OpenRaiser/Intern-Atlas huggingface dataset:
Co-Change Graph Entropy: A New Process Metric for Defect Prediction
Venue: arXiv cs.SE 2025
Year: 2025

Process metrics, valued for their language independence and ease of collection, have been shown to outperform product metrics in defect prediction. Among these, change entropy (Hassan, 2009) is widely used at the file level and has proven highly effective. Additionally, past research suggests that co-change patterns provide valuable insights into software quality. Building on these findings, we introduce Co-Change Graph Entropy, a novel metric that models co-changes as a graph to quantify co-change scattering. Experiments on eight Apache projects reveal a significant correlation between co-change entropy and defect counts at the file level, with a Pearson correlation coefficient of up to 0.54. In filelevel defect classification, replacing change entropy with co-change entropy improves AUROC in 72.5% of cases and MCC in 62.5% across 40 experimental settings (five machine learning classifiers and eight projects), though these improvements are not statistically significant. However, when co-change entropy is combined with change entropy, AUROC improves in 82.5% of cases and MCC in 65%, with statistically significant gains confirmed via the Friedman test followed by the post-hoc Nemenyi test. These results indicate that co-change entropy complements change entropy, significantly enhancing defect classification performance and underscoring its practical importance in defect prediction.
Intelligent Attacks and Defense Methods in Federated Learning-enabled Energy-Efficient Wireless Networks
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning (FL) is a promising technique for learning-based functions in wireless networks, thanks to its distributed implementation capability. On the other hand, distributed learning may increase the risk of exposure to malicious attacks where attacks on a local model may spread to other models by parameter exchange. Meanwhile, such attacks can be hard to detect due to the dynamic wireless environment, especially considering local models can be heterogeneous with non-independent and identically distributed (non-IID) data. Therefore, it is critical to evaluate the effect of malicious attacks and develop advanced defense techniques for FL-enabled wireless networks. In this work, we introduce a federated deep reinforcement learning-based cell sleep control scenario that enhances the energy efficiency of the network. We propose multiple intelligent attacks targeting the learning-based approach and we propose defense methods to mitigate such attacks. In particular, we have designed two attack models, generative adversarial network (GAN)-enhanced model poisoning attack and regularization-based model poisoning attack. As a counteraction, we have proposed two defense schemes, autoencoder-based defense, and knowledge distillation (KD)-enabled defense. The autoencoder-based defense method leverages an autoencoder to identify the malicious participants and only aggregate the parameters of benign local models during the global aggregation, while KD-based defense protects the model from attacks by controlling the knowledge transferred between the global model and local models.
Optimal tables for asymmetric numeral systems
Venue: arXiv cs.IT 2025
Year: 2025

We present several algorithms to generate tables for asymmetric numeral systems and prove that they are optimal in terms of discrepancy. In turn, this gives rise to the strongest proven bound on entropy loss. We further give improved theoretical bounds for the entropy loss in tabled asymmetric numeral systems and a brief empirical evaluation of the stream variant.
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
Venue: arXiv cs.LG 2025
Year: 2025

Generating synthetic tabular data can be challenging, however evaluation of their quality is just as challenging, if not more. This systematic review sheds light on the critical importance of rigorous evaluation of synthetic health data to ensure reliability, relevance, and their appropriate use. Based on screening of 1766 papers and a detailed review of 101 papers we identified key challenges, including lack of consensus on evaluation methods, improper use of evaluation metrics, limited input from domain experts, inadequate reporting of dataset characteristics, and limited reproducibility of results. In response, we provide several guidelines on the generation and evaluation of synthetic data, to allow the community to unlock and fully harness the transformative potential of synthetic data and accelerate innovation.
Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation
Venue: arXiv cs.CR 2025
Year: 2025

Distributed Denial of Service (DDoS) attacks represent a persistent and evolving threat to modern networked systems, capable of causing large-scale service disruptions. The complexity of such attacks, often hidden within high-dimensional and redundant network traffic data, necessitates robust and intelligent feature selection techniques for effective detection. Traditional methods such as filter-based, wrapper-based, and embedded approaches, each offer strengths but struggle with scalability or adaptability in complex attack environments. In this study, we explore these existing techniques through a detailed comparative analysis and highlight their limitations when applied to large-scale DDoS detection tasks. Building upon these insights, we introduce a novel Generative Adversarial Network-based Feature Selection (GANFS) method that leverages adversarial learning dynamics to identify the most informative features. By training a GAN exclusively on attack traffic and employing a perturbation-based sensitivity analysis on the Discriminator, GANFS effectively ranks feature importance without relying on full supervision. Experimental evaluations using the CIC-DDoS2019 dataset demonstrate that GANFS not only improves the accuracy of downstream classifiers but also enhances computational efficiency by significantly reducing feature dimensionality. These results point to the potential of integrating generative learning models into cybersecurity pipelines to build more adaptive and scalable detection systems.
Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway
Venue: arXiv cs.CR 2025
Year: 2025

