OpenRaiser/Intern-Atlas huggingface dataset:
Lessons Learned from the Use of Generative AI in Engineering and Quality Assurance of a WEB System for Healthcare
Venue: arXiv cs.SE 2025
Year: 2025

The advances and availability of technologies involving Generative Artificial Intelligence (AI) are evolving clearly and explicitly, driving immediate changes in various work activities. Software Engineering (SE) is no exception and stands to benefit from these new technologies, enhancing productivity and quality in its software development processes. However, although the use of Generative AI in SE practices is still in its early stages, considering the lack of conclusive results from ongoing research and the limited technological maturity, we have chosen to incorporate these technologies in the development of a web-based software system to be used in clinical trials by a thoracic diseases research group at our university. For this reason, we decided to share this experience report documenting our development team's learning journey in using Generative AI during the software development process. Project management, requirements specification, design, development, and quality assurance activities form the scope of observation. Although we do not yet have definitive technological evidence to evolve our development process significantly, the results obtained and the suggestions shared here represent valuable insights for software organizations seeking to innovate their development practices to achieve software quality with generative AI.
Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift
Venue: arXiv cs.LG 2025
Year: 2025

Knowledge Tracing (KT) has been an established problem in the educational data mining field for decades, and it is commonly assumed that the underlying learning process being modeled remains static. Given the ever-changing landscape of online learning platforms (OLPs), we investigate how concept drift and changing student populations can impact student behavior within an OLP through testing model performance both within a single academic year and across multiple academic years. Four well-studied KT models were applied to five academic years of data to assess how susceptible KT models are to concept drift. Through our analysis, we find that all four families of KT models can exhibit degraded performance, Bayesian Knowledge Tracing (BKT) remains the most stable KT model when applied to newer data, while more complex, attention based models lose predictive power significantly faster.
A Voice-Enabled Virtual Patient System for Interactive Training in Standardized Clinical Assessment
Venue: arXiv cs.HC 2025
Year: 2025

Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities, which can impact data quality in clinical trials. To address this gap, we introduce a voice-enabled virtual patient simulation system powered by a large language model (LLM). This study describes the system's development and validates its ability to generate virtual patients who accurately adhere to pre-defined clinical profiles, maintain coherent narratives, and produce realistic dialogue. We implemented a system using a LLM to simulate patients with specified symptom profiles, demographics, and communication styles. The system was evaluated by 5 experienced clinical raters who conducted 20 simulated structured MADRS interviews across 4 virtual patient personas. The virtual patients demonstrated strong adherence to their clinical profiles, with a mean item difference between rater-assigned MADRS scores and configured scores of 0.52 (SD=0.75). Inter-rater reliability across items was 0.90 (95% CI=0.68-0.99). Expert raters consistently rated the qualitative realism and cohesiveness of the virtual patients favorably, giving average ratings between "Agree" and "Strongly Agree." Our findings suggest that LLM-powered virtual patient simulations are a viable and scalable tool for training clinicians, capable of producing high-fidelity, clinically relevant practice scenarios.
Validating Deep Models for Alzheimer's 18F-FDG PET Diagnosis Across Populations: A Study with Latin American Data
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning models have shown strong performance in diagnosing Alzheimer's disease (AD) using neuroimaging data, particularly 18F-FDG PET scans, with training datasets largely composed of North American cohorts such as those in the Alzheimer's Disease Neuroimaging Initiative (ADNI). However, their generalization to underrepresented populations remains underexplored. In this study, we benchmark convolutional and Transformer-based models on the ADNI dataset and assess their generalization performance on a novel Latin American clinical cohort from the FLENI Institute in Buenos Aires, Argentina. We show that while all models achieve high AUCs on ADNI (up to .96, .97), their performance drops substantially on FLENI (down to .82, .80, respectively), revealing a significant domain shift. The tested architectures demonstrated similar performance, calling into question the supposed advantages of transformers for this specific task. Through ablation studies, we identify per-image normalization and a correct sampling selection as key factors for generalization. Occlusion sensitivity analysis further reveals that models trained on ADNI, generally attend to canonical hypometabolic regions for the AD class, but focus becomes unclear for the other classes and for FLENI scans. These findings highlight the need for population-aware validation of diagnostic AI models and motivate future work on domain adaptation and cohort diversification.
Improved Decoding Algorithms for MDS and Almost-MDS Codesfrom Twisted GRS Codes
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, firstly, we study decoding of a general class of twisted generalized Reed-Solomon (TGRS) codes and provide a precise characterization of the key equation for TGRS codes and propose a decoding algorithm. Secondly, we further study decoding of almost-MDS TGRS codes and provide a decoding algorithm. These two decoding algorithms are more efficient in terms of performance compared with the decoding algorithms presented in [Sun et al., IEEE-TIT, 2024] and [Sui et al., IEEE-TIT, 2023] respectively.
Power Control Based on Multi-Agent Deep Q Network for D2D Communication
Venue: arXiv cs.NI 2025
Year: 2025

