OpenRaiser/Intern-Atlas huggingface dataset:
Exploring the Power of Diffusion Large Language Models for Software Engineering: An Empirical Investigation
Venue: arXiv cs.SE 2025
Year: 2025

Autoregressive Large Language Models (AR-LLMs) are widely used in software engineering (SE) but face limitations in processing code structure information and suffer from high inference latency. Diffusion LLMs (DLLMs) offer a promising alternative with global bidirectional encoding and decoupled generation steps. This work presents the first comprehensive evaluation of DLLMs across the software development lifecycle, including code generation, defect detection, and program repair. On a large-scale benchmark of 52,937 tasks, 7Bparameter DLLMs outperform AR-LLMs with a 30% average accuracy improvement achieving a 113% gain on cross-file repair, while maintaining superior efficiency and reduced latency. Our results establish DLLMs as a superior paradigm for SE tasks.
Accountability Capture: How Record-Keeping to Support AI Transparency and Accountability (Re)shapes Algorithmic Oversight
Venue: arXiv cs.CY 2025
Year: 2025

Accountability regimes typically encourage record-keeping to enable the transparency that supports oversight, investigation, contestation, and redress. However, implementing such record-keeping can introduce considerations, risks, and consequences, which so far remain under-explored. This paper examines how record-keeping practices bring algorithmic systems within accountability regimes, providing a basis to observe and understand their effects. For this, we introduce, describe, and elaborate 'accountability capture' -- the re-configuration of socio-technical processes and the associated downstream effects relating to record-keeping for algorithmic accountability. Surveying 100 practitioners, we evidence and characterise record-keeping issues in practice, identifying their alignment with accountability capture. We further document widespread record-keeping practices, tensions between internal and external accountability requirements, and evidence of employee resistance to practices imposed through accountability capture. We discuss these and other effects for surveillance, privacy, and data protection, highlighting considerations for algorithmic accountability communities. In all, we show that implementing record-keeping to support transparency in algorithmic accountability regimes can itself bring wider implications -- an issue requiring greater attention from practitioners, researchers, and policymakers alike.
A survey on the impact of emotions on the productivity among software developers
Venue: arXiv cs.SE 2025
Year: 2025

The time pressure associated with software development, among other factors, often leads to a diminished emotional state among developers. However, whether emotions affect perceived productivity remains an open question. This study aims to determine the strength and direction of the relationship between emotional state and perceived productivity among software developers. We employed a two-stage approach. First, a survey was conducted with a pool of nine experts to validate the measurement model. Second, a survey was administered to a pool of 88 software developers to empirically test the formulated hypothesis by using Partial Least Squares, as the data analysis method. The results of the path analysis clearly confirm the formulated hypothesis, showing that the emotional state of a software developer has a strong positive, and significant impact (beta = 0.893, p < 0.001) on perceived productivity among software developers. The findings highlight the importance of managing and improving developers emotional well-being to enhance productivity in software development environments. Additionally, interventions aimed at reducing burnout, stress, and other negative factors could have a considerable impact on their performance outcomes.
Forecasting-based Biomedical Time-series Data Synthesis for Open Data and Robust AI
Venue: arXiv cs.LG 2025
Year: 2025

The limited data availability due to strict privacy regulations and significant resource demands severely constrains biomedical time-series AI development, which creates a critical gap between data requirements and accessibility. Synthetic data generation presents a promising solution by producing artificial datasets that maintain the statistical properties of real biomedical time-series data without compromising patient confidentiality. While GANs, VAEs, and diffusion models capture global data distributions, forecasting models offer inductive biases tailored for sequential dynamics. We propose a framework for synthetic biomedical time-series data generation based on recent forecasting models that accurately replicates complex electrophysiological signals such as EEG and EMG with high fidelity. These synthetic datasets can be freely shared for open AI development and consistently improve downstream model performance. Numerical results on sleep-stage classification show up to a 3.71\% performance gain with augmentation and a 91.00\% synthetic-only accuracy that surpasses the real-data-only baseline.
SFANet: Spatial-Frequency Attention Network for Deepfake Detection
Venue: arXiv cs.CV 2025
Year: 2025

Detecting manipulated media has now become a pressing issue with the recent rise of deepfakes. Most existing approaches fail to generalize across diverse datasets and generation techniques. We thus propose a novel ensemble framework, combining the strengths of transformer-based architectures, such as Swin Transformers and ViTs, and texture-based methods, to achieve better detection accuracy and robustness. Our method introduces innovative data-splitting, sequential training, frequency splitting, patch-based attention, and face segmentation techniques to handle dataset imbalances, enhance high-impact regions (e.g., eyes and mouth), and improve generalization. Our model achieves state-of-the-art performance when tested on the DFWild-Cup dataset, a diverse subset of eight deepfake datasets. The ensemble benefits from the complementarity of these approaches, with transformers excelling in global feature extraction and texturebased methods providing interpretability. This work demonstrates that hybrid models can effectively address the evolving challenges of deepfake detection, offering a robust solution for real-world applications.
Social Agent: Mastering Dyadic Nonverbal Behavior Generation via Conversational LLM Agents
Venue: arXiv cs.GR 2025
Year: 2025