The rapid expansion of Internet of Things (IoT) devices, particularly in smart home environments, has introduced considerable security and privacy concerns due to their persistent connectivity and interaction with cloud services. Despite advancements in IoT security, effective privacy measures remain uncovered, with existing solutions often relying on cloud-based threat detection that exposes sensitive data or outdated allow-lists that inadequately restrict non-essential network traffic. This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic (i.e., not influencing the device operations) by analyzing network behavior at the edge, leveraging Machine Learning to classify network destinations. Our approach includes building a labeled dataset based on IoT device behavior and employing a feature-extraction pipeline to enable a binary classification of essential vs. non-essential network destinations. We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic, including previously unseen destinations, without relying on traditional allow-lists. We implement our solution on a home access point, showing the framework has strong potential for scalable deployment, supporting near-real-time traffic classification in large-scale IoT environments with hundreds of devices. This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging
Venue: arXiv cs.LG 2025
Year: 2025

Checkpoint merging is a technique for combining multiple model snapshots into a single superior model, potentially reducing training time for large language models. This paper explores checkpoint merging in the context of parameter-efficient fine-tuning (PEFT), where only small adapter modules (e.g. LoRA) are trained. We propose Metrics-Weighted Averaging (MWA), a simple yet effective method to merge model checkpoints by weighting their parameters according to performance metrics. In particular, we investigate weighting by training loss and by training steps, under the intuition that lower-loss or later-step checkpoints are more valuable. We introduce a formula with a penalty factor to adjust weight distribution, requiring only one hyperparameter regardless of the number of checkpoints. Experiments on three fine-tuning tasks (mathematical reasoning, preference alignment, and general instruction tuning) show that MWA consistently produces merged models that outperform the naive uniform average of checkpoints. Notably, loss-weighted merging often yields the best results, delivering up to 5% higher task accuracy than the baseline uniform merge and even surpassing the final individual checkpoint's performance. These findings validate checkpoint merging for PEFT and demonstrate that a metric-driven weighting heuristic can efficiently boost model performance with minimal computational overhead.
Training Large Language Models to Reason via EM Policy Gradient
Venue: arXiv cs.LG 2025
Year: 2025

Recently, foundation models such as OpenAI's O1 and O3, along with DeepSeek's R1, have demonstrated strong reasoning capacities and problem-solving skills acquired through large-scale reinforcement learning (RL), with wide applications in mathematics, coding, science, intelligent agents, and virtual assistants. In this work, we introduce an off-policy reinforcement learning algorithm, EM Policy Gradient, aimed at enhancing LLM reasoning by optimizing expected return over reasoning trajectories. We frame the reasoning task as an Expectation-Maximization (EM) optimization problem, alternating between sampling diverse rationale trajectories and performing reward-guided fine-tuning. Unlike PPO and GRPO, which rely on complex importance weights and heuristic clipping, our method provides a simpler, more principled off-policy policy gradient approach, eliminating these complexities while maintaining strong performance. We evaluate the effectiveness of EM Policy Gradient on the GSM8K and MATH (HARD) datasets, where it achieves performance comparable to or slightly surpassing the state-of-the-art GRPO, while offering additional advantages in scalability, simplicity, and reasoning conciseness. Moreover, models fine-tuned with our method exhibit cognitive behaviors, such as sub-problem decomposition, self-verification, and backtracking, highlighting its potential to enhance both the interpretability and robustness of LLM reasoning.
The Philosophic Turn for AI Agents: Replacing centralized digital rhetoric with decentralized truth-seeking
Venue: arXiv cs.CY 2025
Year: 2025

In the face of rapidly advancing AI technology, individuals will increasingly rely on AI agents to navigate life's growing complexities, raising critical concerns about maintaining both human agency and autonomy. This paper addresses a fundamental dilemma posed by AI decision-support systems: the risk of either becoming overwhelmed by complex decisions, thus losing agency, or having autonomy compromised by externally controlled choice architectures reminiscent of ``nudging'' practices. While the ``nudge'' framework, based on the use of choice-framing to guide individuals toward presumed beneficial outcomes, initially appeared to preserve liberty, at AI-driven scale, it threatens to erode autonomy. To counteract this risk, the paper proposes a philosophic turn in AI design. AI should be constructed to facilitate decentralized truth-seeking and open-ended inquiry, mirroring the Socratic method of philosophical dialogue. By promoting individual and collective adaptive learning, such AI systems would empower users to maintain control over their judgments, augmenting their agency without undermining autonomy. The paper concludes by outlining essential features for autonomy-preserving AI systems, sketching a path toward AI systems that enhance human judgment rather than undermine it.
Span-Level Hallucination Detection for LLM-Generated Answers
Venue: arXiv cs.CL 2025
Year: 2025

