OpenRaiser/Intern-Atlas huggingface dataset:
Enabling Continuous 5G Connectivity in Aircraft through Low Earth Orbit Satellites
Venue: arXiv cs.NI 2025
Year: 2025

As air travel demand increases, uninterrupted high-speed internet access becomes essential. However, current satellite-based systems face latency and connectivity challenges. While prior research has focused on terrestrial 5G and geostationary satellites, there is a gap in optimizing Low Earth Orbit (LEO)-based 5G systems for aircraft. This study evaluates the feasibility of deployment strategies and improving signal quality with LEO satellites for seamless in-flight 5G connectivity. Using Matlab and Simulink, we model satellite trajectories, aircraft movement, and handover mechanisms, complemented by ray-tracing techniques for in-cabin signal analysis. Results show that proposed LEO satellite configurations enhance coverage and reduce latency, with sequential handovers minimizing service interruptions. These findings contribute to advancing in-flight 5G networks, improving passenger experience, and supporting real-time global connectivity solutions.
ECDSA Cracking Methods
Venue: arXiv cs.CR 2025
Year: 2025

The ECDSA (Elliptic Curve Digital Signature Algorithm) is used in many blockchain networks for digital signatures. This includes the Bitcoin and the Ethereum blockchains. While it has good performance levels and as strong current security, it should be handled with care. This care typically relates to the usage of the nonce value which is used to create the signature. This paper outlines the methods that can be used to break ECDSA signatures, including revealed nonces, weak nonce choice, nonce reuse, two keys and shared nonces, and fault attack.
Conthereum: Concurrent Ethereum Optimized Transaction Scheduling for Multi-Core Execution
Venue: arXiv cs.CR 2025
Year: 2025

Conthereum is a concurrent Ethereum solution for intra-block parallel transaction execution, enabling validators to utilize multi-core infrastructure and transform the sequential execution model of Ethereum into a parallel one. This shift significantly increases throughput and transactions per second (TPS), while ensuring conflict-free execution in both proposer and attestor modes and preserving execution order consistency in the attestor. At the heart of Conthereum is a novel, lightweight, high-performance scheduler inspired by the Flexible Job Shop Scheduling Problem (FJSS). We propose a custom greedy heuristic algorithm, along with its efficient implementation, that solves this formulation effectively and decisively outperforms existing scheduling methods in finding suboptimal solutions that satisfy the constraints, achieve minimal makespan, and maximize speedup in parallel execution. Additionally, Conthereum includes an offline phase that equips its real-time scheduler with a conflict analysis repository obtained through static analysis of smart contracts, identifying potentially conflicting functions using a pessimistic approach. Building on this novel scheduler and extensive conflict data, Conthereum outperforms existing concurrent intra-block solutions. Empirical evaluations show near-linear throughput gains with increasing computational power on standard 8-core machines. Although scalability deviates from linear with higher core counts and increased transaction conflicts, Conthereum still significantly improves upon the current sequential execution model and outperforms existing concurrent solutions under a wide range of conditions.
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning
Venue: arXiv cs.CL 2025
Year: 2025

As Large Language Models (LLMs) are increasingly applied across various tasks, instruction tuning has emerged as a critical method for enhancing model performance. However, current data management strategies face substantial challenges in generating diverse and comprehensive data, restricting further improvements in model performance. To address this gap, we propose MDIT, a novel model-free data interpolation method for diverse instruction tuning, which generates varied and high-quality instruction data by performing task interpolation. Moreover, it contains diversity-based clustering strategies to ensure the diversity of the training data. Extensive experiments show that our method achieves superior performance in multiple benchmark tasks. The LLMs finetuned with MDIT show significant improvements in numerous tasks such as general question answering, math reasoning, and code generation. MDIT offers an efficient and automatic data synthetic method, generating diverse instruction data without depending on external resources while expanding the application potential of LLMs in complex environments.
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions, techniques such as transfer learning and multitask learning have been used. The performance of multitask learning models depends on the strength of the underlying correlations between tasks and the completeness of the data set. Standard multitask models tend to underperform when trained on sparse data sets with weakly correlated properties. To address this gap, we fuse deep-learned embeddings generated by independent pretrained single-task models, resulting in a multitask model that inherits rich, property-specific representations. By reusing (rather than retraining) these embeddings, the resulting fused model outperforms standard multitask models and can be extended with fewer trainable parameters. We demonstrate this technique on a widely used benchmark data set of quantum chemistry data for small molecules as well as a newly compiled sparse data set of experimental data collected from literature and our own quantum chemistry and thermochemical calculations.
PAYADOR: A Minimalist Approach to Grounding Language Models on Structured Data for Interactive Storytelling and Role-playing Games
Venue: arXiv cs.CL 2025
Year: 2025

