OpenRaiser/Intern-Atlas huggingface dataset:
An Expressive Coalgebraic Modal Logic for Cellular Automata
Venue: arXiv cs.LO 2025
Year: 2025

Cellular automata provide models of parallel computation based on cells, whose connectivity is given by an action of a monoid on the cells. At each step in the computation, every cell is decorated with a state that evolves in discrete steps according to a local update rule, which determines the next state of a cell based on its neighbour's states. In this paper, we consider a coalgebraic view on cellular automata, which does not require typical restrictions, such as uniform neighbourhood connectivity and uniform local rules. Using the coalgebraic view, we devise a behavioural equivalence for cellular automata and a modal logic to reason about their behaviour. We then prove a Hennessy-Milner style theorem, which states that pairs of cells satisfy the same modal formulas exactly if they are identified under cellular behavioural equivalence.
QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis
Venue: arXiv cs.LG 2025
Year: 2025

The Quantum Approximate Optimization Algorithm (QAOA) is a promising variational algorithm for solving combinatorial optimization problems on near-term devices. However, as the number of layers in a QAOA circuit increases, which is correlated with the quality of the solution, the number of parameters to optimize grows linearly. This results in more iterations required by the classical optimizer, which results in an increasing computational burden as more circuit executions are needed. To mitigate this issue, we introduce QAOA-PCA, a novel reparameterization technique that employs Principal Component Analysis (PCA) to reduce the dimensionality of the QAOA parameter space. By extracting principal components from optimized parameters of smaller problem instances, QAOA-PCA facilitates efficient optimization with fewer parameters on larger instances. Our empirical evaluation on the prominent MaxCut problem demonstrates that QAOA-PCA consistently requires fewer iterations than standard QAOA, achieving substantial efficiency gains. While this comes at the cost of a slight reduction in approximation ratio compared to QAOA with the same number of layers, QAOA-PCA almost always outperforms standard QAOA when matched by parameter count. QAOA-PCA strikes a favorable balance between efficiency and performance, reducing optimization overhead without significantly compromising solution quality.
Process Reward Models That Think
Venue: arXiv cs.LG 2025
Year: 2025

Step-by-step verifiers -- also known as process reward models (PRMs) -- are a key ingredient for test-time scaling. PRMs require step-level supervision, making them expensive to train. This work aims to build data-efficient PRMs as verbalized step-wise reward models that verify every step in the solution by generating a verification chain-of-thought (CoT). We propose ThinkPRM, a long CoT verifier fine-tuned on orders of magnitude fewer process labels than those required by discriminative PRMs. Our approach capitalizes on the inherent reasoning abilities of long CoT models, and outperforms LLM-as-a-Judge and discriminative verifiers -- using only 1% of the process labels in PRM800K -- across several challenging benchmarks. Specifically, ThinkPRM beats the baselines on ProcessBench, MATH-500, and AIME '24 under best-of-N selection and reward-guided search. In an out-of-domain evaluation on a subset of GPQA-Diamond and LiveCodeBench, our PRM surpasses discriminative verifiers trained on the full PRM800K by 8% and 4.5%, respectively. Lastly, under the same token budget, ThinkPRM scales up verification compute more effectively compared to LLM-as-a-Judge, outperforming it by 7.2% on a subset of ProcessBench. Our work highlights the value of generative, long CoT PRMs that can scale test-time compute for verification while requiring minimal supervision for training. Our code, data, and models are released at https://github.com/mukhal/thinkprm.
Improving Significant Wave Height Prediction Using Chronos Models
Venue: arXiv cs.LG 2025
Year: 2025

Accurate wave height prediction is critical for maritime safety and coastal resilience, yet conventional physics-based models and traditional machine learning methods face challenges in computational efficiency and nonlinear dynamics modeling. This study introduces Chronos, the first implementation of a large language model (LLM)-powered temporal architecture (Chronos) optimized for wave forecasting. Through advanced temporal pattern recognition applied to historical wave data from three strategically chosen marine zones in the Northwest Pacific basin, our framework achieves multimodal improvements: (1) 14.3% reduction in training time with 2.5x faster inference speed compared to PatchTST baselines, achieving 0.575 mean absolute scaled error (MASE) units; (2) superior short-term forecasting (1-24h) across comprehensive metrics; (3) sustained predictive leadership in extended-range forecasts (1-120h); and (4) demonstrated zero-shot capability maintaining median performance (rank 4/12) against specialized operational models. This LLM-enhanced temporal modeling paradigm establishes a new standard in wave prediction, offering both computationally efficient solutions and a transferable framework for complex geophysical systems modeling.
Snorkeling in dark waters: A longitudinal surface exploration of unique Tor Hidden Services (Extended Version)
Venue: arXiv cs.CR 2025
Year: 2025

