OpenRaiser/Intern-Atlas huggingface dataset:
Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Unmanned Aerial Vehicles (UAVs) are increasingly used in defense, surveillance, and disaster response, yet most systems still operate at SAE Level 2 to 3 autonomy. Their dependence on rule-based control and narrow AI limits adaptability in dynamic and uncertain missions. Current UAV architectures lack context-aware reasoning, autonomous decision-making, and integration with external systems. Importantly, none make use of Large Language Model (LLM) agents with tool-calling for real-time knowledge access. This paper introduces the Agentic UAVs framework, a five-layer architecture consisting of Perception, Reasoning, Action, Integration, and Learning. The framework enhances UAV autonomy through LLM-driven reasoning, database querying, and interaction with third-party systems. A prototype built with ROS 2 and Gazebo combines YOLOv11 for object detection with GPT-4 for reasoning and a locally deployed Gemma 3 model. In simulated search-and-rescue scenarios, agentic UAVs achieved higher detection confidence (0.79 compared to 0.72), improved person detection rates (91% compared to 75%), and a major increase in correct action recommendations (92% compared to 4.5%). These results show that modest computational overhead can enable significantly higher levels of autonomy and system-level integration.
Semantic Fusion with Fuzzy-Membership Features for Controllable Language Modelling
Venue: arXiv cs.AI 2025
Year: 2025

We propose semantic fusion, a lightweight scheme that augments a Transformer language model (LM) with a parallel, fuzzy-membership feature channel that encodes token-level semantics. Each token is represented by a vector of interpretable features (e.g. part-of-speech cues, shallow roles, boundary flags, sentiment polarity and strength) whose values are graded degrees from differentiable membership functions (e.g. power kernels). These per-token vectors form a sentence-level semantic matrix fused via a gated adapter into the LM. Training uses standard next-token prediction, an auxiliary loss that reconstructs the semantic features from hidden states, and a lightweight uniformizer that regularizes adjective-class distributions. On a synthetic two-clause corpus with held-out adjectives for out-of-distribution (OOD) control, semantic fusion improves perplexity and enables precise, user-controllable generation of polarity and punctuation while maintaining model simplicity. This approach adds only small overhead, remains fully compatible with tied input-output embeddings, and provides an interpretable pathway for conditioned natural language generation.
Asterisk Operator
Venue: arXiv cs.AI 2025
Year: 2025

We propose the \textbf{Asterisk Operator} ($\ast$-operator), a novel unified framework for abstract reasoning based on Adjacency-Structured Parallel Propagation (ASPP). The operator formalizes structured reasoning tasks as local, parallel state evolution processes guided by implicit relational graphs. We prove that the $\ast$-operator maintains local computational constraints while achieving global reasoning capabilities, providing an efficient and convergent computational paradigm for abstract reasoning problems. Through rigorous mathematical analysis and comprehensive experiments on ARC2 challenges and Conway's Game of Life, we demonstrate the operator's universality, convergence properties, and superior performance. Our innovative Embedding-Asterisk distillation method achieves 100\% accuracy on ARC2 validation with only 6M parameters, representing a significant breakthrough in neural-symbolic reasoning. \textbf{Keywords:} Abstract Reasoning, Adjacency Structure, Parallel Propagation, Asterisk Operator, Convergence, Universal Approximation
Vehicle-to-Grid Integration: Ensuring Grid Stability, Strengthening Cybersecurity, and Advancing Energy Market Dynamics
Venue: arXiv cs.OH 2025
Year: 2025

The increasing adoption of electric vehicles has spurred significant interest in Vehicle-to-Grid technology as a transformative approach to modern energy systems. This paper presents a systematic review of V2G systems, focusing on their integration challenges and potential solutions. First, the current state of V2G development is examined, highlighting its growing importance in mitigating peak demand, enhancing voltage and frequency regulation, and reinforcing grid resilience. The study underscores the pivotal role of artificial intelligence and machine learning in optimizing energy management, load forecasting, and real-time grid control. A critical analysis of cybersecurity risks reveals heightened vulnerabilities stemming from V2G's dependence on interconnected networks and real-time data exchange, prompting an exploration of advanced mitigation strategies, including federated learning, blockchain, and quantum-resistant cryptography. Furthermore, the paper reviews economic and market aspects, including business models (V2G as an aggregator or due to self-consumption), regulation (as flexibility service provider) and factors influencing user acceptance shaping V2G adoption. Data from global case studies and pilot programs offer a snapshot of how V2G has been implemented at different paces across regions. Finally, the study suggests a multi-layered framework that incorporates grid stability resilience, cybersecurity resiliency, and energy market dynamics and provides strategic recommendations to enable scalable, secure, and economically viable V2G deployment.
EdiVal-Agent: An Object-Centric Framework for Automated, Fine-Grained Evaluation of Multi-Turn Editing
Venue: arXiv cs.CV 2025
Year: 2025