Every time an Interactive Storytelling (IS) system gets a player input, it is facing the world-update problem. Classical approaches to this problem consist in mapping that input to known preprogrammed actions, what can severely constrain the free will of the player. When the expected experience has a strong focus on improvisation, like in Role-playing Games (RPGs), this problem is critical. In this paper we present PAYADOR, a different approach that focuses on predicting the outcomes of the actions instead of representing the actions themselves. To implement this approach, we ground a Large Language Model to a minimal representation of the fictional world, obtaining promising results. We make this contribution open-source, so it can be adapted and used for other related research on unleashing the co-creativity power of RPGs.
An Integrated Visual Servoing Framework for Precise Robotic Pruning Operations in Modern Commercial Orchard
Venue: arXiv cs.RO 2025
Year: 2025

This study presents a vision-guided robotic control system for automated fruit tree pruning applications. Traditional pruning practices are labor-intensive and limit agricultural efficiency and scalability, highlighting the need for advanced automation. A key challenge is the precise, robust positioning of the cutting tool in complex orchard environments, where dense branches and occlusions make target access difficult. To address this, an Intel RealSense D435 camera is mounted on the flange of a UR5e robotic arm and CoTracker3, a transformer-based point tracker, is utilized for visual servoing control that centers tracked points in the camera view. The system integrates proportional control with iterative inverse kinematics to achieve precise end-effector positioning. The system was validated in Gazebo simulation, achieving a 77.77% success rate within 5mm positional tolerance and 100% success rate within 10mm tolerance, with a mean end-effector error of 4.28 +/- 1.36 mm. The vision controller demonstrated robust performance across diverse target positions within the pixel workspace. The results validate the effectiveness of integrating vision-based tracking with kinematic control for precision agricultural tasks. Future work will focus on real-world implementation and the integration of force sensing for actual cutting operations.
Dependency Update Adoption Patterns in the Maven Software Ecosystem
Venue: arXiv cs.SE 2025
Year: 2025

Regular dependency updates protect dependent software components from upstream bugs, security vulnerabilities, and poor code quality. Measures of dependency updates across software ecosystems involve two key dimensions: the time span during which a release is being newly adopted (adoption lifespan) and the extent of adoption across the ecosystem (adoption reach). We examine correlations between adoption patterns in the Maven software ecosystem and two factors: the magnitude of code modifications (extent of modifications affecting the meaning or behavior of the code, henceforth called ``semantic change") in an upstream dependency and the relative maintenance rate of upstream packages. Using the Goblin Weaver framework, we find adoption latency in the Maven ecosystem follows a log-normal distribution while adoption reach exhibits an exponential decay distribution.
Mindsets and Management: AI and Gender (In)Equitable Access to Finance
Venue: arXiv cs.CY 2025
Year: 2025

A growing trend in financial technology (fintech) is the use of mobile phone data and machine learning (ML) to provide credit scores- and subsequently, opportunities to access loans- to groups left out of traditional banking. This paper draws on interview data with leaders, investors, and data scientists at fintech companies developing ML-based alternative lending apps in low- and middle-income countries to explore financial inclusion and gender implications. More specifically, it examines how the underlying logics, design choices, and management decisions of ML-based alternative lending tools by fintechs embed or challenge gender biases, and consequently influence gender equity in access to finance. Findings reveal developers follow 'gender blind' approaches, grounded in beliefs that ML is objective and data reflects the truth. This leads to a lack of grappling with the ways data, features for creditworthiness, and access to apps are gendered. Overall, tools increase access to finance, but not gender equitably: Interviewees report less women access loans and receive lower amounts than men, despite being better repayers. Fintechs identify demand- and supply-side reasons for gender differences, but frame them as outside their responsibility. However, that women are observed as better repayers reveals a market inefficiency and potential discriminatory effect, further linked to profit optimization objectives. This research introduces the concept of encoded gender norms, whereby without explicit attention to the gendered nature of data and algorithmic design, AI tools reproduce existing inequalities. In doing so, they reinforce gender norms as self-fulfilling prophecies. The idea that AI is inherently objective and, when left alone, 'fair', is seductive and misleading. In reality, algorithms reflect the perspectives, priorities, and values of the people and institutions that design them.
Undecidability of the Emptiness Problem for Weak Models of Distributed Computing
Venue: arXiv cs.FL 2025
Year: 2025