The Onion Router (Tor) is a controversial network whose utility is constantly under scrutiny. On the one hand, it allows for anonymous interaction and cooperation of users seeking untraceable navigation on the Internet. This freedom also attracts criminals who aim to thwart law enforcement investigations, e.g., trading illegal products or services such as drugs or weapons. Tor allows delivering content without revealing the actual hosting address, by means of .onion (or hidden) services. Different from regular domains, these services can not be resolved by traditional name services, are not indexed by regular search engines, and they frequently change. This generates uncertainty about the extent and size of the Tor network and the type of content offered. In this work, we present a large-scale analysis of the Tor Network. We leverage our crawler, dubbed Mimir, which automatically collects and visits content linked within the pages to collect a dataset of pages from more than 25k sites. We analyze the topology of the Tor Network, including its depth and reachability from the surface web. We define a set of heuristics to detect the presence of replicated content (mirrors) and show that most of the analyzed content in the Dark Web (82% approx.) is a replica of other content. Also, we train a custom Machine Learning classifier to understand the type of content the hidden services offer. Overall, our study provides new insights into the Tor network, highlighting the importance of initial seeding for focus on specific topics, and optimize the crawling process. We show that previous work on large-scale Tor measurements does not consider the presence of mirrors, which biases their understanding of the Dark Web topology and the distribution of content.
Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral imaging provides detailed spectral information and holds significant potential for monitoring of greenhouse gases (GHGs). However, its application is constrained by limited spatial coverage and infrequent revisit times. In contrast, multispectral imaging offers broader spatial and temporal coverage but often lacks the spectral detail that can enhance GHG detection. To address these challenges, this study proposes a spectral transformer model that synthesizes hyperspectral data from multispectral inputs. The model is pre-trained via a band-wise masked autoencoder and subsequently fine-tuned on spatio-temporally aligned multispectral-hyperspectral image pairs. The resulting synthetic hyperspectral data retain the spatial and temporal benefits of multispectral imagery and improve GHG prediction accuracy relative to using multispectral data alone. This approach effectively bridges the trade-off between spectral resolution and coverage, highlighting its potential to advance atmospheric monitoring by combining the strengths of hyperspectral and multispectral systems with self-supervised deep learning.
Building A Secure Agentic AI Application Leveraging A2A Protocol
Venue: arXiv cs.CR 2025
Year: 2025

As Agentic AI systems evolve from basic workflows to complex multi agent collaboration, robust protocols such as Google's Agent2Agent (A2A) become essential enablers. To foster secure adoption and ensure the reliability of these complex interactions, understanding the secure implementation of A2A is essential. This paper addresses this goal by providing a comprehensive security analysis centered on the A2A protocol. We examine its fundamental elements and operational dynamics, situating it within the framework of agent communication development. Utilizing the MAESTRO framework, specifically designed for AI risks, we apply proactive threat modeling to assess potential security issues in A2A deployments, focusing on aspects such as Agent Card management, task execution integrity, and authentication methodologies. Based on these insights, we recommend practical secure development methodologies and architectural best practices designed to build resilient and effective A2A systems. Our analysis also explores how the synergy between A2A and the Model Context Protocol (MCP) can further enhance secure interoperability. This paper equips developers and architects with the knowledge and practical guidance needed to confidently leverage the A2A protocol for building robust and secure next generation agentic applications.
Burning some myths on privacy properties of social networks against active attacks
Venue: arXiv cs.SI 2025
Year: 2025

This work focuses on showing some arguments addressed to dismantle the extended idea about that social networks completely lacks of privacy properties. We consider the so-called active attacks to the privacy of social networks and the counterpart $(k,\ell)$-anonymity measure, which is used to quantify the privacy satisfied by a social network against active attacks. To this end, we make use of the graph theoretical concept of $k$-metric antidimensional graphs for which the case $k=1$ represents those graphs achieving the worst scenario in privacy whilst considering the $(k,\ell)$-anonymity measure. As a product of our investigation, we present a large number of computational results stating that social networks might not be as insecure as one often thinks. In particular, we develop a large number of experiments on random graphs which show that the number of $1$-metric antidimensional graphs is indeed ridiculously small with respect to the total number of graphs that can be considered. Moreover, we search on several real networks in order to check if they are $1$-metric antidimensional, and obtain that none of them are such. Along the way, we show some theoretical studies on the mathematical properties of the $k$-metric antidimensional graphs for any suitable $k\ge 1$. In addition, we also describe some operations on graphs that are $1$-metric antidimensional so that they get embedded into another larger graphs that are not such, in order to obscure their privacy properties against active attacks.
A Novel Graph Transformer Framework for Gene Regulatory Network Inference
Venue: arXiv cs.LG 2025
Year: 2025