Instruction-based image editing has advanced rapidly, yet reliable and interpretable evaluation remains a bottleneck. Current protocols either (i) depend on paired reference images-resulting in limited coverage and inheriting biases from prior generative models-or (ii) rely solely on zero-shot vision-language models (VLMs), whose prompt-based assessments of instruction following, content consistency, and visual quality are often imprecise. To address this, we introduce EdiVal-Agent, an automated and fine-grained evaluation framework grounded in an object-centric perspective, designed to assess not only standard single-turn but also multi-turn instruction-based editing with precision. Given an input image, EdiVal-Agent first decomposes it into semantically meaningful objects, then synthesizes diverse, context-aware editing instructions while dynamically updating object pools across turns. These two stages enable two novel object-centric metrics tailored for multi-turn evaluation and one global metric of visual quality: (1) EdiVal-IF, which measures instruction following by combining open-vocabulary object detectors for symbolic checks with VLMs for semantic verification on detector-guided crops; (2) EdiVal-CC, which evaluates content consistency by calculating semantic similarity of unchanged objects and background using the evolving object pools; and (3) EdiVal-VQ, which quantifies changes in overall visual quality with human preference models. Instantiating this pipeline, we build EdiVal-Bench, a multi-turn editing benchmark covering 9 instruction types and 13 state-of-the-art editing models spanning in-context, flow-matching, and diffusion paradigms. We demonstrate that EdiVal-Agent can be used to identify existing failure modes, thereby informing the development of the next generation of editing models.
Reproducible workflow for online AI in digital health
Venue: arXiv cs.CY 2025
Year: 2025

Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve their performance as streaming data is collected on individuals. Deploying online AI presents a key challenge: balancing adaptability of online AI with reproducibility. Online AI in digital interventions is a rapidly evolving area, driven by advances in algorithms, sensors, software, and devices. Digital health intervention development and deployment is a continuous process, where implementation - including the AI decision-making algorithm - is interspersed with cycles of re-development and optimization. Each deployment informs the next, making iterative deployment a defining characteristic of this field. This iterative nature underscores the importance of reproducibility: data collected across deployments must be accurately stored to have scientific utility, algorithm behavior must be auditable, and results must be comparable over time to facilitate scientific discovery and trustworthy refinement. This paper proposes a reproducible scientific workflow for developing, deploying, and analyzing online AI decision-making algorithms in digital health interventions. Grounded in practical experience from multiple real-world deployments, this workflow addresses key challenges to reproducibility across all phases of the online AI algorithm development life-cycle.
DEFT-VTON: Efficient Virtual Try-On with Consistent Generalised H-Transform
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models enable high-quality virtual try-on (VTO) with their established image synthesis abilities. Despite the extensive end-to-end training of large pre-trained models involved in current VTO methods, real-world applications often prioritize limited training and inference, serving, and deployment budgets for VTO. To solve this obstacle, we apply Doob's h-transform efficient fine-tuning (DEFT) for adapting large pre-trained unconditional models for downstream image-conditioned VTO abilities. DEFT freezes the pre-trained model's parameters and trains a small h-transform network to learn a conditional h-transform. The h-transform network allows training only 1.42 percent of the frozen parameters, compared to a baseline of 5.52 percent in traditional parameter-efficient fine-tuning (PEFT). To further improve DEFT's performance and decrease existing models' inference time, we additionally propose an adaptive consistency loss. Consistency training distills slow but high-performing diffusion models into a fast one while retaining performance by enforcing consistencies along the inference path. Inspired by constrained optimization, instead of distillation, we combine the consistency loss and the denoising score matching loss in a data-adaptive manner for fine-tuning existing VTO models at a low cost. Empirical results show the proposed DEFT-VTON method achieves state-of-the-art performance on VTO tasks, with as few as 15 denoising steps, while maintaining competitive results.
Practitioners' Perspectives on a Differential Privacy Deployment Registry
Venue: arXiv cs.CR 2025
Year: 2025

