OpenRaiser/Intern-Atlas huggingface dataset:
GradES: Significantly Faster Training in Transformers with Gradient-Based Early Stopping
Venue: arXiv cs.LG 2025
Year: 2025

Early stopping monitors global validation loss and halts all parameter updates simultaneously, which is computationally costly for large transformers due to the extended time required for validation inference. We propose \textit{GradES}, a novel gradient-based early stopping approach that operates within transformer components (attention projections and Feed-Forward layer matrices). We found that different components converge at varying rates during fine-tuning for both language and vision-language models. \textit{GradES} tracks the magnitude of gradient changes in backpropagation for these matrices during training. When a projection matrix's magnitude of gradient changes fall below a convergence threshold $τ$, we exclude that projection matrix from further updates individually, eliminating costly validation passes while allowing slow converging matrices to continue learning. \textit{GradES} speeds up training time by 1.57--7.22$\times$ while simultaneously enhancing generalization through early prevention of overfitting, resulting in 1.2\% higher average accuracy in language tasks and 3.88\% on multimodal benchmarks.
Complexity of the Existence of Constrained Secure Equilibria in Multi-Player Games
Venue: arXiv cs.GT 2025
Year: 2025

We consider a multi-player non-zero-sum turn-based game (abbreviated as multi-player game) on a finite directed graph. A secure equilibrium (SE) is a strategy profile in which no player has the incentive to deviate from the strategy because no player can increase her own payoff or lower the payoff of another player. SE is a promising refinement of Nash equilibrium in which a player does not care the payoff of another player. In this paper, we discuss the decidability and complexity of the problem of deciding whether a secure equilibrium with constraints (a payoff profile specifying which players must win) exists for a given multi-player game.
FCT O-RAN: Design and Deployment of a Multi-Vendor End-to-End Private 5G Testbed
Venue: arXiv cs.NI 2025
Year: 2025

The transformation of 5G networks into software-defined, agile, intelligent and programmable architectures necessitates a paradigm shift in deployment strategies. To deliver superior performance and surpass traditional systems, public and private 5G networks must adopt software-centric cloud native frameworks that enable flexibility through tailored configurations and optimized deployment approaches. In Singapore, the Infocomm Media Development Authority (IMDA) and the National Research Foundation Singapore (NRF) launched the Future Communications Research and Development Programme (FCP) to advance the nation's communications and connectivity landscape. At the core of this initiative is the Future Communications Translation Lab (FCT) at the Singapore Institute of Technology (SIT), which focuses on advancing 5G technologies to higher readiness levels, facilitating their adoption across various industries. A key component is the deployment of FCT O-RAN, a state-of-the-art multi-vendor private 5G platform. The setup includes a 5G core network powered by Microsoft Affirmed and ENEA, O-RAN Centralized and Distributed Units from Radisys. Indoor Remote Units are deployed with Foxconn, while outdoor RUs are deployed with Benetel. To optimize the deployment of remote units, a digital twin was created using Wireless InSite, and performance evaluations were conducted for both the digital twin and the private 5G deployment. Smartphones equipped with QualiPoc were used to measure network performance. The testbed demonstrated effective performance with optimized bandwidth allocations for both indoor and outdoor environments. In the indoor setup, utilizing 50 MHz of bandwidth, a downlink throughput of 713 Mbps and an uplink throughput of 66 Mbps were achieved. Meanwhile, the outdoor setup, utilizing 40 MHz of bandwidth, achieved a downlink throughput of 371 Mbps and an uplink throughput of 55 Mbps.
On the Analysis of Random Linear Streaming Codes in Stochastic Channels
Venue: arXiv cs.IT 2025
Year: 2025