Esparza and Reiter have recently conducted a systematic comparative study of weak asynchronous models of distributed computing, in which a network of identical finite-state machines acts cooperatively to decide properties of the network's graph. They introduced a distributed automata framework encompassing many different models, and proved that w.r.t. their expressive power (the graph properties they can decide) distributed automata collapse into seven equivalence classes. In this contribution, we turn our attention to the formal verification problem: Given a distributed automaton, does it decide a given graph property? We consider a fundamental instance of this question - the emptiness problem: Given a distributed automaton, does it accept any graph at all? Our main result is negative: the emptiness problem is undecidable for six of the seven equivalence classes, and trivially decidable for the remaining class.
Code Generation with Small Language Models: A Codeforces-Based Study
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) demonstrate capabilities in code generation, potentially boosting developer productivity. However, their adoption remains limited by high computational costs, among other factors. Small Language Models (SLMs) present a lightweight alternative. While LLMs have been evaluated on competitive programming tasks, prior work often emphasizes metrics like Elo or pass rates, neglecting failure analysis. The potential of SLMs in this space remains underexplored. In this study, we benchmark three open SLMs - Llama-3.2-3B, Gemma-3-12B, and Phi-4-14B - across 280 Codeforces problems spanning Elo ratings from 800 to 2100 and covering 36 distinct topics. All models were tasked with generating Python solutions. Phi-4-14B achieved the best SLM performance with a pass@3 of 63.6%, nearing o3-mini-high (86.8%). Combining Python and C++ outputs increased Phi-4-14B's pass@6 to 73.6%. A qualitative analysis revealed some failures stemmed from minor implementation issues rather than reasoning flaws.
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy
Venue: arXiv cs.CR 2025
Year: 2025

The shuffle model of DP (Differential Privacy) provides high utility by introducing a shuffler that randomly shuffles noisy data sent from users. However, recent studies show that existing shuffle protocols suffer from the following two major drawbacks. First, they are vulnerable to local data poisoning attacks, which manipulate the statistics about input data by sending crafted data, especially when the privacy budget epsilon is small. Second, the actual value of epsilon is increased by collusion attacks by the data collector and users. In this paper, we address these two issues by thoroughly exploring the potential of the augmented shuffle model, which allows the shuffler to perform additional operations, such as random sampling and dummy data addition. Specifically, we propose a generalized framework for local-noise-free protocols in which users send (encrypted) input data to the shuffler without adding noise. We show that this generalized protocol provides DP and is robust to the above two attacks if a simpler mechanism that performs the same process on binary input data provides DP. Based on this framework, we propose three concrete protocols providing DP and robustness against the two attacks. Our first protocol generates the number of dummy values for each item from a binomial distribution and provides higher utility than several state-of-the-art existing shuffle protocols. Our second protocol significantly improves the utility of our first protocol by introducing a novel dummy-count distribution: asymmetric two-sided geometric distribution. Our third protocol is a special case of our second protocol and provides pure epsilon-DP. We show the effectiveness of our protocols through theoretical analysis and comprehensive experiments.
Incremental Planar Nearest Neighbor Queries with Optimal Query Time
Venue: arXiv cs.DS 2025
Year: 2025

In this paper we show that two-dimensional nearest neighbor queries can be answered in optimal $O(\log n)$ time while supporting insertions in $O(\log^{1+\varepsilon}n)$ time. No previous data structure was known that supports $O(\log n)$-time queries and polylog-time insertions. In order to achieve logarithmic queries our data structure uses a new technique related to fractional cascading that leverages the inherent geometry of this problem. Our method can be also used in other semi-dynamic scenarios.
View-Dependent Uncertainty Estimation of 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian Splatting (3DGS) has become increasingly popular in 3D scene reconstruction for its high visual accuracy. However, uncertainty estimation of 3DGS scenes remains underexplored and is crucial to downstream tasks such as asset extraction and scene completion. Since the appearance of 3D gaussians is view-dependent, the color of a gaussian can thus be certain from an angle and uncertain from another. We thus propose to model uncertainty in 3DGS as an additional view-dependent per-gaussian feature that can be modeled with spherical harmonics. This simple yet effective modeling is easily interpretable and can be integrated into the traditional 3DGS pipeline. It is also significantly faster than ensemble methods while maintaining high accuracy, as demonstrated in our experiments.
Talking Point based Ideological Discourse Analysis in News Events
Venue: arXiv cs.CL 2025
Year: 2025

