OpenRaiser/Intern-Atlas huggingface dataset:
Specification-Aware Machine Translation and Evaluation for Purpose Alignment
Venue: arXiv cs.CL 2025
Year: 2025

In professional settings, translation is guided by communicative goals and client needs, often formalized as specifications. While existing evaluation frameworks acknowledge the importance of such specifications, these specifications are often treated only implicitly in machine translation (MT) research. Drawing on translation studies, we provide a theoretical rationale for why specifications matter in professional translation, as well as a practical guide to implementing specification-aware MT and evaluation. Building on this foundation, we apply our framework to the translation of investor relations texts from 33 publicly listed companies. In our experiment, we compare five translation types, including official human translations and prompt-based outputs from large language models (LLMs), using expert error analysis, user preference rankings, and an automatic metric. The results show that LLM translations guided by specifications consistently outperformed official human translations in human evaluations, highlighting a gap between perceived and expected quality. These findings demonstrate that integrating specifications into MT workflows, with human oversight, can improve translation quality in ways aligned with professional practice.
Morphologies of a sagging elastica with intrinsic sensing and actuation
Venue: arXiv cs.RO 2025
Year: 2025

The morphology of a slender soft-robot can be modified by sensing its shape via sensors and exerting moments via actuators embedded along its body. The actuating moments required to morph these soft-robots to a desired shape are often difficult to compute due to the geometric non-linearity associated with the structure, the errors in modeling the experimental system, and the limitations in sensing and feedback/actuation capabilities. In this article, we explore the effect of a simple feedback strategy (actuation being proportional to the sensed curvature) on the shape of a soft-robot, modeled as an elastica. The finite number of sensors and actuators, often seen in experiments, is captured in the model via filters of specified widths. Using proportional feedback, we study the simple task of straightening the device by compensating for the sagging introduced by its self-weight. The device undergoes a hierarchy of morphological instabilities defined in the phase-space given by the gravito-bending number, non-dimensional sensing/feedback gain, and the scaled width of the filter. For complex shape-morphing tasks, given a perfect model of the device with limited sensing and actuating capabilities, we find that a trade-off arises (set by the sensor spacing & actuator size) between capturing the long and short wavelength features. We show that the error in shape-morphing is minimal for a fixed filter width when we choose an appropriate actuating gain (whose magnitude goes as a square of the filter width). Our model provides a quantitative lens to study and design slender soft devices with limited sensing and actuating capabilities for complex maneuvering applications.
Hyperbolic Sets in Incomplete Tables
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we extend results about the implementation of the Berlekamp-Massey-Sakata algorithm on data tables having a number of unknown values.
Impossibility Results of Card-Based Protocols via Mathematical Optimization
Venue: arXiv cs.CR 2025
Year: 2025

This paper introduces mathematical optimization as a new method for proving impossibility results in the field of card-based cryptography. While previous impossibility proofs were often limited to cases involving a small number of cards, this new approach establishes results that hold for a large number of cards. The research focuses on single-cut full-open (SCFO) protocols, which consist of performing one random cut and then revealing all cards. The main contribution is that for any three-variable Boolean function, no new SCFO protocols exist beyond those already known, under the condition that all additional cards have the same color. The significance of this work is that it provides a new framework for proving impossibility results and delivers a proof that is valid for any number of cards, as long as all additional cards have the same color.
From OCL to JSX: declarative constraint modeling in modern SaaS tools
Venue: arXiv cs.SE 2025
Year: 2025

The rise of Node.js in 2010, followed by frameworks like Angular, React, and Vue.js, has accelerated the growth of low code development platforms. These platforms harness modern UIX paradigms, component-based architectures, and the SaaS model to enable non-experts to build software. The widespread adoption of single-page applications (SPAs), driven by these frameworks, has shaped low-code tools to deliver responsive, client side experiences. In parallel, many modeling platforms have moved to the cloud, adopting either server-centric architectures (e.g., GSLP) or client-side intelligence via SPA frameworks, anchoring core components in JavaScript or TypeScript. Within this context, OCL.js, a JavaScript-based implementation of the Object Constraint Language, offers a web aligned approach to model validation, yet faces challenges such as partial standard coverage, limited adoption, and weak integration with modern front-end toolchains. In this paper, we explore JSX, a declarative, functional subset of JavaScript/TypeScript used in the React ecosystem, as an alternative to constraint expression in SaaS-based modeling environments. Its component-oriented structure supports inductive definitions for syntax, code generation, and querying. Through empirical evaluation, we compare JSX-based constraints with OCL.js across representative modeling scenarios. Results show JSX provides broader expressiveness and better fits front-end-first architectures, indicating a promising path for constraint specification in modern modeling tools.
Overview of PlantCLEF 2022: Image-based plant identification at global scale
Venue: arXiv cs.CV 2025
Year: 2025

