OpenRaiser/Intern-Atlas huggingface dataset:
UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis. Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding. A key challenge is the inconsistency of 2D instance labels across views, leading to poor 3D predictions. Existing methods use a two-stage approach in which some rely on contrastive learning with hyperparameter-sensitive clustering, while others preprocess labels for consistency. We propose a unified framework that merges these steps, reducing training time and improving performance by introducing a learnable feature embedding for segmentation in Gaussian primitives. This embedding is then efficiently decoded into instance labels through a novel "Embedding-to-Label" process, effectively integrating the optimization. While this unified framework offers substantial benefits, we observed artifacts at the object boundaries. To address the object boundary issues, we propose hard-mining samples along these boundaries. However, directly applying hard mining to the feature embeddings proved unstable. Therefore, we apply a linear layer to the rasterized feature embeddings before calculating the triplet loss, which stabilizes training and significantly improves performance. Our method outperforms baselines qualitatively and quantitatively on the ScanNet, Replica3D, and Messy-Rooms datasets.
DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes
Venue: arXiv cs.LG 2025
Year: 2025

Precise probabilistic information about drug-target interaction (DTI) predictions is vital for understanding limitations and boosting predictive performance. Gaussian processes (GP) offer a scalable framework to integrate state-of-the-art DTI representations and Bayesian inference, enabling novel operations, such as Bayesian classification with rejection, top-$K$ selection, and ranking. We propose a deep kernel learning-based GP architecture (DTI-GP), which incorporates a combined neural embedding module for chemical compounds and protein targets, and a GP module. The workflow continues with sampling from the predictive distribution to estimate a Bayesian precedence matrix, which is used in fast and accurate selection and ranking operations. DTI-GP outperforms state-of-the-art solutions, and it allows (1) the construction of a Bayesian accuracy-confidence enrichment score, (2) rejection schemes for improved enrichment, and (3) estimation and search for top-$K$ selections and ranking with high expected utility.
LMG Index: A Robust Learned Index for Multi-Dimensional Performance Balance
Venue: arXiv cs.DB 2025
Year: 2025

Index structures are fundamental for efficient query processing on large-scale datasets. Learned indexes model the indexing process as a prediction problem to overcome the inherent trade-offs of traditional indexes. However, most existing learned indexes optimize only for limited objectives like query latency or space usage, neglecting other practical evaluation dimensions such as update efficiency and stability. Moreover, many learned indexes rely on assumptions about data distributions or workloads, lacking theoretical guarantees when facing unknown or evolving scenarios, which limits their generality in real-world systems. In this paper, we propose LMIndex, a robust framework for learned indexing that leverages a efficient query/update top-layer structure (theoretically $O(1)$ when the key type is fixed) and a efficient optimal error threshold training algorithm (approach $O(1)$ in practice). Building upon this, we develop LMG (LMIndex with gaps), a variant employing a novel gap allocation strategy to enhance update performance and maintain stability under dynamic workloads. Extensive evaluations show that LMG achieves competitive or leading performance, including bulk loading (up to 8.25$\times$ faster), point queries (up to 1.49$\times$ faster), range queries (up to 4.02$\times$ faster than B+Tree), update (up to 1.5$\times$ faster on read-write workloads), stability (up to 82.59$\times$ lower coefficient of variation), and space usage (up to 1.38$\times$ smaller). These results demonstrate that LMG effectively breaks the multi-dimensional performance trade-offs inherent in state-of-the-art approaches, offering a balanced and versatile framework.
ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

