OpenRaiser/Intern-Atlas huggingface dataset:
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking
Venue: arXiv cs.AI 2025
Year: 2025

Large language model pipelines have improved automated fact-checking for complex claims, yet many approaches rely on few-shot in-context learning with demonstrations that require substantial human effort and domain expertise. Among these, program-guided reasoning, by decomposing claims into function calls and executing reasoning programs, which has shown particular promise, but remains limited by the need for manually crafted demonstrations. Fundamentally, the underlying principles of effective reasoning program generation still remain underexplored. In this work, we introduce BOOST, a bootstrapping approach for automated few-shot reasoning program generation. BOOST iteratively refines explicit, data-driven guidelines as meta-rules for guiding demonstration creation, using a critique-refine loop that eliminates the need for human intervention. This enables a seamless transition from zero-shot to few-shot program-guided learning, enhancing interpretability and effectiveness. Experimental results show that BOOST outperforms prior few-shot baselines in both zero-shot and few-shot settings for complex claim verification.
Semantic segmentation of forest stands using deep learning
Venue: arXiv cs.CV 2025
Year: 2025

Forest stands are the fundamental units in forest management inventories, silviculture, and financial analysis within operational forestry. Over the past two decades, a common method for mapping stand borders has involved delineation through manual interpretation of stereographic aerial images. This is a time-consuming and subjective process, limiting operational efficiency and introducing inconsistencies. Substantial effort has been devoted to automating the process, using various algorithms together with aerial images and canopy height models constructed from airborne laser scanning (ALS) data, but manual interpretation remains the preferred method. Deep learning (DL) methods have demonstrated great potential in computer vision, yet their application to forest stand delineation remains unexplored in published research. This study presents a novel approach, framing stand delineation as a multiclass segmentation problem and applying a U-Net based DL framework. The model was trained and evaluated using multispectral images, ALS data, and an existing stand map created by an expert interpreter. Performance was assessed on independent data using overall accuracy, a standard metric for classification tasks that measures the proportions of correctly classified pixels. The model achieved an overall accuracy of 0.73. These results demonstrate strong potential for DL in automated stand delineation. However, a few key challenges were noted, especially for complex forest environments.
A Memory-Augmented LLM-Driven Method for Autonomous Merging of 3D Printing Work Orders
Venue: arXiv cs.AI 2025
Year: 2025

With the rapid development of 3D printing, the demand for personalized and customized production on the manufacturing line is steadily increasing. Efficient merging of printing workpieces can significantly enhance the processing efficiency of the production line. Addressing the challenge, a Large Language Model (LLM)-driven method is established in this paper for the autonomous merging of 3D printing work orders, integrated with a memory-augmented learning strategy. In industrial scenarios, both device and order features are modeled into LLM-readable natural language prompt templates, and develop an order-device matching tool along with a merging interference checking module. By incorporating a self-memory learning strategy, an intelligent agent for autonomous order merging is constructed, resulting in improved accuracy and precision in order allocation. The proposed method effectively leverages the strengths of LLMs in industrial applications while reducing hallucination.
UNDO: Understanding Distillation as Optimization
Venue: arXiv cs.CL 2025
Year: 2025

Knowledge distillation has emerged as an effective strategy for compressing large language models' (LLMs) knowledge into smaller, more efficient student models. However, standard one-shot distillation methods often produce suboptimal results due to a mismatch between teacher-generated rationales and the student's specific learning requirements. In this paper, we introduce the UNDO: UNderstanding Distillation as Optimization framework, designed to bridge this gap by iteratively identifying the student's errors and prompting the teacher to refine its explanations accordingly. Each iteration directly targets the student's learning deficiencies, motivating the teacher to provide tailored and enhanced rationales that specifically address these weaknesses. Empirical evaluations on various challenging mathematical and commonsense reasoning tasks demonstrate that our iterative distillation method, UNDO, significantly outperforms standard one-step distillation methods, achieving performance gains of up to 20%. Additionally, we show that teacher-generated data refined through our iterative process remains effective even when applied to different student models, underscoring the broad applicability of our approach. Our work fundamentally reframes knowledge distillation as an iterative teacher-student interaction, effectively leveraging dynamic refinement by the teacher for better knowledge distillation.
A Sensorimotor Vision Transformer
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents the Sensorimotor Transformer (SMT), a vision model inspired by human saccadic eye movements that prioritize high-saliency regions in visual input to enhance computational efficiency and reduce memory consumption. Unlike traditional models that process all image patches uniformly, SMT identifies and selects the most salient patches based on intrinsic two-dimensional (i2D) features, such as corners and occlusions, which are known to convey high-information content and align with human fixation patterns. The SMT architecture uses this biological principle to leverage vision transformers to process only the most informative patches, allowing for a substantial reduction in memory usage that scales with the sequence length of selected patches. This approach aligns with visual neuroscience findings, suggesting that the human visual system optimizes information gathering through selective, spatially dynamic focus. Experimental evaluations on Imagenet-1k demonstrate that SMT achieves competitive top-1 accuracy while significantly reducing memory consumption and computational complexity, particularly when a limited number of patches is used. This work introduces a saccade-like selection mechanism into transformer-based vision models, offering an efficient alternative for image analysis and providing new insights into biologically motivated architectures for resource-constrained applications.
Exploring Individual Factors in the Adoption of LLMs for Specific Software Engineering Tasks
Venue: arXiv cs.SE 2025
Year: 2025

