OpenRaiser/Intern-Atlas huggingface dataset:
Physics-Based Decomposition of Reflectance and Shading using a Single Visible-Thermal Image Pair
Venue: arXiv cs.CV 2025
Year: 2025

Decomposing an image into its underlying photometric factors--surface reflectance and shading--is a long-standing challenge due to the lack of extensive ground-truth data for real-world scenes. We introduce a novel physics-based approach for intrinsic image decomposition using a pair of visible and thermal images. We leverage the principle that light not reflected from an opaque surface is absorbed and detected as heat by a thermal camera. This allows us to relate the ordinalities (or relative magnitudes) between visible and thermal image intensities to the ordinalities of shading and reflectance, which enables a dense self-supervision of an optimizing neural network to recover shading and reflectance. We perform quantitative evaluations with known reflectance and shading under natural and artificial lighting, and qualitative experiments across diverse scenes. The results demonstrate superior performance over both physics-based and recent learning-based methods, providing a path toward scalable real-world data curation with supervision.
Inpainting-Guided Policy Optimization for Diffusion Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Masked diffusion large language models (dLLMs) are emerging as promising alternatives to autoregressive LLMs, offering competitive performance while supporting unique generation capabilities such as inpainting. We explore how inpainting can inform RL algorithm design for dLLMs. Aligning LLMs with reinforcement learning faces an exploration challenge: sparse reward signals and sample waste when models fail to discover correct solutions. While this inefficiency affects LLMs broadly, dLLMs offer a distinctive opportunity--their inpainting ability can guide exploration. We introduce IGPO (Inpainting Guided Policy Optimization), an RL framework that strategically inserts partial ground-truth reasoning traces during online sampling. Unlike providing full solutions, inpainting steers exploration toward promising trajectory spaces while preserving self-generated reasoning, bridging supervised fine-tuning and reinforcement learning. We apply IGPO to group-based optimization methods such as GRPO, where exploration failures cause zero advantages and gradients. IGPO restores meaningful gradients while improving sample efficiency. We also propose supervised fine-tuning on synthetically rewritten concise traces that better align with dLLM generation patterns. With additional techniques including entropy-based filtering, our training recipe yields substantial gains across three mathematical benchmarks--GSM8K, Math500, and AMC--achieving new state-of-the-art results for full-attention masked dLLMs.
Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are becoming integral to modern software development workflows, assisting developers with code generation, API explanation, and iterative problem-solving through natural language conversations. Despite widespread adoption, there is limited understanding of how developers interact with LLMs in practice and how these conversational dynamics influence task outcomes, code quality, and software engineering workflows. To address this, we leverage CodeChat, a large dataset comprising 82,845 real-world developer-LLM conversations, containing 368,506 code snippets generated across over 20 programming languages, derived from the WildChat dataset. We find that LLM responses are substantially longer than developer prompts, with a median token-length ratio of 14:1. Multi-turn conversations account for 68% of the dataset and often evolve due to shifting requirements, incomplete prompts, or clarification requests. Topic analysis identifies web design (9.6% of conversations) and neural network training (8.7% of conversations) as the most frequent LLM-assisted tasks. Evaluation across five languages (i.e., Python, JavaScript, C++, Java, and C#) reveals prevalent and language-specific issues in LLM-generated code: generated Python and JavaScript code often include undefined variables (83.4% and 75.3% of code snippets, respectively); Java code lacks required comments (75.9%); C++ code frequently omits headers (41.1%) and C# code shows unresolved namespaces (49.2%). During a conversation, syntax and import errors persist across turns; however, documentation quality in Java improves by up to 14.7%, and import handling in Python improves by 3.7% over 5 turns. Prompts that point out mistakes in code generated in prior turns and explicitly request a fix are most effective for resolving errors.
TASC: Task-Aware Shared Control for Teleoperated Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

We present TASC, a Task-Aware Shared Control framework for teleoperated manipulation that infers task-level user intent and provides assistance throughout the task. To support everyday tasks without predefined knowledge, TASC constructs an open-vocabulary interaction graph from visual input to represent functional object relationships, and infers user intent accordingly. A shared control policy then provides rotation assistance during both grasping and object interaction, guided by spatial constraints predicted by a vision-language model. Our method addresses two key challenges in general-purpose, long-horizon shared control: (1) understanding and inferring task-level user intent, and (2) generalizing assistance across diverse objects and tasks. Experiments in both simulation and the real world demonstrate that TASC improves task efficiency and reduces user input effort compared to prior methods. To the best of our knowledge, this is the first shared control framework that supports everyday manipulation tasks with zero-shot generalization. The code that supports our experiments is publicly available at https://github.com/fitz0401/tasc.
Run-Time Monitoring of ERTMS/ETCS Control Flow by Process Mining
Venue: arXiv cs.LG 2025
Year: 2025