3D scene graphs have empowered robots with semantic understanding for navigation and planning, yet they often lack the functional information required for physical manipulation, particularly regarding articulated objects. Existing approaches for inferring articulation mechanisms from static observations are prone to visual ambiguity, while methods that estimate parameters from state changes typically rely on constrained settings such as fixed cameras and unobstructed views. Furthermore, fine-grained functional elements like small handles are frequently missed by general object detectors. To bridge this gap, we present ArtiSG, a framework that constructs functional 3D scene graphs by encoding human demonstrations into structured robotic memory. Our approach leverages a robust articulation data collection pipeline utilizing a portable setup to accurately estimate 6-DoF articulation trajectories and axes even under camera ego-motion. We integrate these kinematic priors into a hierarchical and open-vocabulary graph while utilizing interaction data to discover inconspicuous functional elements missed by visual perception. Extensive real-world experiments demonstrate that ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision. Moreover, we show that the constructed graph serves as a reliable functional memory that effectively guides robots to perform language-directed manipulation tasks in real-world environments containing diverse articulated objects.
PRISM: A hierarchical multiscale approach for time series forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Forecasting is critical in areas such as finance, biology, and healthcare. Despite the progress in the field, making accurate forecasts remains challenging because real-world time series contain both global trends, local fine-grained structure, and features on multiple scales in between. Here, we present a new forecasting method, PRISM (Partitioned Representation for Iterative Sequence Modeling), that addresses this challenge through a learnable tree-based partitioning of the signal. At the root of the tree, a global representation captures coarse trends in the signal, while recursive splits reveal increasingly localized views of the signal. At each level of the tree, data are projected onto a time-frequency basis (e.g., wavelets or exponential moving averages) to extract scale-specific features, which are then aggregated across the hierarchy. This design allows the model to jointly capture global structure and local dynamics of the signal, enabling accurate forecasting. Experiments across benchmark datasets show that our method outperforms state-of-the-art methods for forecasting. Overall, these results demonstrate that our hierarchical approach provides a lightweight and flexible framework for forecasting multivariate time series. The code is available at https://github.com/nerdslab/prism.
AI-Driven Cloud Resource Optimization for Multi-Cluster Environments
Venue: arXiv cs.DC 2025
Year: 2025

Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dynamic workloads. These limitations result in inefficient resource utilization, delayed adaptation, and increased operational overhead across distributed environments. This paper presents an AI-driven framework for adaptive resource optimization in multi-cluster cloud systems. The proposed approach integrates predictive learning, policy-aware decision-making, and continuous feedback to enable proactive and coordinated resource management across clusters. By analyzing cross-cluster telemetry and historical execution patterns, the framework dynamically adjusts resource allocation to balance performance, cost, and reliability objectives. A prototype implementation demonstrates improved resource efficiency, faster stabilization during workload fluctuations, and reduced performance variability compared to conventional reactive approaches. The results highlight the effectiveness of intelligent, self-adaptive infrastructure management as a key enabler for scalable and resilient cloud platforms.
Fair Committee Selection under Ordinal Preferences and Limited Cardinal Information
Venue: arXiv cs.DS 2025
Year: 2025

We study the problem of fair $k$-committee selection under an egalitarian objective. Given $n$ agents partitioned into $m$ groups (\eg, demographic quotas), the goal is to aggregate their preferences to form a committee of size $k$ that guarantees minimum representation from each group while minimizing the maximum \emph{cost} incurred by any agent. We model this setting as the ordinal fair $k$-center problem, where agents are embedded in an unknown metric space, and each agent reports a complete preference ranking (i.e., ordinal information) over all agents, consistent with the underlying distance metric (i.e., cardinal information). The cost incurred by an agent with respect to a committee is defined as its distance to the closest committee member. The quality of an algorithm is evaluated using the notion of distortion, which measures the worst-case ratio between the cost of the committee produced by the algorithm and the cost of an optimal committee, when given complete access to the underlying metric space. When cardinal information is not available, no constant distortion is possible for the ordinal $k$-center problem, even without fairness constraints, when $k\geq 3$ [Burkhardt et.al., AAAI'24]. To overcome this hardness, we allow limited access to cardinal information by querying the metric space. In this setting, our main contribution is a factor-$5$ distortion algorithm that requires only $O(k \log^2 k)$ queries. Along the way, we present an improved factor-$3$ distortion algorithm using $O(k^2)$ queries.
Securing High-Concurrency Ticket Sales: A Framework Based on Microservice
Venue: arXiv cs.SE 2025
Year: 2025

The railway ticketing system is one of the most important public service infrastructure. In peak periods such as holidays, it is often faced with the challenge of high concurrency scenarios because of a large number of users accessing at the same time. The traditional aggregation architecture can not meet the peak user requirements because of its insufficient fault tolerance and low ability. Therefore, the system needs to use microservice architecture for development, and add multiple security methods to ensure that the system can have good stability and data consistency under high concurrency scenarios, and can respond quickly to user requests. This paper introduces the use of B/S architecture and Spring Cloud to design and develop a railway ticket purchase system that can maintain stability and reliability under high concurrency scenarios, and formulate multiple security design methods for the system. This system integrates a range of functions, such as real-time train inquiries, dynamic seat updates, online seat selection, and ticket purchasing, effectively addressing common problems associated with offline ticket purchasing, such as long queues and delayed information. It enables a complete online process from inquiry and booking to payment and refunds. Furthermore, the "add passenger" function allows users to purchase tickets for others, extending the convenience of online ticketing to people with limited internet access. The system design prioritizes security and stability, while also focusing on high performance, and achieves these goals through a carefully designed architecture and the integration of multiple middleware components. After the completion of the system development, the core interface of the system is tested, and then the results are analyzed. The test data proves that the system has good ability and stability under high concurrency.
ProDM: Synthetic Reality-driven Property-aware Progressive Diffusion Model for Coronary Calcium Motion Correction in Non-gated Chest CT
Venue: arXiv cs.CV 2025
Year: 2025