The advent of Large Language Models (LLMs) is transforming software development, significantly enhancing software engineering processes. Research has explored their role within development teams, focusing on specific tasks such as artifact generation, decision-making support, and information retrieval. Despite the growing body of work on LLMs in software engineering, most studies have centered on broad adoption trends, neglecting the nuanced relationship between individual cognitive and behavioral factors and their impact on task-specific adoption. While factors such as perceived effort and performance expectancy have been explored at a general level, their influence on distinct software engineering tasks remains underexamined. This gap hinders the development of tailored LLM-based systems (e.g., Generative AI Agents) that align with engineers' specific needs and limits the ability of team leaders to devise effective strategies for fostering LLM adoption in targeted workflows. This study bridges this gap by surveying N=188 software engineers to test the relationship between individual attributes related to technology adoption and LLM adoption across five key tasks, using structural equation modeling (SEM). The Unified Theory of Acceptance and Use of Technology (UTAUT2) was applied to characterize individual adoption behaviors. The findings reveal that task-specific adoption is influenced by distinct factors, some of which negatively impact adoption when considered in isolation, underscoring the complexity of LLM integration in software engineering. To support effective adoption, this article provides actionable recommendations, such as seamlessly integrating LLMs into existing development environments and encouraging peer-driven knowledge sharing to enhance information retrieval.
L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video Compression
Venue: arXiv cs.CV 2025
Year: 2025

Recently, learned video compression (LVC) has shown superior performance under low-delay configuration. However, the performance of learned bi-directional video compression (LBVC) still lags behind traditional bi-directional coding. The performance gap mainly arises from inaccurate long-term motion estimation and prediction of distant frames, especially in large motion scenes. To solve these two critical problems, this paper proposes a novel LBVC framework, namely L-LBVC. Firstly, we propose an adaptive motion estimation module that can handle both short-term and long-term motions. Specifically, we directly estimate the optical flows for adjacent frames and non-adjacent frames with small motions. For non-adjacent frames with large motions, we recursively accumulate local flows between adjacent frames to estimate long-term flows. Secondly, we propose an adaptive motion prediction module that can largely reduce the bit cost for motion coding. To improve the accuracy of long-term motion prediction, we adaptively downsample reference frames during testing to match the motion ranges observed during training. Experiments show that our L-LBVC significantly outperforms previous state-of-the-art LVC methods and even surpasses VVC (VTM) on some test datasets under random access configuration.
Digital Twins for Internet of Battlespace Things (IoBT) Coalitions
Venue: arXiv cs.NI 2025
Year: 2025

This paper presents a new framework for integrating Digital Twins (DTs) within Internet of battlespace Things (IoBT) coalitions. We introduce a novel three-tier architecture that enables efficient coordination and management of DT models across coalition partners while addressing key challenges in interoperability, security, and resource allocation. The architecture comprises specialized controllers at each tier: Digital Twin Coalition Partner (DTCP) controllers managing individual coalition partners' DT resources, a central Digital Twin Coalition(DTC) controller orchestrating cross-partner coordination, and Digital Twin Coalition Mission (DTCP) controllers handling mission-specific DT interactions. We propose a hybrid approach for DT model placement across edge devices, tactical nodes, and cloud infrastructure, optimizing performance while maintaining security and accessibility. The architecture leverages software-defined networking principles for dynamic resource allocation and slice management, enabling efficient sharing of computational and network resources between DT operations and primary IoBT functions. Our proposed framework aims to provide a robust foundation for deploying and managing Digital Twins in coalition warfare, enhancing situational awareness, decision-making capabilities, and operational effectiveness while ensuring secure and interoperable operations across diverse coalition partners.
LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect
Venue: arXiv cs.CL 2025
Year: 2025

