a depth-based warping technique for guidance. This approach ensures structural consistency across the generated frames, preserving spatial and temporal coherence. (2) We then interpolate the remaining frames using a video diffusion model, constructing a fully populated and temporally coherent sampling grid while preserving spatial and temporal consistency. Through this approach, we extend a single video into a multi-view video along novel camera trajectories while maintaining spatio-temporal consistency. Our method is training-free and fully utilizes an off-the-shelf video diffusion model, offering a practical and effective solution for multi-view video generation.
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Sequential Recommendation (SeqRec) aims to predict the next item by capturing sequential patterns from users' historical interactions, playing a crucial role in many real-world recommender systems. However, existing approaches predominantly adopt a direct forward computation paradigm, where the final hidden state of the sequence encoder serves as the user representation. We argue that this inference paradigm, due to its limited computational depth, struggles to model the complex evolving nature of user preferences and lacks a nuanced understanding of long-tail items, leading to suboptimal performance. To address this issue, we propose \textbf{ReaRec}, the first inference-time computing framework for recommender systems, which enhances user representations through implicit multi-step reasoning. Specifically, ReaRec autoregressively feeds the sequence's last hidden state into the sequential recommender while incorporating special reasoning position embeddings to decouple the original item encoding space from the multi-step reasoning space. Moreover, we introduce two lightweight reasoning-based learning methods, Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), to further effectively exploit ReaRec's reasoning potential. Extensive experiments on five public real-world datasets and different SeqRec architectures demonstrate the generality and effectiveness of our proposed ReaRec. Remarkably, post-hoc analyses reveal that ReaRec significantly elevates the performance ceiling of multiple sequential recommendation backbones by approximately 30\%-50\%. Thus, we believe this work can open a new and promising avenue for future research in inference-time computing for sequential recommendation.
TranSplat: Instant Cross-Scene Object Relighting in Gaussian Splatting via Spherical Harmonic Transfer
Venue: arXiv cs.CV 2025
Year: 2025

We present TranSplat, a method for fast and accurate object relighting for the 3D Gaussian Splatting (GS) framework when transferring a 3D object from a source GS scene to a target GS scene. TranSplat is based on a theoretical radiance transfer identity for cross-scene relighting of objects with radially symmetric BRDFs that involves only taking simple products of spherical harmonic appearance coefficients of the object, source, and target environment maps without any explicit computation of scene quantities (e.g., the BRDFs themselves). TranSplat is the first method to demonstrate how this theoretical identity may be used to perform relighting within the GS framework, and furthermore, by automatically inferring unknown source and target environment maps directly from the source and target scene GS representations. We evaluated TranSplat on several synthetic and real-world scenes and objects, demonstrating comparable 3D object relighting performance to recent conventional inverse rendering-based GS methods with a fraction of their runtime. While TranSplat is theoretically best-suited for radially symmetric BRDFs, results demonstrate that TranSplat still offers perceptually realistic renderings on real scenes and opens a valuable, lightweight path forward to relighting with the GS framework.
Truth in Text: A Meta-Analysis of ML-Based Cyber Information Influence Detection Approaches
Venue: arXiv cs.CR 2025
Year: 2025

Cyber information influence, or disinformation in general terms, is widely regarded as one of the biggest threats to social progress and government stability. From US presidential elections to European Union referendums and down to regional news reporting of wildfires, lies and post-truths have normalized radical decision-making. Accordingly, there has been an explosion in research seeking to detect disinformation in online media. The frontier of disinformation detection research is leveraging a variety of ML techniques such as traditional ML algorithms like Support Vector Machines, Random Forest, and Naïve Bayes. Other research has applied deep learning models including Convolutional Neural Networks, Long Short-Term Memory networks, and transformer-based architectures. Despite the overall success of such techniques, the literature demonstrates inconsistencies when viewed holistically which limits our understanding of the true effectiveness. Accordingly, this work employed a two-stage meta-analysis to (a) demonstrate an overall meta statistic for ML model effectiveness in detecting disinformation and (b) investigate the same by subgroups of ML model types. The study found the majority of the 81 ML detection techniques sampled have greater than an 80\% accuracy with a Mean sample effectiveness of 79.18\% accuracy. Meanwhile, subgroups demonstrated no statistically significant difference between-approaches but revealed high within-group variance. Based on the results, this work recommends future work in replication and development of detection methods operating at the ML model level.
From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk
Venue: arXiv cs.LG 2025
Year: 2025

Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.
InfoBid: A Simulation Framework for Studying Information Disclosure in Auctions with Large Language Model-based Agents
Venue: arXiv cs.GT 2025
Year: 2025

In online advertising systems, publishers often face a trade-off in information disclosure strategies: while disclosing more information can enhance efficiency by enabling optimal allocation of ad impressions, it may lose revenue potential by decreasing uncertainty among competing advertisers. Similar to other challenges in market design, understanding this trade-off is constrained by limited access to real-world data, leading researchers and practitioners to turn to simulation frameworks. The recent emergence of large language models (LLMs) offers a novel approach to simulations, providing human-like reasoning and adaptability without necessarily relying on explicit assumptions about agent behavior modeling. Despite their potential, existing frameworks have yet to integrate LLM-based agents for studying information asymmetry and signaling strategies, particularly in the context of auctions. To address this gap, we introduce InfoBid, a flexible simulation framework that leverages LLM agents to examine the effects of information disclosure strategies in multi-agent auction settings. Using GPT-4o, we implemented simulations of second-price auctions with diverse information schemas. The results reveal key insights into how signaling influences strategic behavior and auction outcomes, which align with both economic and social learning theories. Through InfoBid, we hope to foster the use of LLMs as proxies for human economic and social agents in empirical studies, enhancing our understanding of their capabilities and limitations. This work bridges the gap between theoretical market designs and practical applications, advancing research in market simulations, information design, and agent-based reasoning while offering a valuable tool for exploring the dynamics of digital economies.
Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
Venue: arXiv cs.LG 2025
Year: 2025

This study investigates rare event detection on tabular data within binary classification. Standard techniques to handle class imbalance include SMOTE, which generates synthetic samples from the minority class. However, SMOTE is intrinsically designed for continuous input variables. In fact, despite SMOTE-NC-its default extension to handle mixed features (continuous and categorical variables)-very few works propose procedures to synthesize mixed features. On the other hand, many real-world classification tasks, such as in banking sector, deal with mixed features, which have a significant impact on predictive performances. To this purpose, we introduce MGS-GRF, an oversampling strategy designed for mixed features. This method uses a kernel density estimator with locally estimated full-rank covariances to generate continuous features, while categorical ones are drawn from the original samples through a generalized random forest. Empirically, contrary to SMOTE-NC, we show that MGS-GRF exhibits two important properties: (i) the coherence i.e. the ability to only generate combinations of categorical features that are already present in the original dataset and (ii) association, i.e. the ability to preserve the dependence between continuous and categorical features. We also evaluate the predictive performances of LightGBM classifiers trained on data sets, augmented with synthetic samples from various strategies. Our comparison is performed on simulated and public real-world data sets, as well as on a private data set from a leading financial institution. We observe that synthetic procedures that have the properties of coherence and association display better predictive performances in terms of various predictive metrics (PR and ROC AUC...), with MGS-GRF being the best one. Furthermore, our method exhibits promising results for the private banking application, with development pipeline being compliant with regulatory constraints.
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
Venue: arXiv cs.LG 2025
Year: 2025

