OpenRaiser/Intern-Atlas huggingface dataset:
Twinning for Space-Air-Ground-Sea Integrated Networks: Beyond Conventional Digital Twin Towards Goal-Oriented Semantic Twin
Venue: arXiv cs.DC 2025
Year: 2025

A space-air-ground-sea integrated network (SAGSIN) has emerged as a cornerstone of 6G systems, establishing a unified global architecture by integrating multi-domain network resources. Motivated by the demand for real-time situational awareness and intelligent operational maintenance, digital twin (DT) technology was initially regarded as a promising solution, owing to its capability to create virtual replicas and emulate physical system behaviors. However, in the context of SAGSIN, the high-fidelity, full-scale modeling paradigm inherent to conventional DTs encounters fundamental limitations, including prohibitive computational overhead, delayed model synchronization, and cross-system semantic gaps. To address these limitations, this survey paper proposes a novel twinning framework: goal-oriented semantic twin (GOST). Unlike DTs that pursue physical mirroring, GOST prioritizes ``utility'' over ``fidelity,'' leveraging semantic technologies and goal-oriented principles to construct lightweight, task-specific representations. This paper systematically articulates the GOST framework through three layers: knowledge-based semantics, data-driven semantics, and goal-oriented principles. Furthermore, we provide a comprehensive tutorial on constructing GOST by detailing its core enabling technologies and introduce a multidimensional evaluation framework for GOST. We present a case study targeting collaborative tracking tasks in remote satellite-UAV networks, demonstrating that GOST significantly outperforms conventional DTs in timeliness of perceptual data and collaborative tracking. Finally, we outline research directions, establishing GOST as a transformative twinning paradigm to guide the development of SAGSIN.
WING: An Adaptive and Gamified Mobile Learning Platform for Neurodivergent Literacy
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents WING, an adaptive and gamified mobile learning platform designed to support literacy development for neurodivergent children. Motivated by the limitations of traditional literacy approaches in addressing diverse cognitive profiles, the platform integrates inclusive Human-Computer Interaction principles, multisensory design, and adaptive learning paths. WING digitally transposes the Alfabetização Adaptada (AFA) method into an interactive mobile environment, combining usability guidelines for neurodivergent users with gamification strategies to enhance engagement and autonomy. The study follows an applied research methodology, encompassing requirements elicitation, inclusive interface design, high-fidelity prototyping, and qualitative and quantitative evaluation planning. Preliminary results include a functional minimum viable product validated through expert feedback and public exhibitions, indicating the feasibility and potential pedagogical impact of the proposed approach. The platform aims to act as a complementary educational tool, promoting accessibility, personalization, and inclusive digital literacy.
Evaluation of Generative Models for Emotional 3D Animation Generation in VR
Venue: arXiv cs.HC 2025
Year: 2025

Social interactions incorporate nonverbal signals to convey emotions alongside speech, including facial expressions and body gestures. Generative models have demonstrated promising results in creating full-body nonverbal animations synchronized with speech; however, evaluations using statistical metrics in 2D settings fail to fully capture user-perceived emotions, limiting our understanding of model effectiveness. To address this, we evaluate emotional 3D animation generative models within a Virtual Reality (VR) environment, emphasizing user-centric metrics emotional arousal realism, naturalness, enjoyment, diversity, and interaction quality in a real-time human-agent interaction scenario. Through a user study (N=48), we examine perceived emotional quality for three state of the art speech-driven 3D animation methods across two emotions happiness (high arousal) and neutral (mid arousal). Additionally, we compare these generative models against real human expressions obtained via a reconstruction-based method to assess both their strengths and limitations and how closely they replicate real human facial and body expressions. Our results demonstrate that methods explicitly modeling emotions lead to higher recognition accuracy compared to those focusing solely on speech-driven synchrony. Users rated the realism and naturalness of happy animations significantly higher than those of neutral animations, highlighting the limitations of current generative models in handling subtle emotional states. Generative models underperformed compared to reconstruction-based methods in facial expression quality, and all methods received relatively low ratings for animation enjoyment and interaction quality, emphasizing the importance of incorporating user-centric evaluations into generative model development. Finally, participants positively recognized animation diversity across all generative models.
LAPX: Lightweight Hourglass Network with Global Context
Venue: arXiv cs.CV 2025
Year: 2025