Ensuring the resilience of computer-based railways is increasingly crucial to account for uncertainties and changes due to the growing complexity and criticality of those systems. Although their software relies on strict verification and validation processes following well-established best-practices and certification standards, anomalies can still occur at run-time due to residual faults, system and environmental modifications that were unknown at design-time, or other emergent cyber-threat scenarios. This paper explores run-time control-flow anomaly detection using process mining to enhance the resilience of ERTMS/ETCS L2 (European Rail Traffic Management System / European Train Control System Level 2). Process mining allows learning the actual control flow of the system from its execution traces, thus enabling run-time monitoring through online conformance checking. In addition, anomaly localization is performed through unsupervised machine learning to link relevant deviations to critical system components. We test our approach on a reference ERTMS/ETCS L2 scenario, namely the RBC/RBC Handover, to show its capability to detect and localize anomalies with high accuracy, efficiency, and explainability.
AegisShield: Democratizing Cyber Threat Modeling with Generative AI
Venue: arXiv cs.CR 2025
Year: 2025

The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with limited resources. This paper develops and evaluates AegisShield, a generative AI enhanced threat modeling tool that implements STRIDE and MITRE ATT&CK to automate threat generation and provide systematic assessments. By integrating real time threat intelligence from the National Vulnerability Database and AlienVault Open Threat Exchange, AegisShield produces streamlined and accessible threat descriptions. Our assessment of 243 threats from 15 case studies and over 8000 AI generated threats shows that AegisShield reduces complexity (p less than 0.001), yields outputs semantically aligned with expert developed threats (p less than 0.05), and achieves an 85.4 percent success rate in mapping threats to MITRE ATT&CK techniques (p less than 0.001). Automating and standardizing threat modeling helps under resourced organizations address risk earlier and supports wider adoption of secure by design practices.
A Deep Learning Framework for Joint Channel Acquisition and Communication Optimization in Movable Antenna Systems
Venue: arXiv cs.IT 2025
Year: 2025

This paper presents an end-to-end deep learning framework in a movable antenna (MA)-enabled multiuser communication system. In contrast to the conventional works assuming perfect channel state information (CSI), we address the practical CSI acquisition issue through the design of pilot signals and quantized CSI feedback, and further incorporate the joint optimization of channel estimation, MA placement, and precoding design. The proposed mechanism enables the system to learn an optimized transmission strategy from imperfect channel data, overcoming the limitations of conventional methods that conduct channel estimation and antenna position optimization separately. To balance the performance and overhead, we further extend the proposed framework to optimize the antenna placement based on the statistical CSI. Simulation results demonstrate that the proposed approach consistently outperforms traditional benchmarks in terms of achievable sum-rate of users, especially under limited feedback and sparse channel environments. Notably, it achieves a performance comparable to the widely-adopted gradient-based methods with perfect CSI, while maintaining significantly lower CSI feedback overhead. These results highlight the effectiveness and adaptability of learning-based MA system design for future wireless systems.
Offline Contextual Bandit with Counterfactual Sample Identification
Venue: arXiv cs.LG 2025
Year: 2025

In production systems, contextual bandit approaches often rely on direct reward models that take both action and context as input. However, these models can suffer from confounding, making it difficult to isolate the effect of the action from that of the context. We present \emph{Counterfactual Sample Identification}, a new approach that re-frames the problem: rather than predicting reward, it learns to recognize which action led to a successful (binary) outcome by comparing it to a counterfactual action sampled from the logging policy under the same context. The method is theoretically grounded and consistently outperforms direct models in both synthetic experiments and real-world deployments.
Multimodal Deep Learning for ATCO Command Lifecycle Modeling and Workload Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Air traffic controllers (ATCOs) issue high-intensity voice commands in dense airspace, where accurate workload modeling is critical for safety and efficiency. This paper proposes a multimodal deep learning framework that integrates structured data, trajectory sequences, and image features to estimate two key parameters in the ATCO command lifecycle: the time offset between a command and the resulting aircraft maneuver, and the command duration. A high-quality dataset was constructed, with maneuver points detected using sliding window and histogram-based methods. A CNN-Transformer ensemble model was developed for accurate, generalizable, and interpretable predictions. By linking trajectories to voice commands, this work offers the first model of its kind to support intelligent command generation and provides practical value for workload assessment, staffing, and scheduling.
A Hybrid Encryption Framework Combining Classical, Post-Quantum, and QKD Methods
Venue: arXiv cs.CR 2025
Year: 2025

