OpenRaiser/Intern-Atlas huggingface dataset:
TextEditBench: Evaluating Reasoning-aware Text Editing Beyond Rendering
Venue: arXiv cs.CV 2025
Year: 2025

Text rendering has recently emerged as one of the most challenging frontiers in visual generation, drawing significant attention from large-scale diffusion and multimodal models. However, text editing within images remains largely unexplored, as it requires generating legible characters while preserving semantic, geometric, and contextual coherence. To fill this gap, we introduce TextEditBench, a comprehensive evaluation benchmark that explicitly focuses on text-centric regions in images. Beyond basic pixel manipulations, our benchmark emphasizes reasoning-intensive editing scenarios that require models to understand physical plausibility, linguistic meaning, and cross-modal dependencies. We further propose a novel evaluation dimension, Semantic Expectation (SE), which measures reasoning ability of model to maintain semantic consistency, contextual coherence, and cross-modal alignment during text editing. Extensive experiments on state-of-the-art editing systems reveal that while current models can follow simple textual instructions, they still struggle with context-dependent reasoning, physical consistency, and layout-aware integration. By focusing evaluation on this long-overlooked yet fundamental capability, TextEditBench establishes a new testing ground for advancing text-guided image editing and reasoning in multimodal generation.
Sharpness-aware Second-order Latent Factor Model for High-dimensional and Incomplete Data
Venue: arXiv cs.LG 2025
Year: 2025

Second-order Latent Factor (SLF) model, a class of low-rank representation learning methods, has proven effective at extracting node-to-node interaction patterns from High-dimensional and Incomplete (HDI) data. However, its optimization is notoriously difficult due to its bilinear and non-convex nature. Sharpness-aware Minimization (SAM) has recently proposed to find flat local minima when minimizing non-convex objectives, thereby improving the generalization of representation-learning models. To address this challenge, we propose a Sharpness-aware SLF (SSLF) model. SSLF embodies two key ideas: (1) acquiring second-order information via Hessian-vector products; and (2) injecting a sharpness term into the curvature (Hessian) through the designed Hessian-vector products. Experiments on multiple industrial datasets demonstrate that the proposed model consistently outperforms state-of-the-art baselines.
CogSR: Semantic-Aware Speech Super-Resolution via Chain-of-Thought Guided Flow Matching
Venue: arXiv cs.SD 2025
Year: 2025

Applying speech super-resolution (SR) to recordings with severely low sampling rates is a critical challenge in digital archiving and investigative audio recovery. In these scenarios, the input lacks essential acoustic cues. Consequently, existing generative models often fail; without sufficient context, they hallucinate phonetic content, guessing words based on probability rather than meaning. To address this, we propose CogSR, a framework designed specifically for high-precision, offline restoration. Our approach shifts the focus from simple signal mapping to cognitive reconstruction. By integrating a Large Audio-Language Model, we employ Chain-of-Thought reasoning to act as a semantic anchor, while explicit acoustic priors ensure the speaker's identity remains consistent. This guides a Rectified Flow backbone to synthesize high-frequency details that are not only realistic but linguistically accurate. Evaluations show that CogSR effectively eliminates ambiguity in severe degradation regimes, making it a robust solution for restoring high-value legacy and surveillance audio.
LaverNet: Lightweight All-in-one Video Restoration via Selective Propagation
Venue: arXiv cs.CV 2025
Year: 2025

Recent studies have explored all-in-one video restoration, which handles multiple degradations with a unified model. However, these approaches still face two challenges when dealing with time-varying degradations. First, the degradation can dominate temporal modeling, confusing the model to focus on artifacts rather than the video content. Second, current methods typically rely on large models to handle all-in-one restoration, concealing those underlying difficulties. To address these challenges, we propose a lightweight all-in-one video restoration network, LaverNet, with only 362K parameters. To mitigate the impact of degradations on temporal modeling, we introduce a novel propagation mechanism that selectively transmits only degradation-agnostic features across frames. Through LaverNet, we demonstrate that strong all-in-one restoration can be achieved with a compact network. Despite its small size, less than 1\% of the parameters of existing models, LaverNet achieves comparable, even superior performance across benchmarks.
QUIDS: Quality-informed Incentive-driven Multi-agent Dispatching System for Mobile Crowdsensing
Venue: arXiv cs.CV 2025
Year: 2025