Human pose estimation is a crucial task in computer vision. Methods that have SOTA (State-of-the-Art) accuracy, often involve a large number of parameters and incur substantial computational cost. Many lightweight variants have been proposed to reduce the model size and computational cost of them. However, several of these methods still contain components that are not well suited for efficient deployment on edge devices. Moreover, models that primarily emphasize inference speed on edge devices often suffer from limited accuracy due to their overly simplified designs. To address these limitations, we propose LAPX, an Hourglass network with self-attention that captures global contextual information, based on previous work, LAP. In addition to adopting the self-attention module, LAPX advances the stage design and refine the lightweight attention modules. It achieves competitive results on two benchmark datasets, MPII and COCO, with only 2.3M parameters, and demonstrates real-time performance, confirming its edge-device suitability.
AIMM: An AI-Driven Multimodal Framework for Detecting Social-Media-Influenced Stock Market Manipulation
Venue: arXiv cs.LG 2025
Year: 2025

Market manipulation now routinely originates from coordinated social media campaigns, not isolated trades. Retail investors, regulators, and brokerages need tools that connect online narratives and coordination patterns to market behavior. We present AIMM, an AI-driven framework that fuses Reddit activity, bot and coordination indicators, and OHLCV market features into a daily AIMM Manipulation Risk Score for each ticker. The system uses a parquet-native pipeline with a Streamlit dashboard that allows analysts to explore suspicious windows, inspect underlying posts and price action, and log model outputs over time. Due to Reddit API restrictions, we employ calibrated synthetic social features matching documented event characteristics; market data (OHLCV) uses real historical data from Yahoo Finance. This release makes three contributions. First, we build the AIMM Ground Truth dataset (AIMM-GT): 33 labeled ticker-days spanning eight equities, drawing from SEC enforcement actions, community-verified manipulation cases, and matched normal controls. Second, we implement forward-walk evaluation and prospective prediction logging for both retrospective and deployment-style assessment. Third, we analyze lead times and show that AIMM flagged GME 22 days before the January 2021 squeeze peak. The current labeled set is small (33 ticker-days, 3 positive events), but results show preliminary discriminative capability and early warnings for the GME incident. We release the code, dataset schema, and dashboard design to support research on social media-driven market surveillance.
DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack
Venue: arXiv cs.CR 2025
Year: 2025

With the rapid development of cloud-based services, large language models (LLMs) have become increasingly accessible through various web platforms. However, this accessibility has also led to growing risks of model abuse. LLM watermarking has emerged as an effective approach to mitigate such misuse and protect intellectual property. Existing watermarking algorithms, however, primarily focus on defending against paraphrase attacks while overlooking piggyback spoofing attacks, which can inject harmful content, compromise watermark reliability, and undermine trust in attribution. To address this limitation, we propose DualGuard, the first watermarking algorithm capable of defending against both paraphrase and spoofing attacks. DualGuard employs the adaptive dual-stream watermarking mechanism, in which two complementary watermark signals are dynamically injected based on the semantic content. This design enables DualGuard not only to detect but also to trace spoofing attacks, thereby ensuring reliable and trustworthy watermark detection. Extensive experiments conducted across multiple datasets and language models demonstrate that DualGuard achieves excellent detectability, robustness, traceability, and text quality, effectively advancing the state of LLM watermarking for real-world applications.
Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithms on Smooth Functions
Venue: arXiv cs.LG 2025
Year: 2025