It is estimated that there are more than 300,000 species of vascular plants in the world. Increasing our knowledge of these species is of paramount importance for the development of human civilization (agriculture, construction, pharmacopoeia, etc.), especially in the context of the biodiversity crisis. However, the burden of systematic plant identification by human experts strongly penalizes the aggregation of new data and knowledge. Since then, automatic identification has made considerable progress in recent years as highlighted during all previous editions of PlantCLEF. Deep learning techniques now seem mature enough to address the ultimate but realistic problem of global identification of plant biodiversity in spite of many problems that the data may present (a huge number of classes, very strongly unbalanced classes, partially erroneous identifications, duplications, variable visual quality, diversity of visual contents such as photos or herbarium sheets, etc). The PlantCLEF2022 challenge edition proposes to take a step in this direction by tackling a multi-image (and metadata) classification problem with a very large number of classes (80k plant species). This paper presents the resources and evaluations of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of key findings.
A$^2$M$^2$-Net: Adaptively Aligned Multi-Scale Moment for Few-Shot Action Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Thanks to capability to alleviate the cost of large-scale annotation, few-shot action recognition (FSAR) has attracted increased attention of researchers in recent years. Existing FSAR approaches typically neglect the role of individual motion pattern in comparison, and under-explore the feature statistics for video dynamics. Thereby, they struggle to handle the challenging temporal misalignment in video dynamics, particularly by using 2D backbones. To overcome these limitations, this work proposes an adaptively aligned multi-scale second-order moment network, namely A$^2$M$^2$-Net, to describe the latent video dynamics with a collection of powerful representation candidates and adaptively align them in an instance-guided manner. To this end, our A$^2$M$^2$-Net involves two core components, namely, adaptive alignment (A$^2$ module) for matching, and multi-scale second-order moment (M$^2$ block) for strong representation. Specifically, M$^2$ block develops a collection of semantic second-order descriptors at multiple spatio-temporal scales. Furthermore, A$^2$ module aims to adaptively select informative candidate descriptors while considering the individual motion pattern. By such means, our A$^2$M$^2$-Net is able to handle the challenging temporal misalignment problem by establishing an adaptive alignment protocol for strong representation. Notably, our proposed method generalizes well to various few-shot settings and diverse metrics. The experiments are conducted on five widely used FSAR benchmarks, and the results show our A$^2$M$^2$-Net achieves very competitive performance compared to state-of-the-arts, demonstrating its effectiveness and generalization.
SISMA: Semantic Face Image Synthesis with Mamba
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion Models have become very popular for Semantic Image Synthesis (SIS) of human faces. Nevertheless, their training and inference is computationally expensive and their computational requirements are high due to the quadratic complexity of attention layers. In this paper, we propose a novel architecture called SISMA, based on the recently proposed Mamba. SISMA generates high quality samples by controlling their shape using a semantic mask at a reduced computational demand. We validated our approach through comprehensive experiments with CelebAMask-HQ, revealing that our architecture not only achieves a better FID score yet also operates at three times the speed of state-of-the-art architectures. This indicates that the proposed design is a viable, lightweight substitute to transformer-based models.
GLo-MAPPO: A Multi-Agent Proximal Policy Optimization for Energy Efficiency in UAV-Assisted LoRa Networks
Venue: arXiv cs.NI 2025
Year: 2025

Long Range (LoRa) based low-power wide area networks (LPWANs) are crucial for enabling next-generation IoT (NG-IoT) applications in 5G/6G ecosystems due to their long-range, low-power, and low-cost characteristics. However, achieving high energy efficiency in such networks remains a critical challenge, particularly in large-scale or dynamically changing environments. Traditional terrestrial LoRa deployments often suffer from coverage gaps and non-line-of-sight (NLoS) propagation losses, while satellite-based IoT solutions consume excessive energy and introduce high latency, limiting their suitability for energy-constrained and delay-sensitive applications. To address these limitations, we propose a novel architecture using multiple unmanned aerial vehicles (UAVs) as flying LoRa gateways to dynamically collect data from ground-based LoRa end devices. Our approach aims to maximize the system's weighted global energy efficiency by jointly optimizing spreading factors, transmission powers, UAV trajectories, and end-device associations. Additionally, we formulate this complex optimization problem as a partially observable Markov decision process (POMDP) and propose green LoRa multi-agent proximal policy optimization (GLo-MAPPO), a multi-agent reinforcement learning (MARL) framework based on centralized training with decentralized execution (CTDE). Simulation results show that GLo-MAPPO significantly outperforms benchmark algorithms, achieving energy efficiency improvements of 71.25%, 18.56%, 67.00%, 59.73%, and 49.95% for networks with 10, 20, 30, 40, and 50 LoRa end devices, respectively.
DINOv3-Diffusion Policy: Self-Supervised Large Visual Model for Visuomotor Diffusion Policy Learning
Venue: arXiv cs.CV 2025
Year: 2025

