OpenRaiser/Intern-Atlas huggingface dataset:
nodeWSNsec: A hybrid metaheuristic approach for reliable security and node deployment in WSNs
Venue: arXiv cs.CR 2025
Year: 2025

Efficient and reliable node deployment in Wireless Sensor Networks is crucial for optimizing coverage of the area, connectivity among nodes, and energy efficiency. This paper proposes a hybrid meta heuristic approach combining a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to address the challenges of energy efficient and reliable node deployment. The GA PSO hybrid leverages GAs strong exploration capabilities and PSOs rapid convergence, achieving an optimum stability between coverage and energy consumption. The performance of the proposed approach is evaluated against GA and PSO alone and the innovatory meta heuristic based Competitive Multi Objective Marine Predators Algorithm (CMOMPA) across varying sensing ranges. Simulation results demonstrate that GA PSO requires 15% to 25% fewer sensor nodes and maintains 95% or more area coverage while maintaining the connectivity in comparison to standalone GA or PSO algorithm. The proposed algorithm also dominates CMOMPA when compared for long sensing and communication range in terms of higher coverage, improved connectivity, and reduced deployment time while requiring fewer sensor nodes. This study also explores key trade offs in WSN deployment and highlights future research directions, including heterogeneous node deployment, mobile WSNs, and enhanced multi objective optimization techniques. The findings underscore the effectiveness of hybrid meta heuristics in improving WSN performance, offering a promising approach for real world applications such as environmental monitoring, smart cities, smart agriculture, disaster response, and IIoT.
Equinox: Holistic Fair Scheduling in Serving Large Language Models
Venue: arXiv cs.DC 2025
Year: 2025

We address the limitations of current LLM serving with a dual-counter framework separating user and operator perspectives. The User Fairness Counter measures quality of service via weighted tokens and latency; the Resource Fairness Counter measures operational efficiency through throughput and GPU utilization. Since these metrics are only available post-execution, creating a scheduling paradox, we introduce a deterministic Mixture of Prediction Experts (MoPE) framework to predict user-perceived latency, output tokens, throughput, and GPU utilization. These predictions enable calculation of a unified Holistic Fairness score that balances both counters through tunable parameters for proactive fairness-aware scheduling. We implement this in Equinox, an open-source system with other optimizations like adaptive batching, and stall-free scheduling. Evaluations on production traces (ShareGPT, LMSYS) and synthetic workloads demonstrate Equinox achieves up to $1.3\times$ higher throughput, 60\% lower time-to-first-token latency, and 13\% higher fairness versus VTC while maintaining 94\% GPU utilization, proving fairness under bounded discrepancy across heterogeneous platforms.
H2EAL: Hybrid-Bonding Architecture with Hybrid Sparse Attention for Efficient Long-Context LLM Inference
Venue: arXiv cs.PF 2025
Year: 2025

Large language models (LLMs) have demonstrated remarkable proficiency in a wide range of natural language processing applications. However, the high energy and latency overhead induced by the KV cache limits the edge deployment, especially for long contexts. Emerging hybrid bonding (HB) technology has been proposed as a promising alternative to conventional near-memory processing (NMP) architectures, offering improved bandwidth efficiency and lower power consumption while exhibiting characteristics of distributed memory. In this paper, we propose H2EAL, a hybrid bonding-based accelerator with sparse attention algorithm-hardware co-design for efficient LLM inference at the edge. At the algorithm level, we propose a hybrid sparse attention scheme with static and dynamic sparsity for different heads to fully leverage the sparsity with high accuracy. At the hardware level, we co-design the hardware to support hybrid sparse attention and propose memory-compute co-placement to address the distributed memory bottleneck. Since different attention heads exhibit different sparse patterns and the attention structure often mismatches the HB architecture, we further develop a load-balancing scheduler with parallel tiled attention to address workload imbalance and optimize the mapping strategy. Extensive experiments demonstrate H2EAL achieves 5.20~48.21x speedup and 6.22~73.48x energy efficiency improvement over baseline HB implementation, with a negligible average accuracy drop of 0.87% on multiple benchmarks.
A Laplace diffusion-based transformer model for heart rate forecasting within daily activity context
Venue: arXiv cs.LG 2025
Year: 2025