Random Linear Streaming Codes (RLSCs) can dramatically reduce the queuing delay of block codes in real-time services. In this paper, we aim to explore the fundamental limit of large-field-size RLSCs in stochastic symbol erasure channels (SEC). The Non-systematic RLSCs (NRLSCs) in i.i.d. SEC has been analyzed in [Pinwen Su et al. 2022]. In this work, we first derive the closed-form expression on the exact error probability of NRLSCs in Gilbert-Elliott symbol erasure channels (G-ESEC). Compared to i.i.d SEC, the erasure probability of G-ESEC depends on channel state, thus transitions between the states should be considered. To deal with the stochastic state transitions, we introduce two novel techniques. (i) To account for the impact of switching states on probability terms, we find and leverage the recursive structure of the state transition traces. (ii) To obtain the expected number of error timeslots, we derive the stationary initial distribution of the states, and formulate iterative equation to characterize the expectation terms. Then we analyze the Systematic RLSCs (SRLSCs) in a special SEC, i.e., the packet erasure channel (PEC). In this scenario, SRLSCs could save some source symbols which should have exceeded the decoding delay in NRLSCs, and thus could significantly reduce the error probability. To this point, our contributions are two-folds. (i) Through a case study, we find a counter-intuitive phenomenon that SRLSCs can cause unexpected error events comparing to NRLSCs in some erasure patterns. Then we fully characterize the error event of SRLSCs for any erasure pattern. (ii) For i.i.d. PEC, we derive an analytical expression on exact error probability of SRLSCs when length of memory approaches infinity and coding rate equals to 1/2. Simulations are conducted to verify the accuracy of our analysis and compare the performance of NRLSCs, SRLSCs, and existing streaming codes.
A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation
Venue: arXiv cs.CV 2025
Year: 2025

We propose DiTTO, a novel diffusion-based framework for generating realistic, precisely configurable, and diverse multi-device storage traces. Leveraging advanced diffusion techniques, DiTTO enables the synthesis of high-fidelity continuous traces that capture temporal dynamics and inter-device dependencies with user-defined configurations. Our experimental results demonstrate that DiTTO can generate traces with high fidelity and diversity while aligning closely with guided configurations with only 8% errors.
Entry Barriers in Content Markets
Venue: arXiv cs.GT 2025
Year: 2025

The prevalence of low-quality content on online platforms is often attributed to the absence of meaningful entry requirements. This motivates us to investigate whether implicit or explicit entry barriers, alongside appropriate reward mechanisms, can enhance content quality. We present the first game-theoretic analysis of two distinct types of entry barriers in online content platforms. The first, a structural barrier, emerges from the collective behaviour of incumbent content providers which disadvantages new entrants. We show that both rank-order and proportional-share reward mechanisms induce such a structural barrier at Nash equilibrium. The second, a strategic barrier, involves the platform proactively imposing entry fees to discourage participation from low-quality contributors. We consider a scheme in which the platform redirects some or all of the entry fees into the reward pool. We formally demonstrate that this approach can improve overall content quality. Our findings establish a theoretical foundation for designing reward mechanisms coupled with entry fees to promote higher-quality content and support healthier online ecosystems.
2D Gaussian Splatting with Semantic Alignment for Image Inpainting
Venue: arXiv cs.CV 2025
Year: 2025

Gaussian Splatting (GS), a recent technique for converting discrete points into continuous spatial representations, has shown promising results in 3D scene modeling and 2D image super-resolution. In this paper, we explore its untapped potential for image inpainting, which demands both locally coherent pixel synthesis and globally consistent semantic restoration. We propose the first image inpainting framework based on 2D Gaussian Splatting, which encodes incomplete images into a continuous field of 2D Gaussian splat coefficients and reconstructs the final image via a differentiable rasterization process. The continuous rendering paradigm of GS inherently promotes pixel-level coherence in the inpainted results. To improve efficiency and scalability, we introduce a patch-wise rasterization strategy that reduces memory overhead and accelerates inference. For global semantic consistency, we incorporate features from a pretrained DINO model. We observe that DINO's global features are naturally robust to small missing regions and can be effectively adapted to guide semantic alignment in large-mask scenarios, ensuring that the inpainted content remains contextually consistent with the surrounding scene. Extensive experiments on standard benchmarks demonstrate that our method achieves competitive performance in both quantitative metrics and perceptual quality, establishing a new direction for applying Gaussian Splatting to 2D image processing.
MIRAGE: Multimodal Intention Recognition and Admittance-Guided Enhancement in VR-based Multi-object Teleoperation
Venue: arXiv cs.RO 2025
Year: 2025