In device-to-device (D2D) communication under a cell with resource sharing mode the spectrum resource utilization of the system will be improved. However, if the interference generated by the D2D user is not controlled, the performance of the entire system and the quality of service (QOS) of the cellular user may be degraded. Power control is important because it helps to reduce interference in the system. In this paper, we propose a reinforcement learning algorithm for adaptive power control that helps reduce interference to increase system throughput. Simulation results show the proposed algorithm has better performance than traditional algorithm in LTE (Long Term Evolution).
A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
Venue: arXiv cs.SE 2025
Year: 2025

Model hallucination is one of the most critical challenges faced by Large Language Models (LLMs), especially in high-stakes code intelligence tasks. As LLMs become increasingly integrated into software engineering tasks, understanding and mitigating hallucination in code becomes essential. In this survey, we provide a systematic review of hallucination phenomena in code-oriented LLMs from four key perspectives. First, we begin by surveying 60 papers to define hallucination in the context of code and summarize its primary causes, such as data noise, exposure bias, and insufficient semantic grounding, while also tracing recent trends in literature across natural language processing (NLP) and software engineering communities. Second, we review model hallucination surveys in a broader span and summarize representative hallucination mitigation strategies, such as knowledge-enhanced generation, constrained decoding, and post-editing. Third, we review approaches targeted for code intelligence and highlight code-specific challenges that aggravate hallucination, including syntax sensitivity, strict type systems, and dependence on external libraries. Meanwhile, we analyze how emerging code intelligence tasks, e.g., program analysis, symbolic execution, and unit testing, are utilized to detect and mitigate hallucinations. Fourth, we summarize current evaluation benchmarks, ranging from static metrics to dynamic checks, e.g., compilation and execution correctness, and emphasize the need for hallucination-oriented benchmarks.
Class-agnostic 3D Segmentation by Granularity-Consistent Automatic 2D Mask Tracking
Venue: arXiv cs.CV 2025
Year: 2025

3D instance segmentation is an important task for real-world applications. To avoid costly manual annotations, existing methods have explored generating pseudo labels by transferring 2D masks from foundation models to 3D. However, this approach is often suboptimal since the video frames are processed independently. This causes inconsistent segmentation granularity and conflicting 3D pseudo labels, which degrades the accuracy of final segmentation. To address this, we introduce a Granularity-Consistent automatic 2D Mask Tracking approach that maintains temporal correspondences across frames, eliminating conflicting pseudo labels. Combined with a three-stage curriculum learning framework, our approach progressively trains from fragmented single-view data to unified multi-view annotations, ultimately globally coherent full-scene supervision. This structured learning pipeline enables the model to progressively expose to pseudo-labels of increasing consistency. Thus, we can robustly distill a consistent 3D representation from initially fragmented and contradictory 2D priors. Experimental results demonstrated that our method effectively generated consistent and accurate 3D segmentations. Furthermore, the proposed method achieved state-of-the-art results on standard benchmarks and open-vocabulary ability.
Fast PINN Eigensolvers via Biconvex Reformulation
Venue: arXiv cs.LG 2025
Year: 2025

Eigenvalue problems have a distinctive forward-inverse structure and are fundamental to characterizing a system's thermal response, stability, and natural modes. Physics-Informed Neural Networks (PINNs) offer a mesh-free alternative for solving such problems but are often orders of magnitude slower than classical numerical schemes. In this paper, we introduce a reformulated PINN approach that casts the search for eigenpairs as a biconvex optimization problem, enabling fast and provably convergent alternating convex search (ACS) over eigenvalues and eigenfunctions using analytically optimal updates. Numerical experiments show that PINN-ACS attains high accuracy with convergence speeds up to 500$\times$ faster than gradient-based PINN training. We release our codes at https://github.com/NeurIPS-ML4PS-2025/PINN_ACS_CODES.
Rhythm in the Air: Vision-based Real-Time Music Generation through Gestures
Venue: arXiv cs.MM 2025
Year: 2025