In the era of data-driven machine-learning applications, privacy concerns and the scarcity of labeled data have become paramount challenges. These challenges are particularly pronounced in the domain of few-shot learning, where the ability to learn from limited labeled data is crucial. Privacy-preserving few-shot learning algorithms have emerged as a promising solution to address such pronounced challenges. However, it is well-known that privacy-preserving techniques often lead to a drop in utility due to the fundamental trade-off between data privacy and model performance. To enhance the utility of privacy-preserving few-shot learning methods, we introduce a novel approach called Meta-Clip. This technique is specifically designed for meta-learning algorithms, including Differentially Private (DP) model-agnostic meta-learning, DP-Reptile, and DP-MetaSGD algorithms, with the objective of balancing data privacy preservation with learning capacity maximization. By dynamically adjusting clipping thresholds during the training process, our Adaptive Clipping method provides fine-grained control over the disclosure of sensitive information, mitigating overfitting on small datasets and significantly improving the generalization performance of meta-learning models. Through comprehensive experiments on diverse benchmark datasets, we demonstrate the effectiveness of our approach in minimizing utility degradation, showcasing a superior privacy-utility trade-off compared to existing privacy-preserving techniques. The adoption of Adaptive Clipping represents a substantial step forward in the field of privacy-preserving few-shot learning, empowering the development of secure and accurate models for real-world applications, especially in scenarios where there are limited data availability.
Model Lake: a New Alternative for Machine Learning Models Management and Governance
Venue: arXiv cs.LG 2025
Year: 2025

The rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Traditional approaches to ML models management often involve disparate storage systems and lack standardized methodologies for versioning, audit, and re-use. Inspired by data lake concepts, this paper develops the concept of ML Model Lake as a centralized management framework for datasets, codes, and models within organizations environments. We provide an in-depth exploration of the Model Lake concept, delineating its architectural foundations, key components, operational benefits, and practical challenges. We discuss the transformative potential of adopting a Model Lake approach, such as enhanced model lifecycle management, discovery, audit, and reusability. Furthermore, we illustrate a real-world application of Model Lake and its transformative impact on data, code and model management practices.
Towards an intelligent assessment system for evaluating the development of algorithmic thinking skills: An exploratory study in Swiss compulsory schools
Venue: arXiv cs.CY 2025
Year: 2025

The rapid digitalisation of contemporary society has profoundly impacted various facets of our lives, including healthcare, communication, business, and education. The ability to engage with new technologies and solve problems has become crucial, making CT skills, such as pattern recognition, decomposition, and algorithm design, essential competencies. In response, Switzerland is conducting research and initiatives to integrate CT into its educational system. This study aims to develop a comprehensive framework for large-scale assessment of CT skills, particularly focusing on AT, the ability to design algorithms. To achieve this, we first developed a competence model capturing the situated and developmental nature of CT, guiding the design of activities tailored to cognitive abilities, age, and context. This framework clarifies how activity characteristics influence CT development and how to assess these competencies. Additionally, we developed an activity for large-scale assessment of AT skills, offered in two variants: one based on non-digital artefacts (unplugged) and manual expert assessment, and the other based on digital artefacts (virtual) and automatic assessment. To provide a more comprehensive evaluation of students' competencies, we developed an IAS based on BNs with noisy gates, which offers real-time probabilistic assessment for each skill rather than a single overall score. The results indicate that the proposed instrument can measure AT competencies across different age groups and educational contexts in Switzerland, demonstrating its applicability for large-scale use. AT competencies exhibit a progressive development, with no overall gender differences, though variations are observed at the school level, significantly influenced by the artefact-based environment and its context, underscoring the importance of creating accessible and adaptable assessment tools.
Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation
Venue: arXiv cs.CR 2025
Year: 2025

This paper explores the risk that a large language model (LLM) trained for code generation on data mined from software repositories will generate content that discloses sensitive information included in its training data. We decompose this risk, known in the literature as ``unintended memorization,'' into two components: unintentional disclosure (where an LLM presents secrets to users without the user seeking them out) and malicious disclosure (where an LLM presents secrets to an attacker equipped with partial knowledge of the training data). We observe that while existing work mostly anticipates malicious disclosure, unintentional disclosure is also a concern. We describe methods to assess unintentional and malicious disclosure risks side-by-side across different releases of training datasets and models. We demonstrate these methods through an independent assessment of the Open Language Model (OLMo) family of models and its Dolma training datasets. Our results show, first, that changes in data source and processing are associated with substantial changes in unintended memorization risk; second, that the same set of operational changes may increase one risk while mitigating another; and, third, that the risk of disclosing sensitive information varies not only by prompt strategies or test datasets but also by the types of sensitive information. These contributions rely on data mining to enable greater privacy and security testing required for the LLM training data supply chain.