Coronary artery calcium (CAC) scoring from chest CT is a well-established tool to stratify and refine clinical cardiovascular disease risk estimation. CAC quantification relies on the accurate delineation of calcified lesions, but is oftentimes affected by artifacts introduced by cardiac and respiratory motion. ECG-gated cardiac CTs substantially reduce motion artifacts, but their use in population screening and routine imaging remains limited due to gating requirements and lack of insurance coverage. Although identification of incidental CAC from non-gated chest CT is increasingly considered for it offers an accessible and widely available alternative, this modality is limited by more severe motion artifacts. We present ProDM (Property-aware Progressive Correction Diffusion Model), a generative diffusion framework that restores motion-free calcified lesions from non-gated CTs. ProDM introduces three key components: (1) a CAC motion simulation data engine that synthesizes realistic non-gated acquisitions with diverse motion trajectories directly from cardiac-gated CTs, enabling supervised training without paired data; (2) a property-aware learning strategy incorporating calcium-specific priors through a differentiable calcium consistency loss to preserve lesion integrity; and (3) a progressive correction scheme that reduces artifacts gradually across diffusion steps to enhance stability and calcium fidelity. Experiments on real patient datasets show that ProDM significantly improves CAC scoring accuracy, spatial lesion fidelity, and risk stratification performance compared with several baselines. A reader study on real non-gated scans further confirms that ProDM suppresses motion artifacts and improves clinical usability. These findings highlight the potential of progressive, property-aware frameworks for reliable CAC quantification from routine chest CT imaging.
Attribution-Guided Distillation of Matryoshka Sparse Autoencoders
Venue: arXiv cs.LG 2025
Year: 2025

Sparse autoencoders (SAEs) aim to disentangle model activations into monosemantic, human-interpretable features. In practice, learned features are often redundant and vary across training runs and sparsity levels, which makes interpretations difficult to transfer and reuse. We introduce Distilled Matryoshka Sparse Autoencoders (DMSAEs), a training pipeline that distills a compact core of consistently useful features and reuses it to train new SAEs. DMSAEs run an iterative distillation cycle: train a Matryoshka SAE with a shared core, use gradient X activation to measure each feature's contribution to next-token loss in the most nested reconstruction, and keep only the smallest subset that explains a fixed fraction of the attribution. Only the core encoder weight vectors are transferred across cycles; the core decoder and all non-core latents are reinitialized each time. On Gemma-2-2B layer 12 residual stream activations, seven cycles of distillation (500M tokens, 65k width) yielded a distilled core of 197 features that were repeatedly selected. Training using this distilled core improves several SAEBench metrics and demonstrates that consistent sets of latent features can be transferred across sparsity levels
Efficiently Estimating Data Efficiency for Language Model Fine-tuning
Venue: arXiv cs.LG 2025
Year: 2025

While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However, a task's data efficiency--i.e., the number of fine-tuning examples needed to achieve a desired level of performance--is often unknown, resulting in costly cycles of incremental annotation and retraining. Indeed, we demonstrate across a curated set of 30 specialized tasks that performant LLMs may struggle zero-shot but can attain stronger performance after fine-tuning. This motivates the need for methods to predict a task's data efficiency without requiring incremental annotation. After introducing a concrete metric that quantifies a task's data efficiency, we propose using the gradient cosine similarity of low-confidence examples to predict data efficiency based on a small number of labeled samples. We validate our approach on a diverse set of tasks with varying data efficiencies, attaining 8.6% error in overall data efficiency prediction and typically eliminating hundreds of unnecessary annotations on each task. Our experiment results and implementation code are available on GitHub.
Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search
Venue: arXiv cs.OS 2025
Year: 2025