Gesture recognition is an essential component of human-computer interaction (HCI), facilitating seamless interconnectivity between users and computer systems without physical touch. This paper introduces an innovative application of vision-based dynamic gesture recognition (VDGR) for real-time music composition through gestures. To implement this application, we generate a custom gesture dataset that encompasses over 15000 samples across 21 classes, incorporating 7 musical notes each manifesting at three distinct pitch levels. To effectively deal with the modest volume of training data and to accurately discern and prioritize complex gesture sequences for music creation, we develop a multi-layer attention-based gated recurrent unit (MLA-GRU) model, in which gated recurrent unit (GRU) is used to learn temporal patterns from the observed sequence and an attention layer is employed to focus on musically pertinent gesture segments. Our empirical studies demonstrate that MLA-GRU significantly surpasses the classical GRU model, achieving a remarkable accuracy of 96.83% compared to the baseline's 86.7%. Moreover, our approach exhibits superior efficiency and processing speed, which are crucial for interactive applications. Using our proposed system, we believe that people will interact with music in a new and exciting way. It not only advances HCI experiences but also highlights MLA-GRU's effectiveness in scenarios demanding swift and precise gesture recognition.
AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
Venue: arXiv cs.DC 2025
Year: 2025

Maximizing training throughput and cost-efficiency of RL for LLMs is essential to democratize this advanced technique. One promising but challenging approach is to deploy such a computational workflow over heterogeneous GPUs. Unlike conventional large-scale LLM pretraining, RL training generally decomposes into three coupled stages, i.e., rollout generation, reward computation, and policy/value updates, which exhibit markedly different compute intensities, memory footprints, and communication patterns. Recent research shows that fully asynchronous RL training can disaggregate these stages across disjoint hardware pools without sacrificing training stability, creating a great opportunity for real-world heterogeneous deployment. To this end, we present AReaL-Hex, a heterogeneity-aware asynchronous RL training system that effectively schedules how to execute rollout generation and policy model training over heterogeneous GPUs while enforcing data staleness bounds. Concretely, we use a two-phase scheduler: (i) a constrained search with MILP to select per-stage parallelization strategies and workload assignments given a resource budget, and (ii) a graph-partitioning step that allocates heterogeneous GPUs and interconnects to maximize end-to-end throughput. Built atop a fully asynchronous RL architecture, AReaL-Hex maps HBM-I/O-bound generation and compute-bound optimization to more cost-efficient resources and balances their producer-consumer interactions to avoid both idleness and stale rollout trajectories. On the mathematical reasoning task with various model scales (1.5B, 7B, and 14B), compared to homogeneous deployments of state-of-the-art asynchronous RL systems: (i) When maintaining the same total budgets, AReaL-Hex delivers up to 1.50x higher training throughput; (ii) When achieving the same training throughput, AReaL-Hex results in up to 1.46x reduction in training cost.
Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems
Venue: arXiv cs.LG 2025
Year: 2025

Cyber-physical systems (CPS) require the joint optimization of discrete cyber actions and continuous physical parameters under stringent safety logic constraints. However, existing hierarchical approaches often compromise global optimality, whereas reinforcement learning (RL) in hybrid action spaces often relies on brittle reward penalties, masking, or shielding and struggles to guarantee constraint satisfaction. We present logic-informed reinforcement learning (LIRL), which equips standard policy-gradient algorithms with projection that maps a low-dimensional latent action onto the admissible hybrid manifold defined on-the-fly by first-order logic. This guarantees feasibility of every exploratory step without penalty tuning. Experimental evaluations have been conducted across multiple scenarios, including industrial manufacturing, electric vehicle charging stations, and traffic signal control, in all of which the proposed method outperforms existing hierarchical optimization approaches. Taking a robotic reducer assembly system in industrial manufacturing as an example, LIRL achieves a 36.47\% to 44.33\% reduction at most in the combined makespan-energy objective compared to conventional industrial hierarchical scheduling methods. Meanwhile, it consistently maintains zero constraint violations and significantly surpasses state-of-the-art hybrid-action reinforcement learning baselines. Thanks to its declarative logic-based constraint formulation, the framework can be seamlessly transferred to other domains such as smart transportation and smart grid, thereby paving the way for safe and real-time optimization in large-scale CPS.
Do Math Reasoning LLMs Help Predict the Impact of Public Transit Events?
Venue: arXiv cs.AI 2025
Year: 2025

Predicting public transit incident duration from unstructured text alerts is a critical but challenging task. Addressing the domain sparsity of transit operations with standard Supervised Fine-Tuning (SFT) is difficult, as the task involves noisy, continuous labels and lacks reliable expert demonstrations for reasoning. While Reinforcement Learning from Verifiable Rewards (RLVR) excels at tasks with binary correctness, like mathematics, its applicability to noisy, continuous forecasting is an open question. This work, to our knowledge, is the first to bridge the gap between RLVR LLM training with the critical, real-world forecasting challenges in public transit operations. We adapt RLVR to this task by introducing a tolerance-based, shaped reward function that grants partial credit within a continuous error margin, rather than demanding a single correct answer. We systematically evaluate this framework on a curated dataset of NYC MTA service alerts. Our findings show that general-purpose, instruction-tuned LLMs significantly outperform specialized math-reasoning models, which struggle with the ambiguous, real-world text. We empirically demonstrate that the binary reward is unstable and degrades performance, whereas our shaped reward design is critical and allows our model to dominate on the most challenging metrics. While classical regressors are superior at minimizing overall MAE or MSE, our RLVR approach achieved a 35\% relative improvement in 5-minute accuracy (Acc@5) over the strongest baseline. This demonstrates that RLVR can be successfully adapted to real-world, noisy forecasting, but requires a verifier design that reflects the continuous nature of the problem.
Deciphering Scientific Collaboration in Biomedical LLM Research: Dynamics, Institutional Participation, and Resource Disparities
Venue: arXiv cs.SI 2025
Year: 2025