This paper introduces a hybrid encryption framework combining classical cryptography (EdDSA, ECDH), post-quantum cryptography (ML-DSA-6x5, ML-KEM-768), and Quantum Key Distribution (QKD) via Guardian to counter quantum computing threats. Our prototype implements this integration, using a key derivation function to generate secure symmetric and HMAC keys, and evaluates its performance across execution time and network metrics. The approach improves data protection by merging classical efficiency with PQC's quantum resilience and QKD's key security, offering a practical transition path for cryptographic systems. This research lays the foundation for future adoption of PQC in securing digital communication.
GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
Venue: arXiv cs.LG 2025
Year: 2025

Geodetic time series -- such as Global Navigation Satellite System (GNSS) positions, satellite altimetry-derived sea surface height (SSH), and tide gauge (TG) records -- is essential for monitoring surface deformation and sea level change. Accurate forecasts of these variables can enhance early warning systems and support hazard mitigation for earthquakes, landslides, coastal storm surge, and long-term sea level. However, the nonlinear, non-stationary, and incomplete nature of such variables presents significant challenges for classic models, which often fail to capture long-term dependencies and complex spatiotemporal dynamics. We introduce GTS Forecaster, an open-source Python package for geodetic time series forecasting. It integrates advanced deep learning models -- including kernel attention networks (KAN), graph neural network-based gated recurrent units (GNNGRU), and time-aware graph neural networks (TimeGNN) -- to effectively model nonlinear spatial-temporal patterns. The package also provides robust preprocessing tools, including outlier detection and a reinforcement learning-based gap-filling algorithm, the Kalman-TransFusion Interpolation Framework (KTIF). GTS Forecaster currently supports forecasting, visualization, and evaluation of GNSS, SSH, and TG datasets, and is adaptable to general time series applications. By combining cutting-edge models with an accessible interface, it facilitates the application of deep learning in geodetic forecasting tasks.
Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media
Venue: arXiv cs.CY 2025
Year: 2025

Clinical trials (CT) are essential for advancing medical research and treatment, yet efficiently recruiting eligible participants -- each of whom must meet complex eligibility criteria -- remains a significant challenge. Traditional recruitment approaches, such as advertisements or electronic health record screening within hospitals, are often time-consuming and geographically constrained. This work addresses the recruitment challenge by leveraging the vast amount of health-related information individuals share on social media platforms. With the emergence of powerful large language models (LLMs) capable of sophisticated text understanding, we pose the central research question: Can LLM-driven tools facilitate CT recruitment by identifying potential participants through their engagement on social media? To investigate this question, we introduce TRIALQA, a novel dataset comprising two social media collections from the subreddits on colon cancer and prostate cancer. Using eligibility criteria from public real-world CTs, experienced annotators are hired to annotate TRIALQA to indicate (1) whether a social media user meets a given eligibility criterion and (2) the user's stated reasons for interest in participating in CT. We benchmark seven widely used LLMs on these two prediction tasks, employing six distinct training and inference strategies. Our extensive experiments reveal that, while LLMs show considerable promise, they still face challenges in performing the complex, multi-hop reasoning needed to accurately assess eligibility criteria.
Machine Unlearning for Responsible and Adaptive AI in Education
Venue: arXiv cs.CY 2025
Year: 2025

Machine Unlearning (MU) has emerged as a promising approach to addressing persistent challenges in Machine Learning (ML) systems. By enabling the selective removal of learned data, MU introduces protective, corrective, and adaptive capabilities that are central to advancing Responsible and Adaptive AI. However, despite its growing prominence in other domains, MU remains underexplored within education, a sector uniquely characterized by sensitive learner data, dynamic environments, and the high-stakes implications of algorithmic decision-making. This paper examines the potential of MU as both a mechanism for operationalizing Responsible AI principles and a foundation for Adaptive AI in ML-driven educational systems. Drawing on a structured review of 42 peer-reviewed studies, the paper analyzes key MU mechanisms and technical variants, and how they contribute to the practical realization of Responsible and Adaptive AI. Four core intervention domains where MU demonstrates significant promise are identified: privacy protection, resilience to adversarial or corrupted data, fairness through bias mitigation, and adaptability to evolving contexts. Furthermore, MU interventions are mapped to the technical, ethical, and pedagogical challenges inherent in educational AI. This mapping illustrates the role of MU as a strategic mechanism for enhancing compliance, reinforcing ethical safeguards, and supporting adaptability by ensuring that models remain flexible, maintainable, and contextually relevant over time. As a conceptual contribution, the paper introduces MU4RAAI, a reference architecture integrating MU within Responsible and Adaptive AI frameworks for educational contexts. MU is thus positioned not merely as a data deletion process but as a transformative approach for ensuring that educational AI systems remain ethical, adaptive, and trustworthy.
SME-TEAM: Leveraging Trust and Ethics for Secure and Responsible Use of AI and LLMs in SMEs
Venue: arXiv cs.LG 2025
Year: 2025