This paper addresses the challenge of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key obstacles are the interrelated issues of sensing coverage, sensing reliability, and the dynamic participation of vehicles. To tackle these, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and reliability under budget constraints. QUIDS introduces a novel metric, Aggregated Sensing Quality (ASQ), to quantitatively capture QoI by integrating both coverage and reliability. We also develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates incentives under uncertainty, further improving ASQ. Evaluation using real-world data from a metropolitan NVMCS deployment shows QUIDS improves ASQ by 38% over non-dispatching scenarios and by 10% over state-of-the-art methods. It also reduces reconstruction map errors by 39-74% across algorithms. By jointly optimizing coverage and reliability via a quality-informed incentive mechanism, QUIDS enables low-cost, high-quality urban monitoring without dedicated infrastructure, applicable to smart-city scenarios like traffic and environmental sensing.
Beyond openness: Inclusiveness and usability of Chinese scholarly data in OpenAlex
Venue: arXiv cs.DL 2025
Year: 2025

OpenAlex, launched in 2022 as a fully open scholarly data source, promises greater inclusiveness compared to traditional proprietary databases. This study evaluates whether OpenAlex delivers on that promise by examining its coverage and metadata quality for Chinese-language journals and their articles. Using the 2023 edition of A Guide to the Core Journals of China (GCJC) and Wanfang Data as a benchmark, we analyze three aspects: (1) journal-level coverage, (2) article-level coverage, and (3) completeness and accuracy of metadata fields. Results show that OpenAlex indexes only 37% of GCJC journals and 24% of their articles, with substantial disciplinary and temporal variation. Metadata quality is uneven: while basic fields such as title and publication year are complete, bibliographic details, author affiliations, and cited references are frequently missing or inaccurate. DOI coverage is limited, and language information is often incorrect, with most Chinese-language articles labeled as English. These findings highlight significant challenges for achieving full inclusiveness and usability in research evaluation and related activities. We conclude with recommendations for improving data aggregation strategies, DOI registration practices, and metadata standardization to enhance the integration of local scholarly outputs into global open infrastructures.
AI Needs Physics More Than Physics Needs AI
Venue: arXiv cs.AI 2025
Year: 2025

Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures - large language models, reasoning models, and agentic AI - can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of 'Big AI': a synthesis of theory-based rigour with the flexibility of machine learning.
Multivariate Uncertainty Quantification with Tomographic Quantile Forests
Venue: arXiv cs.LG 2025
Year: 2025

Quantifying predictive uncertainty is essential for safe and trustworthy real-world AI deployment. Yet, fully nonparametric estimation of conditional distributions remains challenging for multivariate targets. We propose Tomographic Quantile Forests (TQF), a nonparametric, uncertainty-aware, tree-based regression model for multivariate targets. TQF learns conditional quantiles of directional projections $\mathbf{n}^{\top}\mathbf{y}$ as functions of the input $\mathbf{x}$ and the unit direction $\mathbf{n}$. At inference, it aggregates quantiles across many directions and reconstructs the multivariate conditional distribution by minimizing the sliced Wasserstein distance via an efficient alternating scheme with convex subproblems. Unlike classical directional-quantile approaches that typically produce only convex quantile regions and require training separate models for different directions, TQF covers all directions with a single model without imposing convexity restrictions. We evaluate TQF on synthetic and real-world datasets, and release the source code on GitHub.
PCIA: A Path Construction Imitation Algorithm for Global Optimization
Venue: arXiv cs.AI 2025
Year: 2025

In this paper, a new metaheuristic optimization algorithm, called Path Construction Imitation Algorithm (PCIA), is proposed. PCIA is inspired by how humans construct new paths and use them. Typically, humans prefer popular transportation routes. In the event of a path closure, a new route is built by mixing the existing paths intelligently. Also, humans select different pathways on a random basis to reach unknown destinations. PCIA generates a random population to find the best route toward the destination, similar to swarm-based algorithms. Each particle represents a path toward the destination. PCIA has been tested with 53 mathematical optimization problems and 13 constrained optimization problems. The results showed that the PCIA is highly competitive compared to both popular and the latest metaheuristic algorithms.
SoK: Reviewing Two Decades of Security, Privacy, Accessibility, and Usability Studies on Internet of Things for Older Adults
Venue: arXiv cs.CR 2025
Year: 2025