Developing Automatic Speech Recognition (ASR) systems for Tunisian Arabic Dialect is challenging due to the dialect's linguistic complexity and the scarcity of annotated speech datasets. To address these challenges, we propose the LinTO audio and textual datasets -- comprehensive resources that capture phonological and lexical features of Tunisian Arabic Dialect. These datasets include a variety of texts from numerous sources and real-world audio samples featuring diverse speakers and code-switching between Tunisian Arabic Dialect and English or French. By providing high-quality audio paired with precise transcriptions, the LinTO audio and textual datasets aim to provide qualitative material to build and benchmark ASR systems for the Tunisian Arabic Dialect. Keywords -- Tunisian Arabic Dialect, Speech-to-Text, Low-Resource Languages, Audio Data Augmentation
Fine-Tuning Visual Autoregressive Models for Subject-Driven Generation
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in text-to-image generative models have enabled numerous practical applications, including subject-driven generation, which fine-tunes pretrained models to capture subject semantics from only a few examples. While diffusion-based models produce high-quality images, their extensive denoising steps result in significant computational overhead, limiting real-world applicability. Visual autoregressive (VAR) models, which predict next-scale tokens rather than spatially adjacent ones, offer significantly faster inference suitable for practical deployment. In this paper, we propose the first VAR-based approach for subject-driven generation. However, naive fine-tuning VAR leads to computational overhead, language drift, and reduced diversity. To address these challenges, we introduce selective layer tuning to reduce complexity and prior distillation to mitigate language drift. Additionally, we found that the early stages have a greater influence on the generation of subject than the latter stages, which merely synthesize minor details. Based on this finding, we propose scale-wise weighted tuning, which prioritizes coarser resolutions for promoting the model to focus on the subject-relevant information instead of local details. Extensive experiments validate that our method significantly outperforms diffusion-based baselines across various metrics and demonstrates its practical usage.
Optimizing Resource Allocation to Mitigate the Risk of Disruptive Events in Homeland Security and Emergency Management
Venue: arXiv cs.CY 2025
Year: 2025

Homeland security in the United States faces a daunting task due to the multiple threats and hazards that can occur. Natural disasters, human-caused incidents such as terrorist attacks, and technological failures can result in significant damage, fatalities, injuries, and economic losses. The increasing frequency and severity of disruptive events in the United States highlight the urgent need for effectively allocating resources in homeland security and emergency preparedness. This article presents an optimization-based decision support model to help homeland security policymakers identify and select projects that best mitigate the risk of threats and hazards while satisfying a budget constraint. The model incorporates multiple hazards, probabilistic risk assessments, and multidimensional consequences and integrates historical data and publicly available sources to evaluate and select the most effective risk mitigation projects and optimize resource allocation across various disaster scenarios. We apply this model to the state of Iowa, considering 16 hazards, six types of consequences, and 52 mitigation projects. Our results demonstrate how different budget levels influence project selection, emphasizing cost-effective solutions that maximize risk reduction. Sensitivity analysis examines the robustness of project selection under varying effectiveness assumptions and consequence estimations. The findings offer critical insights for policymakers in homeland security and emergency management and provide a basis for more efficient resource allocation and improved disaster resilience.
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability
Venue: arXiv cs.LG 2025
Year: 2025

Out-of-Distribution (OOD) detection is a critical task in machine learning, particularly in safety-sensitive applications where model failures can have serious consequences. However, current OOD detection methods often suffer from restrictive distributional assumptions, limited scalability, and a lack of interpretability. To address these challenges, we propose STOOD-X, a two-stage methodology that combines a Statistical nonparametric Test for OOD Detection with eXplainability enhancements. In the first stage, STOOD-X uses feature-space distances and a Wilcoxon-Mann-Whitney test to identify OOD samples without assuming a specific feature distribution. In the second stage, it generates user-friendly, concept-based visual explanations that reveal the features driving each decision, aligning with the BLUE XAI paradigm. Through extensive experiments on benchmark datasets and multiple architectures, STOOD-X achieves competitive performance against state-of-the-art post hoc OOD detectors, particularly in high-dimensional and complex settings. In addition, its explainability framework enables human oversight, bias detection, and model debugging, fostering trust and collaboration between humans and AI systems. The STOOD-X methodology therefore offers a robust, explainable, and scalable solution for real-world OOD detection tasks.
EvoChain: A Framework for Tracking and Visualizing Smart Contract Evolution
Venue: arXiv cs.SE 2025
Year: 2025

Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart contract evolution. Building upon data from our previous empirical study, EvoChain models contract relationships using a Neo4j graph database and provides an interactive web interface for exploration. The framework consists of a data layer, an API layer, and a user interface layer. EvoChain allows stakeholders to analyze contract histories, upgrade paths, and associated vulnerabilities by leveraging these components. Our dataset encompasses approximately 1.3 million upgradeable proxies and nearly 15,000 historical versions, enhancing transparency and trust in blockchain ecosystems by providing an accessible platform for understanding smart contract evolution.
Web3DB: Web 3.0 RDBMS for Individual Data Ownership
Venue: arXiv cs.DB 2025
Year: 2025

This paper introduces Web3DB, a decentralized relational database management system (RDBMS) designed to align with the principles of Web 3.0, addressing critical shortcomings of traditional centralized DBMS, such as data privacy, security vulnerabilities, and single points of failure. Several similar systems have been proposed, but they are not compatible with the legacy systems based on RDBMS. Motivated by the necessity for enhanced data sovereignty and the decentralization of data control, Web3DB leverages blockchain technology for fine-grained access control and utilizes decentralized data storage. This system leverages a novel, modular architecture that contributes to enhanced flexibility, scalability, and user-centric functionality. Central to the Web3DB innovation is its decentralized query execution, which uses cryptographic sortition and blockchain verification to ensure secure and fair query processing across network nodes. The motivation for integrating relational databases within decentralized DBMS primarily stems from the need to combine the robustness and ease of use of relational database structures with the benefits of decentralization. This paper outlines the architecture of Web3DB, its practical implementation, and the system's ability to support SQL-like operations on relational data, manage multi-tenancy, and facilitate open data sharing, setting new standards for decentralized databases in the Web 3.0 era.
Computing High-dimensional Confidence Sets for Arbitrary Distributions
Venue: arXiv cs.DS 2025
Year: 2025

We study the problem of learning a high-density region of an arbitrary distribution over $\mathbb{R}^d$. Given a target coverage parameter $δ$, and sample access to an arbitrary distribution $D$, we want to output a confidence set $S \subset \mathbb{R}^d$ such that $S$ achieves $δ$ coverage of $D$, i.e., $\mathbb{P}_{y \sim D} \left[ y \in S \right] \ge δ$, and the volume of $S$ is as small as possible. This is a central problem in high-dimensional statistics with applications in finding confidence sets, uncertainty quantification, and support estimation. In the most general setting, this problem is statistically intractable, so we restrict our attention to competing with sets from a concept class $C$ with bounded VC-dimension. An algorithm is competitive with class $C$ if, given samples from an arbitrary distribution $D$, it outputs in polynomial time a set that achieves $δ$ coverage of $D$, and whose volume is competitive with the smallest set in $C$ with the required coverage $δ$. This problem is computationally challenging even in the basic setting when $C$ is the set of all Euclidean balls. Existing algorithms based on coresets find in polynomial time a ball whose volume is $\exp(\tilde{O}( d/ \log d))$-factor competitive with the volume of the best ball. Our main result is an algorithm that finds a confidence set whose volume is $\exp(\tilde{O}(d^{1/2}))$ factor competitive with the optimal ball having the desired coverage. The algorithm is improper (it outputs an ellipsoid). Combined with our computational intractability result for proper learning balls within an $\exp(\tilde{O}(d^{1-o(1)}))$ approximation factor in volume, our results provide an interesting separation between proper and (improper) learning of confidence sets.
Curbing the Ramifications of Authorship Abuse in Science
Venue: arXiv cs.DL 2025
Year: 2025

Research performance is often measured using bibliometric indicators, such as publication count, total citations, and $h$-index. These metrics influence career advancements, salary adjustments, administrative opportunities, funding prospects, and professional recognition. However, the reliance on these metrics has also made them targets for manipulation, misuse, and abuse. One primary ethical concern is authorship abuse, which includes paid, ornamental, exploitative, cartel, and colonial authorship. These practices are prevalent because they artificially enhance multiple bibliometric indicators all at once. Our study confirms a significant rise in the mean and median number of authors per publication across multiple disciplines over the last 34 years. While it is important to identify the cases of authorship abuse, a thorough investigation of every paper proves impractical. In this study, we propose a credit allocation scheme based on the reciprocals of the Fibonacci numbers, designed to adjust credit for individual contributions while systematically reducing credit for potential authorship abuse. The proposed scheme aligns with rigorous authorship guidelines from scientific associations, which mandate significant contributions across most phases of a study, while accommodating more lenient guidelines from scientific publishers, which recognize authorship for minimal contributions. We recalibrate the traditional bibliometric indicators to emphasize author contribution rather than participation in publications. Additionally, we propose a new indicator, $T^{\prime}$-index, to assess researchers' leading and contributing roles in their publications. Our proposed credit allocation scheme mitigates the effects of authorship abuse and promotes a more ethical scientific ecosystem.