Differential privacy (DP) -- a principled approach to producing statistical data products with strong, mathematically provable privacy guarantees for the individuals in the underlying dataset -- has seen substantial adoption in practice over the past decade. Applying DP requires making several implementation decisions, each with significant impacts on data privacy and/or utility. Hence, to promote shared learning and accountability around DP deployments, Dwork, Kohli, and Mulligan (2019) proposed a public-facing repository ("registry") of DP deployments. The DP community has recently started to work toward realizing this vision. We contribute to this effort by (1) developing a holistic, hierarchical schema to describe any given DP deployment and (2) designing and implementing an interactive interface to act as a registry where practitioners can access information about past DP deployments. We (3) populate our interface with 21 real-world DP deployments and (4) conduct an exploratory user study with DP practitioners ($n=16$) to understand how they would use the registry, as well as what challenges and opportunities they foresee around its adoption. We find that participants were enthusiastic about the registry as a valuable resource for evaluating prior deployments and making future deployments. They also identified several opportunities for the registry, including that it can become a "hub" for the community and support broader communication around DP (e.g., to legal teams). At the same time, they identified challenges around the registry gaining adoption, including the effort and risk involved with making implementation choices public and moderating the quality of entries. Based on our findings, we offer recommendations for encouraging adoption and increasing the registry's value not only to DP practitioners, but also to policymakers, data users, and data subjects.
An Analysis of Optimizer Choice on Energy Efficiency and Performance in Neural Network Training
Venue: arXiv cs.LG 2025
Year: 2025

As machine learning models grow increasingly complex and computationally demanding, understanding the environmental impact of training decisions becomes critical for sustainable AI development. This paper presents a comprehensive empirical study investigating the relationship between optimizer choice and energy efficiency in neural network training. We conducted 360 controlled experiments across three benchmark datasets (MNIST, CIFAR-10, CIFAR-100) using eight popular optimizers (SGD, Adam, AdamW, RMSprop, Adagrad, Adadelta, Adamax, NAdam) with 15 random seeds each. Using CodeCarbon for precise energy tracking on Apple M1 Pro hardware, we measured training duration, peak memory usage, carbon dioxide emissions, and final model performance. Our findings reveal substantial trade-offs between training speed, accuracy, and environmental impact that vary across datasets and model complexity. We identify AdamW and NAdam as consistently efficient choices, while SGD demonstrates superior performance on complex datasets despite higher emissions. These results provide actionable insights for practitioners seeking to balance performance and sustainability in machine learning workflows.
Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Whole-body manipulation (WBM) for humanoid robots presents a promising approach for executing embracing tasks involving bulky objects, where traditional grasping relying on end-effectors only remains limited in such scenarios due to inherent stability and payload constraints. This paper introduces a reinforcement learning framework that integrates a pre-trained human motion prior with a neural signed distance field (NSDF) representation to achieve robust whole-body embracing. Our method leverages a teacher-student architecture to distill large-scale human motion data, generating kinematically natural and physically feasible whole-body motion patterns. This facilitates coordinated control across the arms and torso, enabling stable multi-contact interactions that enhance the robustness in manipulation and also the load capacity. The embedded NSDF further provides accurate and continuous geometric perception, improving contact awareness throughout long-horizon tasks. We thoroughly evaluate the approach through comprehensive simulations and real-world experiments. The results demonstrate improved adaptability to diverse shapes and sizes of objects and also successful sim-to-real transfer. These indicate that the proposed framework offers an effective and practical solution for multi-contact and long-horizon WBM tasks of humanoid robots.
Demystifying Progressive Web Application Permission Systems
Venue: arXiv cs.CR 2025
Year: 2025

Progressive Web Applications (PWAs) blend the advantages of web and native apps, offering features like offline access, push notifications, and installability. Beyond these, modern PWAs are increasingly granted system-level capabilities such as auto-start on login and shared context with native applications. However, their permission management remains poorly defined and inconsistently implemented across platforms and browsers. To investigate these gaps, we developed Permissioner, a cross-platform analysis tool, and conducted a systematic study of PWA permissions. Our analysis uncovered critical issues of inconsistency, incompleteness, and unclear boundaries in permission enforcement, leading to various attacks including permission leakage, device identification, and Permission API abuse. We further examined why some browsers resist adopting more granular permission controls, identifying trade-offs involving usability, compatibility, and platform limitations. Through collaboration with browser vendors, several issues reported in our findings were acknowledged and resolved, notably by Firefox and Chrome. Our work highlights the urgent need for a unified, robust permission model for PWAs and provides actionable guidance toward achieving this goal.
XASDB -- Design and Implementation of an Open-Access Spectral Database
Venue: arXiv cs.DB 2025
Year: 2025

The increasing volume and complexity of X-ray absorption spectroscopy (XAS) data generated at synchrotron facilities worldwide require robust infrastructure for data management, sharing, and analysis. This paper introduces the XAS Database (XASDB), a comprehensive web-based platform developed and hosted by the Canadian Light Source (CLS). The database houses more than 1000 reference spectra spanning 40 elements and 324 chemical compounds. The platform employs a Node.js/MongoDB architecture designed to handle diverse data formats from multiple beamlines and synchrotron facilities. A key innovation is the XASproc JavaScript library, which enables browser-based XAS data processing including normalization, background sub- traction, extended X-ray absorption fine structure (EXAFS) extraction, and preliminary analysis traditionally limited to desktop applications. The integrated XASVue spectral viewer provides installation-free data visualization and analysis with broad accessibility across devices and operating systems. By offering standardized data output, comprehensive metadata, and integrated analytical ca- pabilities, XASDB facilitates collaborative research and promotes FAIR (Findable, Accessible, In- teroperable, and Reusable) data principles. The platform serves as a valuable resource for linear combination fitting (LCF) analysis, machine learning applications, and educational purposes. This initiative demonstrates the potential for web-centric approaches in XAS data analysis, accelerating advances in materials science, environmental research, chemistry, and biology.
Gen AI in Proof-based Math Courses: A Pilot Study
Venue: arXiv cs.AI 2025
Year: 2025