Analyzing ideological discourse even in the age of LLMs remains a challenge, as these models often struggle to capture the key elements that shape real-world narratives. Specifically, LLMs fail to focus on characteristic elements driving dominant discourses and lack the ability to integrate contextual information required for understanding abstract ideological views. To address these limitations, we propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events. Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion. It then constructs a vocabulary of repeating themes - prominent talking points, that are used to generate ideology-specific viewpoints (or partisan perspectives). We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation. Additionally, we demonstrate straightforward applicability of our framework in creating event snapshots, a visual way of interpreting event discourse. We release resulting dataset and model to the community to support further research.
LLM-Enabled Data Transmission in End-to-End Semantic Communication
Venue: arXiv cs.NI 2025
Year: 2025

Emerging services such as augmented reality (AR) and virtual reality (VR) have increased the volume of data transmitted in wireless communication systems, revealing the limitations of traditional Shannon theory. To address these limitations, semantic communication has been proposed as a solution that prioritizes the meaning of messages over the exact transmission of bits. This paper explores semantic communication for text data transmission in end-to-end (E2E) systems through a novel approach called KG-LLM semantic communication, which integrates knowledge graph (KG) extraction and large language model (LLM) coding. In this method, the transmitter first utilizes a KG to extract key entities and relationships from sentences. The extracted information is then encoded using an LLM to obtain the semantic meaning. On the receiver side, messages are decoded using another LLM, while a bidirectional encoder representations from transformers (i.e., BERT) model further refines the reconstructed sentences for improved semantic similarity. The KG-LLM semantic communication method reduces the transmitted text data volume by 30% through KG-based compression and achieves 84\% semantic similarity between the original and received messages. This demonstrates the KG-LLM methods efficiency and robustness in semantic communication systems, outperforming the deep learning-based semantic communication model (DeepSC), which achieves only 63%.
Achilles Heel of Distributed Multi-Agent Systems
Venue: arXiv cs.MA 2025
Year: 2025

Multi-agent system (MAS) has demonstrated exceptional capabilities in addressing complex challenges, largely due to the integration of multiple large language models (LLMs). However, the heterogeneity of LLMs, the scalability of quantities of LLMs, and local computational constraints pose significant challenges to hosting these models locally. To address these issues, we propose a new framework termed Distributed Multi-Agent System (DMAS). In DMAS, heterogeneous third-party agents function as service providers managed remotely by a central MAS server and each agent offers its services through API interfaces. However, the distributed nature of DMAS introduces several concerns about trustworthiness. In this paper, we study the Achilles heel of distributed multi-agent systems, identifying four critical trustworthiness challenges: free riding, susceptibility to malicious attacks, communication inefficiencies, and system instability. Extensive experiments across seven frameworks and four datasets reveal significant vulnerabilities of the DMAS. These attack strategies can lead to a performance degradation of up to 80% and attain a 100% success rate in executing free riding and malicious attacks. We envision our work will serve as a useful red-teaming tool for evaluating future multi-agent systems and spark further research on trustworthiness challenges in distributed multi-agent systems.
Program Skeletons for Automated Program Translation
Venue: arXiv cs.PL 2025
Year: 2025

Translating software between programming languages is a challenging task, for which automated techniques have been elusive and hard to scale up to larger programs. A key difficulty in cross-language translation is that one has to re-express the intended behavior of the source program into idiomatic constructs of a different target language. This task needs abstracting away from the source language-specific details, while keeping the overall functionality the same. In this work, we propose a novel and systematic approach for making such translation amenable to automation based on a framework we call program skeletons. A program skeleton retains the high-level structure of the source program by abstracting away and effectively summarizing lower-level concrete code fragments, which can be mechanically translated to the target programming language. A skeleton, by design, permits many different ways of filling in the concrete implementation for fragments, which can work in conjunction with existing data-driven code synthesizers. Most importantly, skeletons can conceptually enable sound decomposition, i.e., if each individual fragment is correctly translated, taken together with the mechanically translated skeleton, the final translated program is deemed to be correct as a whole. We present a prototype system called Skel embodying the idea of skeleton-based translation from Python to JavaScript. Our results show promising scalability compared to prior works. For 9 real-world Python programs, some with more than about 1k lines of code, 95% of their code fragments can be automatically translated, while about 5% require manual effort. All the final translations are correct with respect to whole-program test suites.