The Internet of Things (IoT) has the potential to enhance older adults' independence and quality of life, but it also exposes them to security, privacy, accessibility, and usability (SPAU) risks. We conducted a systematic review of 44 peer-reviewed studies published between 2004 and 2024 using a five-phase screening pipeline. From each study, we extracted data on study design, IoT type, SPAU measures, and identified research gaps. We introduce the SPAU-IoT Framework, which comprises 27 criteria across four dimensions: security (e.g., resilience to cyber threats, secure authentication, encrypted communication, secure-by-default settings, and guardianship features), privacy (e.g., data minimization, explicit consent, and privacy-preserving analytics), accessibility (e.g., compliance with ADA/WCAG standards and assistive-technology compatibility), and usability (e.g., guided interaction, integrated assistance, and progressive learning). Applying this framework revealed that more than 70% of studies implemented authentication and encryption mechanisms, whereas fewer than 50% addressed accessibility or usability concerns. We further developed a threat model that maps IoT assets, networks, and backend servers to exploit vectors such as phishing, caregiver exploitation, and weak-password attacks, explicitly accounting for age-related vulnerabilities including cognitive decline and sensory impairment. Our results expose a systemic lack of integrated SPAU approaches in existing IoT research and translate these gaps into actionable, standards-aligned design guidelines for IoT systems designed for older adults.
Using Gaussian Splats to Create High-Fidelity Facial Geometry and Texture
Venue: arXiv cs.CV 2025
Year: 2025

We leverage increasingly popular three-dimensional neural representations in order to construct a unified and consistent explanation of a collection of uncalibrated images of the human face. Our approach utilizes Gaussian Splatting, since it is more explicit and thus more amenable to constraints than NeRFs. We leverage segmentation annotations to align the semantic regions of the face, facilitating the reconstruction of a neutral pose from only 11 images (as opposed to requiring a long video). We soft constrain the Gaussians to an underlying triangulated surface in order to provide a more structured Gaussian Splat reconstruction, which in turn informs subsequent perturbations to increase the accuracy of the underlying triangulated surface. The resulting triangulated surface can then be used in a standard graphics pipeline. In addition, and perhaps most impactful, we show how accurate geometry enables the Gaussian Splats to be transformed into texture space where they can be treated as a view-dependent neural texture. This allows one to use high visual fidelity Gaussian Splatting on any asset in a scene without the need to modify any other asset or any other aspect (geometry, lighting, renderer, etc.) of the graphics pipeline. We utilize a relightable Gaussian model to disentangle texture from lighting in order to obtain a delit high-resolution albedo texture that is also readily usable in a standard graphics pipeline. The flexibility of our system allows for training with disparate images, even with incompatible lighting, facilitating robust regularization. Finally, we demonstrate the efficacy of our approach by illustrating its use in a text-driven asset creation pipeline.
Conquering the Multiverse: The River Voting Method with Efficient Parallel Universe Tiebreaking
Venue: arXiv cs.DS 2025
Year: 2025

Democracy relies on making collective decisions through voting. In addition, voting procedures have further applications, for example in the training of artificial intelligence. An essential criterion for determining the winner of a fair election is that all alternatives are treated equally: this is called neutrality. The established Ranked Pairs voting method cannot simultaneously guarantee neutrality and be computationally tractable for election with ties. River, the recently introduced voting method, shares desirable properties with Ranked Pairs and has further advantages, such as a new property related to resistance against manipulation. Both Ranked Pairs and River use a weighted margin graph to model the election. Ties in the election can lead to edges of equal margin. To order the edges in such a case, a tiebreaking scheme must be employed. Many tiebreaks violate neutrality or other important properties. A tiebreaking scheme that preserves neutrality is Parallel Universe Tiebreaking (PUT). Ranked Pairs with PUT is NP-hard to compute. The main result of this thesis shows that River with PUT can be computed in polynomial worst-case runtime: We can check whether an alternative is a River PUT winner, by running River with a specially constructed ordering of the edges. To construct this ordering, we introduce the semi-River diagram which contains the edges that can appear in any River diagram for some arbitrary tiebreak. On this diagram we can compute the River winners, by applying a variant of Prims algorithm per alternative. Additionally, we give an algorithm improve the previous naive runtime of River from $\mathcal{O}(n^4)$ to $\mathcal{O}(n^2 \log n)$, where n is the number of alternatives.
Geometric Disentanglement of Text Embeddings for Subject-Consistent Text-to-Image Generation using A Single Prompt
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image diffusion models excel at generating high-quality images from natural language descriptions but often fail to preserve subject consistency across multiple outputs, limiting their use in visual storytelling. Existing approaches rely on model fine-tuning or image conditioning, which are computationally expensive and require per-subject optimization. 1Prompt1Story, a training-free approach, concatenates all scene descriptions into a single prompt and rescales token embeddings, but it suffers from semantic leakage, where embeddings across frames become entangled, causing text misalignment. In this paper, we propose a simple yet effective training-free approach that addresses semantic entanglement from a geometric perspective by refining text embeddings to suppress unwanted semantics. Extensive experiments prove that our approach significantly improves both subject consistency and text alignment over existing baselines.
Single-View Shape Completion for Robotic Grasping in Clutter
Venue: arXiv cs.RO 2025
Year: 2025