Rank-based zeroth-order (ZO) optimization -- which relies only on the ordering of function evaluations -- offers strong robustness to noise and monotone transformations, and underlies many successful algorithms such as CMA-ES, natural evolution strategies, and rank-based genetic algorithms. Despite its widespread use, the theoretical understanding of rank-based ZO methods remains limited: existing analyses provide only asymptotic insights and do not yield explicit convergence rates for algorithms selecting the top-$k$ directions. This work closes this gap by analyzing a simple rank-based ZO algorithm and establishing the first \emph{explicit}, and \emph{non-asymptotic} query complexities. For a $d$-dimension problem, if the function is $L$-smooth and $μ$-strongly convex, the algorithm achieves $\widetilde{\mathcal O}\!\left(\frac{dL}μ\log\!\frac{dL}{μδ}\log\!\frac{1}{\varepsilon}\right)$ to find an $\varepsilon$-suboptimal solution, and for smooth nonconvex objectives it reaches $\mathcal O\!\left(\frac{dL}{\varepsilon}\log\!\frac{1}{\varepsilon}\right)$. Notation $\cO(\cdot)$ hides constant terms and $\widetilde{\mathcal O}(\cdot)$ hides extra $\log\log\frac{1}{\varepsilon}$ term. These query complexities hold with a probability at least $1-δ$ with $0<δ<1$. The analysis in this paper is novel and avoids classical drift and information-geometric techniques. Our analysis offers new insight into why rank-based heuristics lead to efficient ZO optimization.
Learning High-Quality Initial Noise for Single-View Synthesis with Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Single-view novel view synthesis (NVS) models based on diffusion models have recently attracted increasing attention, as they can generate a series of novel view images from a single image prompt and camera pose information as conditions. It has been observed that in diffusion models, certain high-quality initial noise patterns lead to better generation results than others. However, there remains a lack of dedicated learning frameworks that enable NVS models to learn such high-quality noise. To obtain high-quality initial noise from random Gaussian noise, we make the following contributions. First, we design a discretized Euler inversion method to inject image semantic information into random noise, thereby constructing paired datasets of random and high-quality noise. Second, we propose a learning framework based on an encoder-decoder network (EDN) that directly transforms random noise into high-quality noise. Experiments demonstrate that the proposed EDN can be seamlessly plugged into various NVS models, such as SV3D and MV-Adapter, achieving significant performance improvements across multiple datasets. Code is available at: https://github.com/zhihao0512/EDN.
The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Models
Venue: arXiv cs.IR 2025
Year: 2025

Reranking is a critical stage in contemporary information retrieval (IR) systems, improving the relevance of the user-presented final results by honing initial candidate sets. This paper is a thorough guide to examine the changing reranker landscape and offer a clear view of the advancements made in reranking methods. We present a comprehensive survey of reranking models employed in IR, particularly within modern Retrieval Augmented Generation (RAG) pipelines, where retrieved documents notably influence output quality. We embark on a chronological journey through the historical trajectory of reranking techniques, starting with foundational approaches, before exploring the wide range of sophisticated neural network architectures such as cross-encoders, sequence-generation models like T5, and Graph Neural Networks (GNNs) utilized for structural information. Recognizing the computational cost of advancing neural rerankers, we analyze techniques for enhancing efficiency, notably knowledge distillation for creating competitive, lighter alternatives. Furthermore, we map the emerging territory of integrating Large Language Models (LLMs) in reranking, examining novel prompting strategies and fine-tuning tactics. This survey seeks to elucidate the fundamental ideas, relative effectiveness, computational features, and real-world trade-offs of various reranking strategies. The survey provides a structured synthesis of the diverse reranking paradigms, highlighting their underlying principles and comparative strengths and weaknesses.
Learning to Wait: Synchronizing Agents with the Physical World
Venue: arXiv cs.AI 2025
Year: 2025