Large language models (LLMs) are increasingly transforming biomedical discovery and clinical innovation, yet their impact extends far beyond algorithmic revolution-LLMs are restructuring how scientific collaboration occurs, who participates, and how resources shape innovation. Despite this profound transformation, how this rapid technological shift is reshaping the structure and equity of scientific collaboration in biomedical LLM research remains largely unknown. By analyzing 5,674 LLM-related biomedical publications from PubMed, we examine how collaboration diversity evolves over time, identify institutions and disciplines that anchor and bridge collaboration networks, and assess how resource disparities underpin research performance. We find that collaboration diversity has grown steadily, with a decreasing share of Computer Science and Artificial Intelligence authors, suggesting that LLMs are lowering technical barriers for biomedical investigators. Network analysis reveals central institutions, including Stanford University and Harvard Medical School, and bridging disciplines such as Medicine and Computer Science that anchor collaborations in this field. Furthermore, biomedical research resources are strongly linked to research performance, with high-performing resource-constrained institutions exhibiting larger collaboration volume with the top 1% most connected institutions in the network. Together, these findings reveal a complex landscape, where democratizing trends coexist with a persistent, resource-driven hierarchy, highlighting the critical role of strategic collaboration in this evolving field.
TINC: Trusted Intelligent NetChain
Venue: arXiv cs.NI 2025
Year: 2025

Blockchain technology facilitates the development of decentralized systems that ensure trust and transparency without the need for expensive centralized intermediaries. However, existing blockchain architectures particularly consortium blockchains face critical challenges related to scalability and efficiency. State sharding has emerged as a promising approach to enhance blockchain scalability and performance. However, current shard-based solutions often struggle to guarantee fair participation and a balanced workload distribution among consortium members. To address these limitations, we propose Trusted Intelligent NetChain (TINC), a multi-plane sharding architecture specifically designed for consortium blockchains. TINC incorporates intelligent mechanisms for adaptive node assignment and dynamic workload balancing, enabling the system to respond effectively to changing network conditions while maintaining equitable shard utilization. By decoupling the control and data planes, TINC allows control nodes to focus on consensus operations, while data nodes handle large-scale storage, thus improving overall resource efficiency. Extensive experimental evaluation and formal analysis demonstrate that TINC significantly outperforms existing shard-based blockchain frameworks. It achieves higher throughput, lower latency, balanced node and transaction distributions, and reduced transaction failure rates. Furthermore, TINC maintains essential blockchain security guarantees, exhibiting resilience against Byzantine faults and dynamic network environments. The integration of Dynamic Decentralized Identifiers (DDIDs) further strengthens trust and security management within the consortium network.
CodeClash: Benchmarking Goal-Oriented Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

Current benchmarks for coding evaluate language models (LMs) on concrete, well-specified tasks such as fixing specific bugs or writing targeted tests. However, human programmers do not spend all day incessantly addressing isolated tasks. Instead, real-world software development is grounded in the pursuit of high-level goals, like improving user retention or reducing costs. Evaluating whether LMs can also iteratively develop code to better accomplish open-ended objectives without any explicit guidance remains an open challenge. To address this, we introduce CodeClash, a benchmark where LMs compete in multi-round tournaments to build the best codebase for achieving a competitive objective. Each round proceeds in two phases: agents edit their code, then their codebases compete head-to-head in a code arena that determines winners based on objectives like score maximization, resource acquisition, or survival. Whether it's writing notes, scrutinizing documentation, analyzing competition logs, or creating test suites, models must decide for themselves how to improve their codebases both absolutely and against their opponents. We run 1680 tournaments (25,200 rounds total) to evaluate 8 LMs across 6 arenas. Our results reveal that while models exhibit diverse development styles, they share fundamental limitations in strategic reasoning. Models also struggle with long-term codebase maintenance, as repositories become progressively messy and redundant. These limitations are stark: top models lose every round against expert human programmers. We open-source CodeClash to advance the study of autonomous, goal-oriented code development.