Effective human-robot interaction (HRI) in multi-object teleoperation tasks faces significant challenges due to perceptual ambiguities in virtual reality (VR) environments and the limitations of single-modality intention recognition. This paper proposes a shared control framework that combines a virtual admittance (VA) model with a Multimodal-CNN-based Human Intention Perception Network (MMIPN) to enhance teleoperation performance and user experience. The VA model employs artificial potential fields to guide operators toward target objects by adjusting admittance force and optimizing motion trajectories. MMIPN processes multimodal inputs, including gaze movement, robot motions, and environmental context, to estimate human grasping intentions, helping to overcome depth perception challenges in VR. Our user study evaluated four conditions across two factors, and the results showed that MMIPN significantly improved grasp success rates, while the VA model enhanced movement efficiency by reducing path lengths. Gaze data emerged as the most crucial input modality. These findings demonstrate the effectiveness of combining multimodal cues with implicit guidance in VR-based teleoperation, providing a robust solution for multi-object grasping tasks and enabling more natural interactions across various applications in the future.
Bouncy particle sampler with infinite exchanging parallel tempering
Venue: arXiv cs.LG 2025
Year: 2025

Bayesian inference is useful to obtain a predictive distribution with a small generalization error. However, since posterior distributions are rarely evaluated analytically, we employ the variational Bayesian inference or sampling method to approximate posterior distributions. When we obtain samples from a posterior distribution, Hamiltonian Monte Carlo (HMC) has been widely used for the continuous variable part and Markov chain Monte Carlo (MCMC) for the discrete variable part. Another sampling method, the bouncy particle sampler (BPS), has been proposed, which combines uniform linear motion and stochastic reflection to perform sampling. BPS was reported to have the advantage of being easier to set simulation parameters than HMC. To accelerate the convergence to a posterior distribution, we introduced parallel tempering (PT) to BPS, and then proposed an algorithm when the inverse temperature exchange rate is set to infinity. We performed numerical simulations and demonstrated its effectiveness for multimodal distribution.
Generalizing Unsupervised Lidar Odometry Model from Normal to Snowy Weather Conditions
Venue: arXiv cs.RO 2025
Year: 2025

Deep learning-based LiDAR odometry is crucial for autonomous driving and robotic navigation, yet its performance under adverse weather, especially snowfall, remains challenging. Existing models struggle to generalize across conditions due to sensitivity to snow-induced noise, limiting real-world use. In this work, we present an unsupervised LiDAR odometry model to close the gap between clear and snowy weather conditions. Our approach focuses on effective denoising to mitigate the impact of snowflake noise and outlier points on pose estimation, while also maintaining computational efficiency for real-time applications. To achieve this, we introduce a Patch Spatial Measure (PSM) module that evaluates the dispersion of points within each patch, enabling effective detection of sparse and discrete noise. We further propose a Patch Point Weight Predictor (PPWP) to assign adaptive point-wise weights, enhancing their discriminative capacity within local regions. To support real-time performance, we first apply an intensity threshold mask to quickly suppress dense snowflake clusters near the LiDAR, and then perform multi-modal feature fusion to refine the point-wise weight prediction, improving overall robustness under adverse weather. Our model is trained in clear weather conditions and rigorously tested across various scenarios, including snowy and dynamic. Extensive experimental results confirm the effectiveness of our method, demonstrating robust performance in both clear and snowy weather. This advancement enhances the model's generalizability and paves the way for more reliable autonomous systems capable of operating across a wider range of environmental conditions.
Vision-Based Embedded System for Noncontact Monitoring of Preterm Infant Behavior in Low-Resource Care Settings
Venue: arXiv cs.CV 2025
Year: 2025