In vision-based robot manipulation, a single camera view can only capture one side of objects of interest, with additional occlusions in cluttered scenes further restricting visibility. As a result, the observed geometry is incomplete, and grasp estimation algorithms perform suboptimally. To address this limitation, we leverage diffusion models to perform category-level 3D shape completion from partial depth observations obtained from a single view, reconstructing complete object geometries to provide richer context for grasp planning. Our method focuses on common household items with diverse geometries, generating full 3D shapes that serve as input to downstream grasp inference networks. Unlike prior work, which primarily considers isolated objects or minimal clutter, we evaluate shape completion and grasping in realistic clutter scenarios with household objects. In preliminary evaluations on a cluttered scene, our approach consistently results in better grasp success rates than a naive baseline without shape completion by 23% and over a recent state of the art shape completion approach by 19%. Our code is available at https://amm.aass.oru.se/shape-completion-grasping/.
New Quantum Stabilizer Codes from generalized Monomial-Cartesian Codes constructed using two different generalized Reed-Solomon codes
Venue: arXiv cs.IT 2025
Year: 2025

In this work, we define Generalized Monomial Cartesian Codes (GMCC), which constitute a natural extension of generalized Reed-Solomon codes. We describe how two different generalized Reed-Solomon codes can be combined to construct one GMCC. We further establish sufficient conditions ensuring that the GMCC are Hermitian self-orthogonal, thus leading to new constructions of quantum codes.
A Survey on Spatio-Temporal Knowledge Graph Models
Venue: arXiv cs.SI 2025
Year: 2025

Many complex real-world systems exhibit inherently intertwined temporal and spatial characteristics. Spatio-temporal knowledge graphs (STKGs) have therefore emerged as a powerful representation paradigm, as they integrate entities, relationships, time and space within a unified graph structure. They are increasingly applied across diverse domains, including environmental systems and urban, transportation, social and human mobility networks. However, modeling STKGs remains challenging: their foundations span classical graph theory as well as temporal and spatial graph models, which have evolved independently across different research communities and follow heterogeneous modeling assumptions and terminologies. As a result, existing approaches often lack conceptual alignment, generalizability and reusability. This survey provides a systematic review of spatio-temporal knowledge graph models, tracing their origins in static, temporal and spatial graph modeling. We analyze existing approaches along key modeling dimensions, including edge semantics, temporal and spatial annotation strategies, temporal and spatial semantics and relate these choices to their respective application domains. Our analysis reveals that unified modeling frameworks are largely absent and that most current models are tailored to specific use cases rather than designed for reuse or long-term knowledge preservation. Based on these findings, we derive modeling guidelines and identify open challenges to guide future research.
Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
Venue: arXiv cs.AI 2025
Year: 2025

Empirical research on meta-algorithmics, such as algorithm selection, configuration, and scheduling, often relies on extensive and thus computationally expensive experiments. With the large degree of freedom we have over our experimental setup and design comes a plethora of possible error sources that threaten the scalability and validity of our scientific insights. Best practices for meta-algorithmic research exist, but they are scattered between different publications and fields, and continue to evolve separately from each other. In this report, we collect good practices for empirical meta-algorithmic research across the subfields of the COSEAL community, encompassing the entire experimental cycle: from formulating research questions and selecting an experimental design, to executing experiments, and ultimately, analyzing and presenting results impartially. It establishes the current state-of-the-art practices within meta-algorithmic research and serves as a guideline to both new researchers and practitioners in meta-algorithmic fields.
VenusBench-GD: A Comprehensive Multi-Platform GUI Benchmark for Diverse Grounding Tasks
Venue: arXiv cs.CV 2025
Year: 2025

GUI grounding is a critical component in building capable GUI agents. However, existing grounding benchmarks suffer from significant limitations: they either provide insufficient data volume and narrow domain coverage, or focus excessively on a single platform and require highly specialized domain knowledge. In this work, we present VenusBench-GD, a comprehensive, bilingual benchmark for GUI grounding that spans multiple platforms, enabling hierarchical evaluation for real-word applications. VenusBench-GD contributes as follows: (i) we introduce a large-scale, cross-platform benchmark with extensive coverage of applications, diverse UI elements, and rich annotated data, (ii) we establish a high-quality data construction pipeline for grounding tasks, achieving higher annotation accuracy than existing benchmarks, and (iii) we extend the scope of element grounding by proposing a hierarchical task taxonomy that divides grounding into basic and advanced categories, encompassing six distinct subtasks designed to evaluate models from complementary perspectives. Our experimental findings reveal critical insights: general-purpose multimodal models now match or even surpass specialized GUI models on basic grounding tasks. In contrast, advanced tasks, still favor GUI-specialized models, though they exhibit significant overfitting and poor robustness. These results underscore the necessity of comprehensive, multi-tiered evaluation frameworks.