This paper evaluates DINOv3, a recent large-scale self-supervised vision backbone, for visuomotor diffusion policy learning in robotic manipulation. We investigate whether a purely self-supervised encoder can match or surpass conventional supervised ImageNet-pretrained backbones (e.g., ResNet-18) under three regimes: training from scratch, frozen, and finetuned. Across four benchmark tasks (Push-T, Lift, Can, Square) using a unified FiLM-conditioned diffusion policy, we find that (i) finetuned DINOv3 matches or exceeds ResNet-18 on several tasks, (ii) frozen DINOv3 remains competitive, indicating strong transferable priors, and (iii) self-supervised features improve sample efficiency and robustness. These results support self-supervised large visual models as effective, generalizable perceptual front-ends for action diffusion policies, motivating further exploration of scalable label-free pretraining in robotic manipulation. Compared to using ResNet18 as a backbone, our approach with DINOv3 achieves up to a 10% absolute increase in test-time success rates on challenging tasks such as Can, and on-the-par performance in tasks like Lift, PushT, and Square.
Fast, Accurate and Interpretable Graph Classification with Topological Kernels
Venue: arXiv cs.LG 2025
Year: 2025

We introduce a novel class of explicit feature maps based on topological indices that represent each graph by a compact feature vector, enabling fast and interpretable graph classification. Using radial basis function kernels on these compact vectors, we define a measure of similarity between graphs. We perform evaluation on standard molecular datasets and observe that classification accuracies based on single topological-index feature vectors underperform compared to state-of-the-art substructure-based kernels. However, we achieve significantly faster Gram matrix evaluation -- up to $20\times$ faster -- compared to the Weisfeiler--Lehman subtree kernel. To enhance performance, we propose two extensions: 1) concatenating multiple topological indices into an \emph{Extended Feature Vector} (EFV), and 2) \emph{Linear Combination of Topological Kernels} (LCTK) by linearly combining Radial Basis Function kernels computed on feature vectors of individual topological graph indices. These extensions deliver up to $12\%$ percent accuracy gains across all the molecular datasets. A complexity analysis highlights the potential for exponential quantum speedup for some of the vector components. Our results indicate that LCTK and EFV offer a favourable trade-off between accuracy and efficiency, making them strong candidates for practical graph learning applications.
Investigating Bias: A Multilingual Pipeline for Generating, Solving, and Evaluating Math Problems with LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are increasingly used for educational support, yet their response quality varies depending on the language of interaction. This paper presents an automated multilingual pipeline for generating, solving, and evaluating math problems aligned with the German K-10 curriculum. We generated 628 math exercises and translated them into English, German, and Arabic. Three commercial LLMs (GPT-4o-mini, Gemini 2.5 Flash, and Qwen-plus) were prompted to produce step-by-step solutions in each language. A held-out panel of LLM judges, including Claude 3.5 Haiku, evaluated solution quality using a comparative framework. Results show a consistent gap, with English solutions consistently rated highest, and Arabic often ranked lower. These findings highlight persistent linguistic bias and the need for more equitable multilingual AI systems in education.
Virtual Arc Consistency for Linear Constraints in Cost Function Networks
Venue: arXiv cs.AI 2025
Year: 2025

In Constraint Programming, solving discrete minimization problems with hard and soft constraints can be done either using (i) soft global constraints, (ii) a reformulation into a linear program, or (iii) a reformulation into local cost functions. Approach (i) benefits from a vast catalog of constraints. Each soft constraint propagator communicates with other soft constraints only through the variable domains, resulting in weak lower bounds. Conversely, the approach (ii) provides a global view with strong bounds, but the size of the reformulation can be problematic. We focus on approach (iii) in which soft arc consistency (SAC) algorithms produce bounds of intermediate quality. Recently, the introduction of linear constraints as local cost functions increases their modeling expressiveness. We adapt an existing SAC algorithm to handle linear constraints. We show that our algorithm significantly improves the lower bounds compared to the original algorithm on several benchmarks, reducing solving time in some cases.
Ordered Multi-Signatures with Public-Key Aggregation from SXDH Assumption
Venue: arXiv cs.CR 2025
Year: 2025

An ordered multi-signature scheme allows multiple signers to sign a common message in a sequential manner and allows anyone to verify the signing order of signers with a public-key list. In this work, we propose an ordered multi-signature scheme by modifying the sequential aggregate signature scheme by Chatterjee and Kabaleeshwaran (ACISP 2020). Our scheme offers compact public parameter size and the public-key aggregation property. This property allows us to compress a public-key list into a short aggregated key. We prove the security of our scheme under the symmetric external Diffie-Hellman (SXDH) assumption without the random oracle model.
Breaking Token Into Concepts: Exploring Extreme Compression in Token Representation Via Compositional Shared Semantics
Venue: arXiv cs.CL 2025
Year: 2025