Preterm birth remains a leading cause of neonatal mortality, disproportionately affecting low-resource settings with limited access to advanced neonatal intensive care units (NICUs).Continuous monitoring of infant behavior, such as sleep/awake states and crying episodes, is critical but relies on manual observation or invasive sensors, which are prone to error, impractical, and can cause skin damage. This paper presents a novel, noninvasive, and automated vision-based framework to address this gap. We introduce an embedded monitoring system that utilizes a quantized MobileNet model deployed on a Raspberry Pi for real-time behavioral state detection. When trained and evaluated on public neonatal image datasets, our system achieves state-of-the-art accuracy (91.8% for sleep detection and 97.7% for crying/normal classification) while maintaining computational efficiency suitable for edge deployment. Through comparative benchmarking, we provide a critical analysis of the trade-offs between model size, inference latency, and diagnostic accuracy. Our findings demonstrate that while larger architectures (e.g., ResNet152, VGG19) offer marginal gains in accuracy, their computational cost is prohibitive for real-time edge use. The proposed framework integrates three key innovations: model quantization for memory-efficient inference (68% reduction in size), Raspberry Pi-optimized vision pipelines, and secure IoT communication for clinical alerts. This work conclusively shows that lightweight, optimized models such as the MobileNet offer the most viable foundation for scalable, low-cost, and clinically actionable NICU monitoring systems, paving the way for improved preterm care in resource-constrained environments.
Genetic Programming with Model Driven Dimension Repair for Learning Interpretable Appointment Scheduling Rules
Venue: arXiv cs.LG 2025
Year: 2025

Appointment scheduling is a great challenge in healthcare operations management. Appointment rules (AR) provide medical practitioners with a simple yet effective tool to determine patient appointment times. Genetic programming (GP) can be used to evolve ARs. However, directly applying GP to design ARs may lead to rules that are difficult for end-users to interpret and trust. A key reason is that GP is unaware of the dimensional consistency, which ensures that the evolved rules align with users' domain knowledge and intuitive understanding. In this paper, we develop a new dimensionally aware GP algorithm with dimension repair to evolve ARs with dimensional consistency and high performance. A key innovation of our method is the dimension repair procedure, which optimizes the dimensional consistency of an expression tree while minimizing structural changes and ensuring that its output dimension meets the problem's requirements. We formulate the task as a mixed-integer linear programming model that can be efficiently solved using common mathematical programming methods. With the support of the dimension repair procedure, our method can explore a wider range of AR structures by temporarily breaking the dimensional consistency of individuals, and then restoring it without altering their overall structure, thereby identifying individuals with greater potential advantages. We evaluated the proposed method in a comprehensive set of simulated clinics. The experimental results demonstrate that our approach managed to evolve high-quality ARs that significantly outperform not only the manually designed ARs but also existing state-of-the-art dimensionally aware GP methods in terms of both objective values and dimensional consistency. In addition, we analyzed the semantics of the evolved ARs, providing insight into the design of more effective and interpretable ARs.
How Instruction-Tuning Imparts Length Control: A Cross-Lingual Mechanistic Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Adhering to explicit length constraints, such as generating text with a precise word count, remains a significant challenge for Large Language Models (LLMs). This study aims at investigating the differences between foundation models and their instruction-tuned counterparts, on length-controlled text generation in English and Italian. We analyze both performance and internal component contributions using Cumulative Weighted Attribution, a metric derived from Direct Logit Attribution. Our findings reveal that instruction-tuning substantially improves length control, primarily by specializing components in deeper model layers. Specifically, attention heads in later layers of IT models show increasingly positive contributions, particularly in English. In Italian, while attention contributions are more attenuated, final-layer MLPs exhibit a stronger positive role, suggesting a compensatory mechanism. These results indicate that instruction-tuning reconfigures later layers for task adherence, with component-level strategies potentially adapting to linguistic context.
GeoLayer: Towards Low-Latency and Cost-Efficient Geo-Distributed Graph Stores with Layered Graph
Venue: arXiv cs.DB 2025
Year: 2025