Real-world agentic tasks, unlike synchronous Markov Decision Processes (MDPs), often involve non-blocking actions with variable latencies, creating a fundamental \textit{Temporal Gap} between action initiation and completion. Existing environment-side solutions, such as blocking wrappers or frequent polling, either limit scalability or dilute the agent's context window with redundant observations. In this work, we propose an \textbf{Agent-side Approach} that empowers Large Language Models (LLMs) to actively align their \textit{Cognitive Timeline} with the physical world. By extending the Code-as-Action paradigm to the temporal domain, agents utilize semantic priors and In-Context Learning (ICL) to predict precise waiting durations (\texttt{time.sleep(t)}), effectively synchronizing with asynchronous environment without exhaustive checking. Experiments in a simulated Kubernetes cluster demonstrate that agents can precisely calibrate their internal clocks to minimize both query overhead and execution latency, validating that temporal awareness is a learnable capability essential for autonomous evolution in open-ended environments.
Privacy-Aware Sharing of Raw Spatial Sensor Data for Cooperative Perception
Venue: arXiv cs.NI 2025
Year: 2025

Cooperative perception between vehicles is poised to offer robust and reliable scene understanding. Recently, we are witnessing experimental systems research building testbeds that share raw spatial sensor data for cooperative perception. While there has been a marked improvement in accuracies and is the natural way forward, we take a moment to consider the problems with such an approach for eventual adoption by automakers. In this paper, we first argue that new forms of privacy concerns arise and discourage stakeholders to share raw sensor data. Next, we present SHARP, a research framework to minimize privacy leakage and drive stakeholders towards the ambitious goal of raw data based cooperative perception. Finally, we discuss open questions for networked systems, mobile computing, perception researchers, industry and government in realizing our proposed framework.
Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learning
Venue: arXiv cs.CV 2025
Year: 2025

Fluorescence lifetime imaging microscopy (FLIM) is a powerful quantitative technique that provides metabolic and molecular contrast, offering strong translational potential for label-free, real-time diagnostics. However, its clinical adoption remains limited by long pixel dwell times and low signal-to-noise ratio (SNR), which impose a stricter resolution-speed trade-off than conventional optical imaging approaches. Here, we introduce FLIM_PSR_k, a deep learning-based multi-channel pixel super-resolution (PSR) framework that reconstructs high-resolution FLIM images from data acquired with up to a 5-fold increased pixel size. The model is trained using the conditional generative adversarial network (cGAN) framework, which, compared to diffusion model-based alternatives, delivers a more robust PSR reconstruction with substantially shorter inference times, a crucial advantage for practical deployment. FLIM_PSR_k not only enables faster image acquisition but can also alleviate SNR limitations in autofluorescence-based FLIM. Blind testing on held-out patient-derived tumor tissue samples demonstrates that FLIM_PSR_k reliably achieves a super-resolution factor of k = 5, resulting in a 25-fold increase in the space-bandwidth product of the output images and revealing fine architectural features lost in lower-resolution inputs, with statistically significant improvements across various image quality metrics. By increasing FLIM's effective spatial resolution, FLIM_PSR_k advances lifetime imaging toward faster, higher-resolution, and hardware-flexible implementations compatible with low-numerical-aperture and miniaturized platforms, better positioning FLIM for translational applications.
Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
Venue: arXiv cs.CR 2025
Year: 2025

Synthetic data generation is gaining traction as a privacy enhancing technology (PET). When properly generated, synthetic data preserve the analytic utility of real data while avoiding the retention of information that would allow the identification of specific individuals. However, the concept of data privacy remains elusive, making it challenging for practitioners to evaluate and benchmark the degree of privacy protection offered by synthetic data. In this paper, we propose a framework to empirically assess the efficacy of tabular synthetic data privacy quantification methods through controlled, deliberate risk insertion. To demonstrate this framework, we survey existing approaches to synthetic data privacy quantification and the related legal theory. We then apply the framework to the main privacy quantification methods with no-box threat models on publicly available datasets.
Evaluating OpenAI GPT Models for Translation of Endangered Uralic Languages: A Comparison of Reasoning and Non-Reasoning Architectures
Venue: arXiv cs.CL 2025
Year: 2025