Standard language models employ unique, monolithic embeddings for each token, potentially limiting their ability to capture the multifaceted nature of word meanings. We investigate whether tokens can be more effectively represented through a compositional structure that accumulates diverse semantic facets. To explore this, we propose Aggregate Semantic Grouping (ASG), a novel approach leveraging Product Quantization (PQ). We apply ASG to standard transformer architectures (mBERT, XLM-R, mT5) and evaluate this representational scheme across diverse tasks (NLI, NER, QA), as well as a biomedical domain-specific benchmark (BC5CDR) using BioBERT. Our findings demonstrate that representing tokens compositionally via ASG achieves extreme compression in embedding parameters (0.4--0.5\%) while maintaining $>$95\% task performance relative to the base model, even in generative tasks and extends to both cross lingual transfer and domain-specific settings. These results validate the principle that tokens can be effectively modeled as combinations of shared semantic building blocks. ASG offers a simple yet concrete method for achieving this, showcasing how compositional representations can capture linguistic richness while enabling compact yet semantically rich models.
Diagnosing Violations of State-based Specifications in iCFTL
Venue: arXiv cs.SE 2025
Year: 2025

As modern software systems grow in complexity and operate in dynamic environments, the need for runtime analysis techniques becomes a more critical part of the verification and validation process. Runtime verification monitors the runtime system behaviour by checking whether an execution trace - a sequence of recorded events - satisfies a given specification, yielding a Boolean or quantitative verdict. However, when a specification is violated, such a verdict is often insufficient to understand why the violation happened. To fill this gap, diagnostics approaches aim to produce more informative verdicts. In this paper, we address the problem of generating informative verdicts for violated Inter-procedural Control-Flow Temporal Logic (iCFTL) specifications that express constraints over program variable values. We propose a diagnostic approach based on backward data-flow analysis to statically determine the relevant statements contributing to the specification violation. Using this analysis, we instrument the program to produce enriched execution traces. Using the enriched execution traces, we perform the runtime analysis and identify the statements whose execution led to the specification violation. We implemented our approach in a prototype tool, iCFTL-Diagnostics, and evaluated it on 112 specifications across 10 software projects. Our tool achieves 90% precision in identifying relevant statements for 100 of the 112 specifications. It reduces the number of lines that have to be inspected for diagnosing a violation by at least 90%. In terms of computational cost, iCFTL-Diagnostics generates a diagnosis within 7 min, and requires no more than 25 MB of memory. The instrumentation required to support diagnostics incurs an execution time overhead of less than 30% and a memory overhead below 20%.
Effect of Appearance and Animation Realism on the Perception of Emotionally Expressive Virtual Humans
Venue: arXiv cs.GR 2025
Year: 2025

3D Virtual Human technology is growing with several potential applications in health, education, business and telecommunications. Investigating the perception of these virtual humans can help guide to develop better and more effective applications. Recent developments show that the appearance of the virtual humans reached to a very realistic level. However, there is not yet adequate analysis on the perception of appearance and animation realism for emotionally expressive virtual humans. In this paper, we designed a user experiment and analyzed the effect of a realistic virtual human's appearance realism and animation realism in varying emotion conditions. We found that higher appearance realism and higher animation realism leads to higher social presence and higher attractiveness ratings. We also found significant effects of animation realism on perceived realism and emotion intensity levels. Our study sheds light into how appearance and animation realism effects the perception of highly realistic virtual humans in emotionally expressive scenarios and points out to future directions.
MSGAT-GRU: A Multi-Scale Graph Attention and Recurrent Model for Spatiotemporal Road Accident Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of road accidents remains challenging due to intertwined spatial, temporal, and contextual factors in urban traffic. We propose MSGAT-GRU, a multi-scale graph attention and recurrent model that jointly captures localized and long-range spatial dependencies while modeling sequential dynamics. Heterogeneous inputs, such as traffic flow, road attributes, weather, and points of interest, are systematically fused to enhance robustness and interpretability. On the Hybrid Beijing Accidents dataset, MSGAT-GRU achieves an RMSE of 0.334 and an F1-score of 0.878, consistently outperforming strong baselines. Cross-dataset evaluation on METR-LA under a 1-hour horizon further supports transferability, with RMSE of 6.48 (vs. 7.21 for the GMAN model) and comparable MAPE. Ablations indicate that three-hop spatial aggregation and a two-layer GRU offer the best accuracy-stability trade-off. These results position MSGAT-GRU as a scalable and generalizable model for intelligent transportation systems, providing interpretable signals that can inform proactive traffic management and road safety analytics.