Resource-management tasks in modern operating and distributed systems continue to rely primarily on hand-designed heuristics for tasks such as scheduling, caching, or active queue management. Designing performant heuristics is an expensive, time-consuming process that we are forced to continuously go through due to the constant flux of hardware, workloads and environments. We propose a new alternative: synthesizing instance-optimal heuristics -- specialized for the exact workloads and hardware where they will be deployed -- using code-generating large language models (LLMs). To make this synthesis tractable, Vulcan separates policy and mechanism through LLM-friendly, task-agnostic interfaces. With these interfaces, users specify the inputs and objectives of their desired policy, while Vulcan searches for performant policies via evolutionary search over LLM-generated code. This interface is expressive enough to capture a wide range of system policies, yet sufficiently constrained to allow even small, inexpensive LLMs to generate correct and executable code. We use Vulcan to synthesize performant heuristics for cache eviction and memory tiering, and find that these heuristics outperform all human-designed state-of-the-art algorithms by upto 69% and 7.9% in performance for each of these tasks respectively.
From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing
Venue: arXiv cs.CV 2025
Year: 2025

Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ while all other visual conditions are identical. Existing methods circumvent this with a mask-based inpainting paradigm, where an incomplete visual conditioning forces models to simultaneously hallucinate missing content and sync lips, leading to visual artifacts, identity drift, and poor synchronization. In this work, we propose a novel self-bootstrapping framework that reframes visual dubbing from an ill-posed inpainting task into a well-conditioned video-to-video editing problem. Our approach employs a Diffusion Transformer, first as a data generator, to synthesize ideal training data: a lip-altered companion video for each real sample, forming visually aligned video pairs. A DiT-based audio-driven editor is then trained on these pairs end-to-end, leveraging the complete and aligned input video frames to focus solely on precise, audio-driven lip modifications. This complete, frame-aligned input conditioning forms a rich visual context for the editor, providing it with complete identity cues, scene interactions, and continuous spatiotemporal dynamics. Leveraging this rich context fundamentally enables our method to achieve highly accurate lip sync, faithful identity preservation, and exceptional robustness against challenging in-the-wild scenarios. We further introduce a timestep-adaptive multi-phase learning strategy as a necessary component to disentangle conflicting editing objectives across diffusion timesteps, thereby facilitating stable training and yielding enhanced lip synchronization and visual fidelity. Additionally, we propose ContextDubBench, a comprehensive benchmark dataset for robust evaluation in diverse and challenging practical application scenarios.
Augmented Reality Indoor Wayfinding in Hospital Environments An Empirical Study on Navigation Efficiency, User Experience, and Cognitive Load
Venue: arXiv cs.HC 2025
Year: 2025

Hospitals are among the most cognitively demanding indoor environments, especially for patients and visitors unfamiliar with their layout. This study investigates the effectiveness of an augmented reality (AR)-based handheld navigation system compared to traditional paper maps in a large hospital setting. Through a mixed-methods experiment with 32 participants, we measured navigation performance, cognitive workload (NASA-TLX), situational anxiety (STAI-State), spatial behavior, and user satisfaction. Results show that AR users completed navigation tasks significantly faster, made fewer errors, and reported lower anxiety and workload. However, paper map users demonstrated stronger spatial memory in sketch-based recall tasks, highlighting a trade-off between real-time efficiency and long-term spatial learning. We discuss implications for inclusive AR design, spatial cognition, and healthcare accessibility, offering actionable design strategies for adaptive indoor navigation tools.
From Clay to Code: Typological and Material Reasoning in AI Interpretations of Iranian Pigeon Towers
Venue: arXiv cs.AI 2025
Year: 2025

This study investigates how generative AI systems interpret the architectural intelligence embedded in vernacular form. Using the Iranian pigeon tower as a case study, the research tests three diffusion models, Midjourney v6, DALL-E 3, and DreamStudio based on Stable Diffusion XL (SDXL), across three prompt stages: referential, adaptive, and speculative. A five-criteria evaluation framework assesses how each system reconstructs typology, materiality, environment, realism, and cultural specificity. Results show that AI reliably reproduces geometric patterns but misreads material and climatic reasoning. Reference imagery improves realism yet limits creativity, while freedom from reference generates inventive but culturally ambiguous outcomes. The findings define a boundary between visual resemblance and architectural reasoning, positioning computational vernacular reasoning as a framework for analyzing how AI perceives, distorts, and reimagines traditional design intelligence.