Artificial Intelligence (AI) and Large Language Models (LLMs) are revolutionizing today's business practices; however, their adoption within small and medium-sized enterprises (SMEs) raises serious trust, ethical, and technical issues. In this perspective paper, we introduce a structured, multi-phased framework, "SME-TEAM" for the secure and responsible use of these technologies in SMEs. Based on a conceptual structure of four key pillars, i.e., Data, Algorithms, Human Oversight, and Model Architecture, SME-TEAM bridges theoretical ethical principles with operational practice, enhancing AI capabilities across a wide range of applications in SMEs. Ultimately, this paper provides a structured roadmap for the adoption of these emerging technologies, positioning trust and ethics as a driving force for resilience, competitiveness, and sustainable innovation within the area of business analytics and SMEs.
Faster Results from a Smarter Schedule: Reframing Collegiate Cross Country through Analysis of the National Running Club Database
Venue: arXiv cs.CY 2025
Year: 2025

Collegiate cross country teams often build their season schedules on intuition rather than evidence, partly because large-scale performance datasets are not publicly accessible. To address this limitation, we introduce the National Running Club Database (NRCD), the first openly available dataset to aggregate 23,725 race results from 7,594 collegiate club athletes across the 2023-2025 seasons. Unlike existing resources, NRCD includes detailed course metadata, allowing us to develop two standardized performance metrics: Converted Only (distance correction) and Standardized (distance, weather, and elevation adjusted). Using these standardized measures, we find that athletes with slower initial performances exhibit the greatest improvement within a season, and that race frequency is the strongest predictor of improvement. Using six machine learning models, random forest achieves the highest accuracy (r squared equals 0.92), revealing that athletes who race more frequently progress significantly faster than those who do not. At the team level, programs whose athletes race at least four times during the regular season have substantially higher odds of placing in the top 15 at nationals (chi-squared less than 0.01). These results challenge common coaching practices that favor minimal racing before championship meets. Our findings demonstrate that a data-informed scheduling strategy improves both individual development and team competitiveness. The NRCD provides a new foundation for evidence-based decision-making in collegiate cross country and opens opportunities for further research on standardized, longitudinal athlete performance modeling.
pySigLib -- Fast Signature-Based Computations on CPU and GPU
Venue: arXiv cs.LG 2025
Year: 2025

Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However, existing implementations do not scale to the dataset sizes and sequence lengths encountered in practice. We present pySigLib, a high-performance Python library offering optimised implementations of signatures and signature kernels on CPU and GPU, fully compatible with PyTorch's automatic differentiation. Beyond an efficient software stack for large-scale signature-based computation, we introduce a novel differentiation scheme for signature kernels that delivers accurate gradients at a fraction of the runtime of existing libraries.
SCOR: A Framework for Responsible AI Innovation in Digital Ecosystems
Venue: arXiv cs.CY 2025
Year: 2025

AI-driven digital ecosystems span diverse stakeholders including technology firms, regulators, accelerators and civil society, yet often lack cohesive ethical governance. This paper proposes a four-pillar framework (SCOR) to embed accountability, fairness, and inclusivity across such multi-actor networks. Leveraging a design science approach, we develop a Shared Ethical Charter(S), structured Co-Design and Stakeholder Engagement protocols(C), a system of Continuous Oversight and Learning(O), and Adaptive Regulatory Alignment strategies(R). Each component includes practical guidance, from lite modules for resource-constrained start-ups to in-depth auditing systems for larger consortia. Through illustrative vignettes in healthcare, finance, and smart city contexts, we demonstrate how the framework can harmonize organizational culture, leadership incentives, and cross-jurisdictional compliance. Our mixed-method KPI design further ensures that quantitative targets are complemented by qualitative assessments of user trust and cultural change. By uniting ethical principles with scalable operational structures, this paper offers a replicable pathway toward responsible AI innovation in complex digital ecosystems.
Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration
Venue: arXiv cs.LG 2025
Year: 2025

For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning. However, designing reward functions to elicit such behavior is challenging. In this paper, we study how self-supervised goal-reaching techniques can be leveraged to enable agents to cooperate. The key idea is that, rather than have agents maximize some scalar reward, agents aim to maximize the likelihood of visiting a certain goal. This problem setting enables human users to specify tasks via a single goal state rather than implementing a complex reward function. While the feedback signal is quite sparse, we will demonstrate that self-supervised goal-reaching techniques enable agents to learn from such feedback. On MARL benchmarks, our proposed method outperforms alternative approaches that have access to the same sparse reward signal as our method. While our method has no explicit mechanism for exploration, we observe that self-supervised multi-agent goal-reaching leads to emergent cooperation and exploration in settings where alternative approaches never witness a single successful trial.