With the rapid rise of generative AI in higher education and the unreliability of current AI detection tools, developing policies that encourage student learning and critical thinking has become increasingly important. This study examines student use and perceptions of generative AI across three proof-based undergraduate mathematics courses: a first-semester abstract algebra course, a topology course and a second-semester abstract algebra course. In each case, course policy permitted some use of generative AI. Drawing on survey responses and student interviews, we analyze how students engaged with AI tools, their perceptions of generative AI's usefulness and limitations, and what implications these perceptions hold for teaching proof-based mathematics. We conclude by discussing future considerations for integrating generative AI into proof-based mathematics instruction.
Testing and benchmarking emerging supercomputers via the MFC flow solver
Venue: arXiv cs.DC 2025
Year: 2025

Deploying new supercomputers requires testing and evaluation via application codes. Portable, user-friendly tools enable evaluation, and the Multicomponent Flow Code (MFC), a computational fluid dynamics (CFD) code, addresses this need. MFC is adorned with a toolchain that automates input generation, compilation, batch job submission, regression testing, and benchmarking. The toolchain design enables users to evaluate compiler-hardware combinations for correctness and performance with limited software engineering experience. As with other PDE solvers, wall time per spatially discretized grid point serves as a figure of merit. We present MFC benchmarking results for five generations of NVIDIA GPUs, three generations of AMD GPUs, and various CPU architectures, utilizing Intel, Cray, NVIDIA, AMD, and GNU compilers. These tests have revealed compiler bugs and regressions on recent machines such as Frontier and El Capitan. MFC has benchmarked approximately 50 compute devices and 5 flagship supercomputers.
Modeling the Carbon Footprint of HPC: The Top 500 and EasyC
Venue: arXiv cs.DC 2025
Year: 2025

Climate change is a critical concern for HPC systems, but GHG protocol carbon-emission accounting methodologies are difficult for a single system, and effectively infeasible for a collection of systems. As a result, there is no HPC-wide carbon reporting, and even the largest HPC sites do not do GHG protocol reporting. We assess the carbon footprint of HPC, focusing on the Top 500 systems. The key challenge lies in modeling the carbon footprint with limited data availability. With the disclosed top500 website data, and using a new tool, EasyC, we were able to model the operational carbon of 391 HPC systems and the embodied carbon of 283 HPC systems. We further show how this coverage can be enhanced by exploiting additional public information. With improved coverage, then interpolation is used to produce the first carbon footprint estimates of the Top 500 HPC systems. They are 1.4 million MT CO2e operational carbon (1 Year) and 1.9 million MT CO2e embodied carbon. We also project how the Top 500's carbon footprint will increase through 2030. A key enabler is the EasyC tool which models carbon footprint with only a few data metrics. We explore availability of data and enhancement, showing that coverage can be increased to 98% of Top 500 systems for operational and 80.8% of the systems for embodied emissions.
Hardness of Dynamic Core and Truss Decompositions
Venue: arXiv cs.DS 2025
Year: 2025

The k-core of a graph is its maximal subgraph with minimum degree at least k, and the core value of a vertex u is the largest k for which u is contained in the k-core of the graph. Among cohesive subgraphs, k-core and its variants have received a lot of attention recently, particularly on dynamic graphs, as reported by Hanauer, Henzinger, and Schulz in their recent survey on dynamic graph algorithms. We answer questions on k-core stated in the survey, proving that there is no efficient dynamic algorithm for k-core or to find (2 - ε)-approximations for the core values, unless we can improve decade-long state-of-the-art algorithms in many areas including matrix multiplication and satisfiability, based on the established OMv and SETH conjectures. Some of our results show that there is no dynamic algorithm for k-core asymptotically faster than the trivial ones. This explains why most recent research papers in this area focus not on a generic efficient dynamic algorithm, but on finding a bounded algorithm, which is fast when few core values change per update. However, we also prove that such bounded algorithms do not exist, based on the OMv conjecture. We present lower bounds also for a directed version of the problem, and for the edge variant of the problem, known as k-truss. On the positive side, we present a polylogarithmic dynamic algorithm for 2-core.