We present Social Agent, a novel framework for synthesizing realistic and contextually appropriate co-speech nonverbal behaviors in dyadic conversations. In this framework, we develop an agentic system driven by a Large Language Model (LLM) to direct the conversation flow and determine appropriate interactive behaviors for both participants. Additionally, we propose a novel dual-person gesture generation model based on an auto-regressive diffusion model, which synthesizes coordinated motions from speech signals. The output of the agentic system is translated into high-level guidance for the gesture generator, resulting in realistic movement at both the behavioral and motion levels. Furthermore, the agentic system periodically examines the movements of interlocutors and infers their intentions, forming a continuous feedback loop that enables dynamic and responsive interactions between the two participants. User studies and quantitative evaluations show that our model significantly improves the quality of dyadic interactions, producing natural, synchronized nonverbal behaviors.
EduPersona: Benchmarking Subjective Ability Boundaries of Virtual Student Agents
Venue: arXiv cs.CV 2025
Year: 2025

As large language models are increasingly integrated into education, virtual student agents are becoming vital for classroom simulation and teacher training. Yet their classroom-oriented subjective abilities remain largely unassessed, limiting understanding of model boundaries and hindering trustworthy deployment. We present EduPersona, a large-scale benchmark spanning two languages, three subjects, and ten persona types based on the Big Five theory. The dataset contains 1,308 authentic classroom dialogue rounds, corresponding to 12,814 teacher-student Q&A turns, and is further expanded through persona stylization into roughly 10 times larger scale (128k turns), providing a solid foundation for evaluation. Building on this resource, we decompose hard-to-quantify subjective performance into three progressive tasks: TASK1 basic coherence (whether behavior, emotion, expression, and voice align with classroom context), TASK2 student realism, and TASK3 long-term persona consistency, thereby establishing an evaluation framework grounded in educational theory and research value. We conduct systematic experiments on three representative LLMs, comparing their original versions with ten persona-fine-tuned variants trained on EduPersona. Results show consistent and significant average improvements across all tasks: TASK1 +33.6%, TASK2 +30.6%, and TASK3 +14.9%. These improvements highlight the dataset's effectiveness and research value, while also revealing the heterogeneous difficulty of persona modeling. In summary, EduPersona delivers the first classroom benchmark centered on subjective abilities, establishes a decoupled and verifiable research paradigm, and we will open-source both the dataset and the framework to support the broader research community in advancing trustworthy and human-like AI for education.
A Complete Diagrammatic Calculus for Conditional Gaussian Mixtures
Venue: arXiv cs.LO 2025
Year: 2025

We extend the synthetic theories of discrete and Gaussian categorical probability by introducing a diagrammatic calculus for reasoning about hybrid probabilistic models in which continuous random variables, conditioned on discrete ones, follow a multivariate Gaussian distribution. This setting includes important classes of models such as Gaussian mixture models, where each Gaussian component is selected according to a discrete variable. We develop a string diagrammatic syntax for expressing and combining these models, give it a compositional semantics, and equip it with a sound and complete equational theory that characterises when two models represent the same distribution.
Satellite Direct-to-Device from Low Earth Orbit: Techno-Economic Analysis of a Global Non-Terrestrial Network
Venue: arXiv cs.NI 2025
Year: 2025

Low Earth orbit (LEO) satellites and satellite direct-to-device (D2D) technology are at the heart of the next-generation global connectivity which promises direct access to space-based broadband services for unmodified 3GPP-compliant handsets. With a rapidly evolving ecosystem, it is important to evaluate the feasibility, cost-effectiveness, and profitability of these services. By assessing the technological aspects as well as economic implications, stakeholders can make informed decisions about investment, development, and deployment strategies. This paper presents a comprehensive techno-economic analysis (TEA) framework for evaluating LEO-based satellite D2D systems. The framework integrates a global satellite constellation model, radio propagation aspects including atmospheric and rainfall attenuation models compliant with ITU-R recommendations, 3GPP-compliant capacity calculations, realistic global population data, and an all-encompassing cost model accounting for both capital and operational expenses associated with space and ground segments. Further, the framework evaluates three different architectural options for realizing a global non-terrestrial network (NTN) for satellite D2D services. With an emphasis on reproducibility, the framework has been implemented through significant enhancements to an open-source tool. The economic assessment reveals that global satellite D2D services can be provided at a monthly cost per subscriber which is comparable to terrestrial services while achieving a positive return on investment (ROI). Moreover, the results show the potential of Open RAN technology for realizing cost-effective satellite D2D services.
How does the optimizer implicitly bias the model merging loss landscape?
Venue: arXiv cs.LG 2025
Year: 2025