With the advent of wearable Internet of Things (IoT) devices, remote patient monitoring (RPM) emerged as a promising solution for managing heart failure. However, the heart rate can fluctuate significantly due to various factors, and without correlating it to the patient's actual physical activity, it becomes difficult to assess whether changes are significant. Although Artificial Intelligence (AI) models may enhance the accuracy and contextual understanding of remote heart rate monitoring, the integration of activity data is still rarely addressed. In this paper, we propose a Transformer model combined with a Laplace diffusion technique to model heart rate fluctuations driven by physical activity of the patient. Unlike prior models that treat activity as secondary, our approach conditions the entire modeling process on activity context using specialized embeddings and attention mechanisms to prioritize activity specific historical patents. The model captures both long-term patterns and activity-specific heart rate dynamics by incorporating contextualized embeddings and dedicated encoder. The Transformer model was validated on a real-world dataset collected from 29 patients over a 4-month period. Experimental results show that our model outperforms current state-of-the-art methods, achieving a 43% reduction in mean absolute error compared to the considered baseline models. Moreover, the coefficient of determination R2 is 0.97 indicating the model predicted heart rate is in strong agreement with actual heart rate values. These findings suggest that the proposed model is a practical and effective tool for supporting both healthcare providers and remote patient monitoring systems.
Bridging the Mobile Trust Gap: A Zero Trust Framework for Consumer-Facing Applications
Venue: arXiv cs.CR 2025
Year: 2025

Zero Trust Architecture (ZTA) has become a widely adopted model for securing enterprise environments, promoting continuous verification and minimal trust across systems. However, its application in mobile contexts remains limited, despite mobile applications now accounting for most global digital interactions and being increasingly targeted by sophisticated threats. Existing Zero Trust frameworks developed by organisations such as the National Institute of Standards and Technology (NIST) and the Cybersecurity and Infrastructure Security Agency (CISA) primarily focus on enterprise-managed infrastructure, assuming organisational control over devices, networks, and identities. This paper addresses a critical gap by proposing an extended Zero Trust model designed for mobile applications operating in untrusted, user-controlled environments. Using a design science methodology, the study introduced a six-pillar framework that supports runtime enforcement of trust through controls including device integrity, user identity validation, data protection, secure application programming interface (API) usage, behavioural monitoring, and live application protection. Each pillar was mapped to relevant regulatory and security standards to support compliance. A phased implementation roadmap and maturity assessment model were also developed to guide adoption across varying organisational contexts. The proposed model offers a practical and standards-aligned approach to securing mobile applications beyond pre-deployment controls, aligning real-time enforcement with Zero Trust principles. This contribution expands the operational boundaries of ZTA and provides organisations with a deployable path to reduce fraud, enhance compliance, and address emerging mobile security challenges. Future research may include empirical validation of the framework and cross-sector application testing.
The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
Venue: arXiv cs.CY 2025
Year: 2025

We analyzed nearly 460,000 AI model cards from Hugging Face to examine how developers report risks. From these, we extracted around 3,000 unique risk mentions and built the \emph{AI Model Risk Catalog}. We compared these with risks identified by researchers in the MIT Risk Repository and with real-world incidents from the AI Incident Database. Developers focused on technical issues like bias and safety, while researchers emphasized broader social impacts. Both groups paid little attention to fraud and manipulation, which are common harms arising from how people interact with AI. Our findings show the need for clearer, structured risk reporting that helps developers think about human-interaction and systemic risks early in the design process. The catalog and paper appendix are available at: https://social-dynamics.net/ai-risks/catalog.
Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