The evaluation of Large Language Models (LLMs) for translation tasks has primarily focused on high-resource languages, leaving a significant gap in understanding their performance on low-resource and endangered languages. This study presents a comprehensive comparison of OpenAI's GPT models, specifically examining the differences between reasoning and non-reasoning architectures for translating between Finnish and four low-resource Uralic languages: Komi-Zyrian, Moksha, Erzya, and Udmurt. Using a parallel corpus of literary texts, we evaluate model willingness to attempt translation through refusal rate analysis across different model architectures. Our findings reveal significant performance variations between reasoning and non-reasoning models, with reasoning models showing 16 percentage points lower refusal rates. The results provide valuable insights for researchers and practitioners working with Uralic languages and contribute to the broader understanding of reasoning model capabilities for endangered language preservation.
Ein Typenrad auf der Überholspur: Die Kult-Schreibmaschine "Erika" trifft KI
Venue: arXiv cs.HC 2025
Year: 2025

In the 15th century, printing revolutionized the dissemination of information. Innovations such as typewriters and computers have increased the speed and volume of information flows over time. More recent developments in large language models such as ChatGPT enable text to be generated in a matter of seconds. However, many people do not understand how this works and what the long-term implications are. That is why we have "hacked" an old typewriter so that users can interact with an LLM chatbot, which over 1,200 participants have now been able to experience. It helps to understand the possibilities and limitations of AI. It gives us researchers insights into participants' concepts of AI as well as their expectations and concerns. It raises questions about these technological developments and stimulates discussions about the social impact of the intensification and acceleration of information and communication flows.
MACL: Multi-Label Adaptive Contrastive Learning Loss for Remote Sensing Image Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

Semantic overlap among land-cover categories, highly imbalanced label distributions, and complex inter-class co-occurrence patterns constitute significant challenges for multi-label remote-sensing image retrieval. In this article, Multi-Label Adaptive Contrastive Learning (MACL) is introduced as an extension of contrastive learning to address them. It integrates label-aware sampling, frequency-sensitive weighting, and dynamic-temperature scaling to achieve balanced representation learning across both common and rare categories. Extensive experiments on three benchmark datasets (DLRSD, ML-AID, and WHDLD), show that MACL consistently outperforms contrastive-loss based baselines, effectively mitigating semantic imbalance and delivering more reliable retrieval performance in large-scale remote-sensing archives. Code, pretrained models, and evaluation scripts will be released at https://github.com/amna/MACL upon acceptance.
ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

One-shot imitation learning (OSIL) offers a promising way to teach robots new skills without large-scale data collection. However, current OSIL methods are primarily limited to short-horizon tasks, thus limiting their applicability to complex, long-horizon manipulations. To address this limitation, we propose ManiLong-Shot, a novel framework that enables effective OSIL for long-horizon prehensile manipulation tasks. ManiLong-Shot structures long-horizon tasks around physical interaction events, reframing the problem as sequencing interaction-aware primitives instead of directly imitating continuous trajectories. This primitive decomposition can be driven by high-level reasoning from a vision-language model (VLM) or by rule-based heuristics derived from robot state changes. For each primitive, ManiLong-Shot predicts invariant regions critical to the interaction, establishes correspondences between the demonstration and the current observation, and computes the target end-effector pose, enabling effective task execution. Extensive simulation experiments show that ManiLong-Shot, trained on only 10 short-horizon tasks, generalizes to 20 unseen long-horizon tasks across three difficulty levels via one-shot imitation, achieving a 22.8% relative improvement over the SOTA. Additionally, real-robot experiments validate ManiLong-Shot's ability to robustly execute three long-horizon manipulation tasks via OSIL, confirming its practical applicability.