Model merging methods combine models with different capabilities into a single one while maintaining the same inference cost. Two popular approaches are linear interpolation, which linearly interpolates between model weights, and task arithmetic, which combines task vectors obtained by the difference between finetuned and base models. While useful in practice, what properties make merging effective are poorly understood. This paper explores how the optimization process affects the loss landscape geometry and its impact on merging success. We show that a single quantity -- the effective noise scale -- unifies the impact of optimizer and data choices on model merging. Across architectures and datasets, the effectiveness of merging success is a non-monotonic function of effective noise, with a distinct optimum. Decomposing this quantity, we find that larger learning rates, stronger weight decay, smaller batch sizes, and data augmentation all independently modulate the effective noise scale, exhibiting the same qualitative trend. Unlike prior work that connects optimizer noise to the flatness or generalization of individual minima, we show that it also affects the global loss landscape, predicting when independently trained solutions can be merged. Our findings broaden the understanding of how optimization shapes the loss landscape geometry and its downstream consequences for model merging, suggesting the possibility of further manipulating the training dynamics to improve merging effectiveness.
JSON Whisperer: Efficient JSON Editing with LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) can modify JSON documents through natural language commands, but current approaches regenerate entire structures for each edit, resulting in computational inefficiency. We present JSON Whisperer, a framework that enables LLMs to generate RFC 6902 diff patches-expressing only the necessary modifications-rather than complete documents. We identify two key challenges in patch-based editing: (1) LLMs often miss related updates when generating isolated patches, and (2) array manipulations require tracking index shifts across operations, which LLMs handle poorly. To address these issues, we introduce EASE (Explicitly Addressed Sequence Encoding), which transforms arrays into dictionaries with stable keys, eliminating index arithmetic complexities. Our evaluation shows that patch generation with EASE reduces token usage by 31% while maintaining edit quality within 5% of full regeneration with particular gains for complex instructions and list manipulations. The dataset is available at: https://github.com/emnlp2025/JSON-Whisperer/
Performance-guided Task-specific Optimization for Multirotor Design
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. Results demonstrate that optimized designs achieve superior performance compared to conventional multirotor configurations in agile waypoint navigation tasks, including against fully actuated designs from the literature. We build and test one of the optimized designs in the real world to validate the sim2real transferability of our approach.
Evaluating UORA-Based Polling Mechanism for Latency-Sensitive Uplink Traffic in Wi-Fi Networks
Venue: arXiv cs.NI 2025
Year: 2025

IEEE 802.11ax (Wi-Fi 6) introduced Orthogonal Frequency Division Multiple Access (OFDMA), which enables simultaneous transmissions through centralized resource allocation. However, effective uplink scheduling requires the Access Point (AP) to identify which stations (STAs) have data to transmit. This typically necessitates polling for buffer status reports, a process that becomes increasingly inefficient and unscalable with growing device density. In this paper, we study how the Uplink OFDMA-based Random Access (UORA) feature improves the scalability and delay experienced by latency-sensitive data streams. We show that UORA enables efficient uplink scheduling while opportunistically identifying buffered traffic from unscheduled STAs, striking a balance between coordination and scalability. Performance evaluation of different polling strategies is done by means of simulation in ns-3. The results indicate that UORA-based polling outperforms alternative schemes in densely deployed network environments with heterogeneous uplink traffic patterns. Furthermore, under highly sparse and sporadic traffic conditions, UORA-based polling yields over 40% delay reduction compared to Scheduled Access (SA) OFDMA.
Speak, Edit, Repeat: High-Fidelity Voice Editing and Zero-Shot TTS with Cross-Attentive Mamba
Venue: arXiv cs.SD 2025
Year: 2025

We introduce MAVE (Mamba with Cross-Attention for Voice Editing and Synthesis), a novel autoregressive architecture for text-conditioned voice editing and high-fidelity text-to-speech (TTS) synthesis, built on a cross-attentive Mamba backbone. MAVE achieves state-of-the-art performance in speech editing and very competitive results in zero-shot TTS, while not being explicitly trained on the latter task, outperforming leading autoregressive and diffusion models on diverse, real-world audio. By integrating Mamba for efficient audio sequence modeling with cross-attention for precise text-acoustic alignment, MAVE enables context-aware voice editing with exceptional naturalness and speaker consistency. In pairwise human evaluations on a random 40-sample subset of the RealEdit benchmark (400 judgments), 57.2% of listeners rated MAVE - edited speech as perceptually equal to the original, while 24.8% prefered the original and 18.0% MAVE - demonstrating that in the majority of cases edits are indistinguishable from the source. MAVE compares favorably with VoiceCraft and FluentSpeech both on pairwise comparisons and standalone mean opinion score (MOS) evaluations. For zero-shot TTS, MAVE exceeds VoiceCraft in both speaker similarity and naturalness, without requiring multiple inference runs or post-processing. Remarkably, these quality gains come with a significantly lower memory cost and approximately the same latency: MAVE requires ~6x less memory than VoiceCraft during inference on utterances from the RealEdit database (mean duration: 6.21s, A100, FP16, batch size 1). Our results demonstrate that MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the synergistic integration of structured state-space modeling and cross-modal attention.
ExposureEngine: Oriented Logo Detection and Sponsor Visibility Analytics in Sports Broadcasts
Venue: arXiv cs.CV 2025
Year: 2025