Artificial Intelligence (AI) is increasingly used in hiring, with large language models (LLMs) having the potential to influence or even make hiring decisions. However, this raises pressing concerns about bias, fairness, and trust, particularly across diverse cultural contexts. Despite their growing role, few studies have systematically examined the potential biases in AI-driven hiring evaluation across cultures. In this study, we conduct a systematic analysis of how LLMs assess job interviews across cultural and identity dimensions. Using two datasets of interview transcripts, 100 from UK and 100 from Indian job seekers, we first examine cross-cultural differences in LLM-generated scores for hirability and related traits. Indian transcripts receive consistently lower scores than UK transcripts, even when they were anonymized, with disparities linked to linguistic features such as sentence complexity and lexical diversity. We then perform controlled identity substitutions (varying names by gender, caste, and region) within the Indian dataset to test for name-based bias. These substitutions do not yield statistically significant effects, indicating that names alone, when isolated from other contextual signals, may not influence LLM evaluations. Our findings underscore the importance of evaluating both linguistic and social dimensions in LLM-driven evaluations and highlight the need for culturally sensitive design and accountability in AI-assisted hiring.
Democratizing AI Development: Local LLM Deployment for India's Developer Ecosystem in the Era of Tokenized APIs
Venue: arXiv cs.SE 2025
Year: 2025

India's developer community faces significant barriers to sustained experimentation and learning with commercial Large Language Model (LLM) APIs, primarily due to economic and infrastructural constraints. This study empirically evaluates local LLM deployment using Ollama as an alternative to commercial cloud-based services for developer-focused applications. Through a mixed-methods analysis involving 180 Indian developers, students, and AI enthusiasts, we find that local deployment enables substantially greater hands-on development and experimentation, while reducing costs by 33% compared to commercial solutions. Developers using local LLMs completed over twice as many experimental iterations and reported deeper understanding of advanced AI architectures. Our results highlight local deployment as a critical enabler for inclusive and accessible AI development, demonstrating how technological accessibility can enhance learning outcomes and innovation capacity in resource-constrained environments.
Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change
Venue: arXiv cs.CY 2025
Year: 2025

This study demonstrates the extent to which prominent debates about the future of AI are best understood as subjective, philosophical disagreements over the history and future of technological change rather than as objective, material disagreements over the technologies themselves. It focuses on the deep disagreements over whether artificial general intelligence (AGI) will prove transformative for human society; a question that is analytically prior to that of whether this transformative effect will help or harm humanity. The study begins by distinguishing two fundamental camps in this debate. The first of these can be identified as "transformationalists," who argue that continued AI development will inevitably have a profound effect on society. Opposed to them are "skeptics," a more eclectic group united by their disbelief that AI can or will live up to such high expectations. Each camp admits further "strong" and "weak" variants depending on their tolerance for epistemic risk. These stylized contrasts help to identify a set of fundamental questions that shape the camps' respective interpretations of the future of AI. Three questions in particular are focused on: the possibility of non-biological intelligence, the appropriate time frame of technological predictions, and the assumed trajectory of technological development. In highlighting these specific points of non-technical disagreement, this study demonstrates the wide range of different arguments used to justify either the transformationalist or skeptical position. At the same time, it highlights the strong argumentative burden of the transformationalist position, the way that belief in this position creates competitive pressures to achieve first-mover advantage, and the need to widen the concept of "expertise" in debates surrounding the future development of AI.
The Snowblower Problem: An Improvement
Venue: arXiv cs.CG 2025
Year: 2025

We present more optimal solutions to the snowblower problem introduced in arXiv:cs/0603026. In particular, we present more optimal ways to clear lines and combs, which are shapes as described in the aforementioned paper that the original input is dissected into. We note that our methods to clear lines and combs reduce the number of steps needed to clear such shapes, while also acknowledging that they do not change the overall complexity.
Securing Heterogeneous Network (HetNet) Communications for Wildfire Management: Mitigating the Effects of Adversarial and Environmental Threats
Venue: arXiv cs.CR 2025
Year: 2025

In the face of adverse environmental conditions and cyber threats, robust communication systems for critical applications such as wildfire management and detection demand secure and resilient architectures. This paper presents a novel framework that considers both adversarial factors, building resilience into a heterogeneous network (HetNet) integrating Low Earth Orbit (LEO) satellite constellation with High-Altitude Platform Ground Stations (HAPGS) and Low-Altitude Platforms (LAPS), tailored to support wildfire management operations. Building upon our previous work on secure-by-component approach for link segment security, we extend protection to the communication layer by securing both Radio Frequency (RF)/Free Space Optics (FSO) management and different links. Through a case study, we quantify how environmental stressors impact secrecy capacity and expose the system to passive adversaries. Key findings demonstrate that atmospheric attenuation and beam misalignment can notably degrade secrecy capacity across both short- and long-range communication links, while high-altitude eavesdroppers face less signal degradation, increasing their interception capability. Moreover, increasing transmit power to counter environmental losses can inadvertently improve eavesdropper reception, thereby reducing overall link confidentiality. Our work not only highlights the importance of protecting networks from these dual threats but also aligns with the IEEE P3536 Standard for Space System Cybersecurity Design, ensuring resilience and the prevention of mission failures.
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
Venue: arXiv cs.SE 2025
Year: 2025