Detecting spans of hallucination in LLM-generated answers is crucial for improving factual consistency. This paper presents a span-level hallucination detection framework for the SemEval-2025 Shared Task, focusing on English and Arabic texts. Our approach integrates Semantic Role Labeling (SRL) to decompose the answer into atomic roles, which are then compared with a retrieved reference context obtained via question-based LLM prompting. Using a DeBERTa-based textual entailment model, we evaluate each role semantic alignment with the retrieved context. The entailment scores are further refined through token-level confidence measures derived from output logits, and the combined scores are used to detect hallucinated spans. Experiments on the Mu-SHROOM dataset demonstrate competitive performance. Additionally, hallucinated spans have been verified through fact-checking by prompting GPT-4 and LLaMA. Our findings contribute to improving hallucination detection in LLM-generated responses.
Performance Analysis and Experimental Validation of UAV Corridor-Assisted Networks
Venue: arXiv cs.IT 2025
Year: 2025

Unmanned aerial vehicle (UAV) corridor-assisted communication networks are expected to expand significantly in the upcoming years driven by several technological, regulatory, and societal trends. In this new type of networks, accurate and realistic channel models are essential for designing reliable, efficient, and secure communication systems. In this paper, an analytical framework is presented that is based on one-dimensional (1D) finite point processes, namely the binomial point process (BPP) and the finite homogeneous Poisson point process (HPPP), to model the spatial locations of UAV-Base Stations (UAV-BSs). To this end, the shadowing conditions experienced in the UAV-BS-to-ground users links are accurately considered in a realistic maximum power-based user association policy. Subsequently, coverage probability analysis under the two spatial models is conducted, and exact-form expressions are derived. In an attempt to reduce the analytical complexity of the derived expressions, a dominant interferer-based approach is also investigated. Finally, the main outcomes of this paper are extensively validated by empirical data collected in an air-to-ground measurement campaign. To the best of the authors' knowledge, this is the first work to experimentally verify a generic spatial model by jointly considering the random spatial and shadowing characteristics of a UAV-assisted air-to-ground network.
Transformational Creativity in Science: A Graphical Theory
Venue: arXiv cs.AI 2025
Year: 2025

Creative processes are typically divided into three types: combinatorial, exploratory, and transformational. Here, we provide a graphical theory of transformational scientific creativity, synthesizing Boden's insight that transformational creativity arises from changes in the "enabling constraints" of a conceptual space and Kuhn's structure of scientific revolutions as resulting from paradigm shifts. We prove that modifications made to axioms of our graphical model have the most transformative potential and then illustrate how several historical instances of transformational creativity can be captured by our framework.
Technical Challenges in Maintaining Tax Prep Software with Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

As the US tax law evolves to adapt to ever-changing politico-economic realities, tax preparation software plays a significant role in helping taxpayers navigate these complexities. The dynamic nature of tax regulations poses a significant challenge to accurately and timely maintaining tax software artifacts. The state-of-the-art in maintaining tax prep software is time-consuming and error-prone as it involves manual code analysis combined with an expert interpretation of tax law amendments. We posit that the rigor and formality of tax amendment language, as expressed in IRS publications, makes it amenable to automatic translation to executable specifications (code). Our research efforts focus on identifying, understanding, and tackling technical challenges in leveraging Large Language Models (LLMs), such as ChatGPT and Llama, to faithfully extract code differentials from IRS publications and automatically integrate them with the prior version of the code to automate tax prep software maintenance.
Codetations: Intelligent, Persistent Notes and UIs for Programs and Other Documents
Venue: arXiv cs.SE 2025
Year: 2025

Software developers maintain extensive mental models of code they produce and its context, often relying on memory to retrieve or reconstruct design decisions, edge cases, and debugging experiences. These missing links and data obstruct both developers and, more recently, large language models (LLMs) working with unfamiliar code. We present Codetations, a system that helps developers contextualize documents with rich notes and tools. Unlike previous approaches, notes in Codetations stay outside the document to prevent code clutter, attaching to spans in the document using a hybrid edit-tracking/LLM-based method. Their content is dynamic, interactive, and synchronized with code changes. A worked example shows that relevant notes with interactively-collected data improve LLM performance during code repair. In our user evaluation, developers praised these properties and saw significant potential in annotation types that we generated with an LLM in just a few minutes.
An Interactive Debugger for Rust Trait Errors
Venue: arXiv cs.PL 2025
Year: 2025