The inherent connectivity and dependency of graph-structured data, combined with its unique topology-driven access patterns, pose fundamental challenges to conventional data replication and request routing strategies in geo-distributed cloud storage systems. In this paper, we propose GeoLayer, a geo-distributed graph storage framework that jointly optimizes graph replica placement and pattern request routing. We first construct a latency-aware layered graph architecture that decomposes the graph topology into multiple layers, aiming to reduce the decision space and computational complexity of the optimization problem, while mitigating the impact of network heterogeneity in geo-distributed environments. Building on the layered graph, we introduce an overlap-centric replica placement scheme to accommodate the diversity of graph pattern accesses, along with a directed heat diffusion model that captures heat conduction and superposition effects to guide data allocation. For request routing, we develop a stepwise layered routing strategy that performs progressive expansion over the layered graph to efficiently retrieve the required data. Experimental results show that, compared to state-of-the-art replica placement and routing schemes, GeoLayer achieves a 1.34x - 3.67x improvement in response times for online graph pattern requests and a 1.28x - 3.56x speedup in offline graph analysis performance.
FlexNGIA 2.0: Redesigning the Internet with Agentic AI -- Protocols, Services, and Traffic Engineering Designed, Deployed, and Managed by AI
Venue: arXiv cs.NI 2025
Year: 2025

The escalating demands of immersive communications, alongside advances in network softwarization and AI-driven cognition and generative reasoning, create a pivotal opportunity to rethink and reshape the future Internet. In this context, we introduce in this paper, FlexNGIA 2.0, an Agentic AI-driven Internet architecture that leverages LLM-based AI agents to autonomously orchestrate, configure, and evolve the network. These agents can, at runtime, perceive, reason, coordinate among themselves to dynamically design, implement, deploy, and adapt communication protocols, Service Function Chains (SFCs), network functions, resource allocation strategies, congestion control, and traffic engineering schemes, thereby ensuring optimal performance, reliability, and efficiency under evolving conditions. The paper first outlines the overall architecture of FlexNGIA 2.0 and its constituent LLM-Based AI agents. For each agent, we detail its design, implementation, inputs and outputs, prompt structures, interactions with tools and other agents, followed by preliminary proof-of-concept experiments demonstrating its operation and potential. The results clearly highlight the ability of these LLM-based AI agents to automate the design, the implementation, the deployment, and the performance evaluation of transport protocols, service function chains, network functions, congestion control schemes, and resource allocation strategies. FlexNGIA 2.0 paves the way for a new class of Agentic AI-Driven networks, where fully cognitive, self-evolving AI agents can autonomously design, implement, adapt and optimize the network's protocols, algorithms, and behaviors to efficiently operate across complex, dynamic, and heterogeneous environments. To bring this vision to reality, we also identify key research challenges toward achieving fully autonomous, adaptive, and agentic AI-driven networks.
Shared Control for Game Accessibility: Understanding Current Human Cooperation Practices to Inform the Design of Partial Automation Solutions
Venue: arXiv cs.HC 2025
Year: 2025

Shared control is a form of video gaming accessibility support that allows players with disabilities to delegate inaccessible controls to another person. Through interviews involving 14 individuals with lived experience of accessible gaming in shared control, we explore the ways in which shared control technologies are adopted in practice, the accessibility challenges they address, and how the support currently provided in shared control can be automated to remove the need for a human assistant. Findings indicate that shared control is essential for enabling access to otherwise inaccessible games, but its reliance on human support is a key limitation. Participants welcomed the idea of automating the support with software agents, while also identifying limitations and design requirements. Accordingly, this work contributes insights into current practices and proposes guidelines for developing automated support systems.