Code language models (so-called CodeLLMs) are now commonplace in software development. As a general rule, CodeLLMs are trained by dividing training examples into input tokens and then learn importance of those tokens in a process called machine attention. Machine attention is based solely on input token salience to output token examples during training. Human software developers are different, as humans intuitively know that some tokens are more salient than others. While intuition itself is ineffable and a subject of philosophy, clues about salience are present in human visual attention, since people tend to look at more salient words more often. In this paper, we present EyeMulator, a technique for training CodeLLMs to mimic human visual attention while training for various software development tasks. We add special weights for each token in each input example to the loss function used during LLM fine-tuning. We draw these weights from observations of human visual attention derived from a previously-collected publicly-available dataset of eye-tracking experiments in software engineering tasks. These new weights ultimately induce changes in the attention of the subject LLM during training, resulting in a model that does not need eye-tracking data during inference. Our evaluation shows that EyeMulator outperforms strong LLM baselines on several tasks such as code translation, completion and summarization. We further show an ablation study that demonstrates the improvement is due to subject models learning to mimic human attention.
Interpreting the Effects of Quantization on LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Quantization offers a practical solution to deploy LLMs in resource-constraint environments. However, its impact on internal representations remains understudied, raising questions about the reliability of quantized models. In this study, we employ a range of interpretability techniques to investigate how quantization affects model and neuron behavior. We analyze multiple LLMs under 4-bit and 8-bit quantization. Our findings reveal that the impact of quantization on model calibration is generally minor. Analysis of neuron activations indicates that the number of dead neurons, i.e., those with activation values close to 0 across the dataset, remains consistent regardless of quantization. In terms of neuron contribution to predictions, we observe that smaller full precision models exhibit fewer salient neurons, whereas larger models tend to have more, with the exception of Llama-2-7B. The effect of quantization on neuron redundancy varies across models. Overall, our findings suggest that effect of quantization may vary by model and tasks, however, we did not observe any drastic change which may discourage the use of quantization as a reliable model compression technique.
Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities
Venue: arXiv cs.CL 2025
Year: 2025

Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, (c) controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model's effectiveness in real-world OMHC settings.
PICO: Performance Insights for Collective Operations
Venue: arXiv cs.DC 2025
Year: 2025

Collective operations are cornerstones of both HPC application and large-scale AI training and inference. Yet, comprehensive, systematic and reproducible performance evaluation and benchmarking of said operations is not straightforward. Existing frameworks do not provide sufficiently detailed profiling information, nor they ensure reproducibility and extensibility. In this paper, we present PICO (Performance Insights for Collective Operations), a novel lightweight, extensible framework built with the aim of simplifying collective operations benchmarking.
AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results
Venue: arXiv cs.CV 2025
Year: 2025

This paper reviews the AIM 2025 (Advances in Image Manipulation) Low-Light RAW Video Denoising Challenge. The task is to develop methods that denoise low-light RAW video by exploiting temporal redundancy while operating under exposure-time limits imposed by frame rate and adapting to sensor-specific, signal-dependent noise. We introduce a new benchmark of 756 ten-frame sequences captured with 14 smartphone camera sensors across nine conditions (illumination: 1/5/10 lx; exposure: 1/24, 1/60, 1/120 s), with high-SNR references obtained via burst averaging. Participants process linear RAW sequences and output the denoised 10th frame while preserving the Bayer pattern. Submissions are evaluated on a private test set using full-reference PSNR and SSIM, with final ranking given by the mean of per-metric ranks. This report describes the dataset, challenge protocol, and submitted approaches.