Quantifying sponsor visibility in sports broadcasts is a critical marketing task traditionally hindered by manual, subjective, and unscalable analysis methods. While automated systems offer an alternative, their reliance on axis-aligned Horizontal Bounding Box (HBB) leads to inaccurate exposuremetrics when logos appear rotated or skewed due to dynamic camera angles and perspective distortions. This paper introduces ExposureEngine, an end-to-end system designed for accurate, rotation-aware sponsor visibility analytics in sports broadcasts, demonstrated in a soccer case study. Our approach predicts Oriented Bounding Box (OBB) to provide a geometrically precise fit to each logo regardless of the orientation on-screen. To train and evaluate our detector, we developed a new dataset comprising 1,103 frames from Swedish elite soccer, featuring 670 unique sponsor logos annotated with OBBs. Our model achieves a mean Average Precision (mAP@0.5) of 0.859, with a precision of 0.96 and recall of 0.87, demonstrating robust performance in localizing logos under diverse broadcast conditions. The system integrates these detections into an analytical pipeline that calculates precise visibility metrics, such as exposure duration and on-screen coverage. Furthermore, we incorporate a language-driven agentic layer, enabling users to generate reports, summaries, and media content through natural language queries. The complete system, including the dataset and the analytics dashboard, provides a comprehensive solution for auditable and interpretable sponsor measurement in sports media. An overview of the ExposureEngine is available online: https://youtu.be/tRw6OBISuW4 .
Anomaly-Aware YOLO: A Frugal yet Robust Approach to Infrared Small Target Detection
Venue: arXiv cs.CV 2025
Year: 2025

Infrared Small Target Detection (IRSTD) is a challenging task in defense applications, where complex backgrounds and tiny target sizes often result in numerous false alarms using conventional object detectors. To overcome this limitation, we propose Anomaly-Aware YOLO (AA-YOLO), which integrates a statistical anomaly detection test into its detection head. By treating small targets as unexpected patterns against the background, AA-YOLO effectively controls the false alarm rate. Our approach not only achieves competitive performance on several IRSTD benchmarks, but also demonstrates remarkable robustness in scenarios with limited training data, noise, and domain shifts. Furthermore, since only the detection head is modified, our design is highly generic and has been successfully applied across various YOLO backbones, including lightweight models. It also provides promising results when integrated into an instance segmentation YOLO. This versatility makes AA-YOLO an attractive solution for real-world deployments where resources are constrained. The code will be publicly released.
DiT-VTON: Diffusion Transformer Framework for Unified Multi-Category Virtual Try-On and Virtual Try-All with Integrated Image Editing
Venue: arXiv cs.CV 2025
Year: 2025

The rapid growth of e-commerce has intensified the demand for Virtual Try-On (VTO) technologies, enabling customers to realistically visualize products overlaid on their own images. Despite recent advances, existing VTO models face challenges with fine-grained detail preservation, robustness to real-world imagery, efficient sampling, image editing capabilities, and generalization across diverse product categories. In this paper, we present DiT-VTON, a novel VTO framework that leverages a Diffusion Transformer (DiT), renowned for its performance on text-conditioned image generation, adapted here for the image-conditioned VTO task. We systematically explore multiple DiT configurations, including in-context token concatenation, channel concatenation, and ControlNet integration, to determine the best setup for VTO image conditioning. To enhance robustness, we train the model on an expanded dataset encompassing varied backgrounds, unstructured references, and non-garment categories, demonstrating the benefits of data scaling for VTO adaptability. DiT-VTON also redefines the VTO task beyond garment try-on, offering a versatile Virtual Try-All (VTA) solution capable of handling a wide range of product categories and supporting advanced image editing functionalities such as pose preservation, localized editing, texture transfer, and object-level customization. Experimental results show that our model surpasses state-of-the-art methods on VITON-HD, achieving superior detail preservation and robustness without reliance on additional condition encoders. It also outperforms models with VTA and image editing capabilities on a diverse dataset spanning thousands of product categories.