Compiler diagnostics for type inference failures are notoriously bad, and type classes only make the problem worse. By introducing a complex search process during inference, type classes can lead to wholly inscrutable or useless errors. We describe a system, Argus, for interactively visualizing type class inferences to help programmers debug inference failures, applied specifically to Rust's trait system. The core insight of Argus is to avoid the traditional model of compiler diagnostics as one-size-fits-all, instead providing the programmer with different views on the search tree corresponding to different debugging goals. Argus carefully uses defaults to improve debugging productivity, including interface design (e.g., not showing full paths of types by default) and heuristics (e.g., sorting obligations based on the expected complexity of fixing them). We evaluated Argus in a user study where $N = 25$ participants debugged type inference failures in realistic Rust programs, finding that participants using Argus correctly localized $2.2\times$ as many faults and localized $3.3\times$ faster compared to not using Argus.
Spatial Speech Translation: Translating Across Space With Binaural Hearables
Venue: arXiv cs.CL 2025
Year: 2025

Imagine being in a crowded space where people speak a different language and having hearables that transform the auditory space into your native language, while preserving the spatial cues for all speakers. We introduce spatial speech translation, a novel concept for hearables that translate speakers in the wearer's environment, while maintaining the direction and unique voice characteristics of each speaker in the binaural output. To achieve this, we tackle several technical challenges spanning blind source separation, localization, real-time expressive translation, and binaural rendering to preserve the speaker directions in the translated audio, while achieving real-time inference on the Apple M2 silicon. Our proof-of-concept evaluation with a prototype binaural headset shows that, unlike existing models, which fail in the presence of interference, we achieve a BLEU score of up to 22.01 when translating between languages, despite strong interference from other speakers in the environment. User studies further confirm the system's effectiveness in spatially rendering the translated speech in previously unseen real-world reverberant environments. Taking a step back, this work marks the first step towards integrating spatial perception into speech translation.
Multimodal graph representation learning for website generation based on visual sketch
Venue: arXiv cs.LG 2025
Year: 2025

The Design2Code problem, which involves converting digital designs into functional source code, is a significant challenge in software development due to its complexity and time-consuming nature. Traditional approaches often struggle with accurately interpreting the intricate visual details and structural relationships inherent in webpage designs, leading to limitations in automation and efficiency. In this paper, we propose a novel method that leverages multimodal graph representation learning to address these challenges. By integrating both visual and structural information from design sketches, our approach enhances the accuracy and efficiency of code generation, particularly in producing semantically correct and structurally sound HTML code. We present a comprehensive evaluation of our method, demonstrating significant improvements in both accuracy and efficiency compared to existing techniques. Extensive evaluation demonstrates significant improvements of multimodal graph learning over existing techniques, highlighting the potential of our method to revolutionize design-to-code automation. Code available at https://github.com/HySonLab/Design2Code
Covert Communication Over a Quantum MAC with a Helper
Venue: arXiv cs.IT 2025
Year: 2025

We study covert classical communication over a quantum multiple-access channel (MAC) with a helper. Specifically, we consider three transmitters, where one transmitter helps the other two transmitters communicate covertly with a receiver. We demonstrate the feasibility of achieving a positive covert rate over this channel and establish an achievable rate region. Our result recovers as a special case known results for classical communication over classical MACs with a degraded message set, classical communication over quantum MACs, and classical communication over MACs with a helper. To the best of our knowledge, our result is the first to achieve covert communication with positive rates over both classical and quantum MACs.
Minimum Cost Nowhere-zero Flows and Cut-balanced Orientations
Venue: arXiv cs.DS 2025
Year: 2025

Flows and colorings are disparate concepts in graph algorithms -- the former is tractable while the latter is intractable. Tutte introduced the concept of nowhere-zero flows to unify these two concepts. Jaeger showed that nowhere-zero flows are equivalent to cut-balanced orientations. Motivated by connections between nowhere-zero flows, cut-balanced orientations, Nash-Williams' well-balanced orientations, and postman problems, we study optimization versions of nowhere-zero flows and cut-balanced orientations. Given a bidirected graph with asymmetric costs on two orientations of each edge, we study the min cost nowhere-zero $k$-flow problem and min cost $k$-cut-balanced orientation problem. We show that both problems are NP-hard to approximate within any finite factor. Given the strong inapproximability result, we design bicriteria approximations for both problems: we obtain a $(6,6)$-approximation to the min cost nowhere-zero $k$-flow and a $(k,6)$-approximation to the min cost $k$-cut-balanced orientation. For the case of symmetric costs (where the costs of both orientations are the same for every edge), we show that the nowhere-zero $k$-flow problem remains NP-hard and admits a $3$-approximation.