The inference of gene regulatory networks (GRNs) is a foundational stride towards deciphering the fundamentals of complex biological systems. Inferring a possible regulatory link between two genes can be formulated as a link prediction problem. Inference of GRNs via gene coexpression profiling data may not always reflect true biological interactions, as its susceptibility to noise and misrepresenting true biological regulatory relationships. Most GRN inference methods face several challenges in the network reconstruction phase. Therefore, it is important to encode gene expression values, leverege the prior knowledge gained from the available inferred network structures and positional informations of the input network nodes towards inferring a better and more confident GRN network reconstruction. In this paper, we explore the integration of multiple inferred networks to enhance the inference of Gene Regulatory Networks (GRNs). Primarily, we employ autoencoder embeddings to capture gene expression patterns directly from raw data, preserving intricate biological signals. Then, we embed the prior knowledge from GRN structures transforming them into a text-like representation using random walks, which are then encoded with a masked language model, BERT, to generate global embeddings for each gene across all networks. Additionally, we embed the positional encodings of the input gene networks to better identify the position of each unique gene within the graph. These embeddings are integrated into graph transformer-based model, termed GT-GRN, for GRN inference. The GT-GRN model effectively utilizes the topological structure of the ground truth network while incorporating the enriched encoded information. Experimental results demonstrate that GT-GRN significantly outperforms existing GRN inference methods, achieving superior accuracy and highlighting the robustness of our approach.
Seeing The Words: Evaluating AI-generated Biblical Art
Venue: arXiv cs.CY 2025
Year: 2025

The past years witnessed a significant amount of Artificial Intelligence (AI) tools that can generate images from texts. This triggers the discussion of whether AI can generate accurate images using text from the Bible with respect to the corresponding biblical contexts and backgrounds. Despite some existing attempts at a small scale, little work has been done to systematically evaluate these generated images. In this work, we provide a large dataset of over 7K images using biblical text as prompts. These images were evaluated with multiple neural network-based tools on various aspects. We provide an assessment of accuracy and some analysis from the perspective of religion and aesthetics. Finally, we discuss the use of the generated images and reflect on the performance of the AI generators.
Molecular Communication Channel as a Physical Reservoir Computer
Venue: arXiv cs.ET 2025
Year: 2025

Molecular Communication (MC) channels are characterized by significant memory and nonlinear dynamics arising from diffusion and receptor kinetics. While often viewed as impairments to reliable data transmission, this work introduces a paradigm shift by reconceptualizing these intrinsic physical properties as computational resources. We frame a canonical point-to-point MC channel, comprising ligand diffusion and reversible ligand-receptor binding at a spherical receiver, as a Physical Reservoir Computer (PRC). Utilizing deterministic mean-field modeling and particle-based spatial stochastic simulations, we demonstrate the MC system's inherent capability for complex temporal information processing on standard chaotic time-series benchmarks. We comprehensively evaluate performance using both task-specific Normalized Root Mean Square Error (NRMSE) and the task-independent Information Processing Capacity (IPC). Our results reveal a non-monotonic dependence of computational power on key biophysical parameters (receptor kinetic rates, diffusion coefficient, and transmitter-receiver distance), identifying optimal operational regimes where memory and nonlinearity are balanced. These findings establish the MC channel as a viable computational substrate, paving the way for novel architectures in \emph{wetware} artificial intelligence.
What Makes for a Good Saliency Map? Comparing Strategies for Evaluating Saliency Maps in Explainable AI (XAI)
Venue: arXiv cs.HC 2025
Year: 2025

Saliency maps are a popular approach for explaining classifications of (convolutional) neural networks. However, it remains an open question as to how best to evaluate salience maps, with three families of evaluation methods commonly being used: subjective user measures, objective user measures, and mathematical metrics. We examine three of the most popular saliency map approaches (viz., LIME, Grad-CAM, and Guided Backpropagation) in a between subject study (N=166) across these families of evaluation methods. We test 1) for subjective measures, if the maps differ with respect to user trust and satisfaction; 2) for objective measures, if the maps increase users' abilities and thus understanding of a model; 3) for mathematical metrics, which map achieves the best ratings across metrics; and 4) whether the mathematical metrics can be associated with objective user measures. To our knowledge, our study is the first to compare several salience maps across all these evaluation methods$-$with the finding that they do not agree in their assessment (i.e., there was no difference concerning trust and satisfaction, Grad-CAM improved users' abilities best, and Guided Backpropagation had the most favorable mathematical metrics). Additionally, we show that some mathematical metrics were associated with user understanding, although this relationship was often counterintuitive. We discuss these findings in light of general debates concerning the complementary use of user studies and mathematical metrics in the evaluation of explainable AI (XAI) approaches.
Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Made by a University Research Lab Using GPU
Venue: arXiv cs.LG 2025
Year: 2025

This paper demonstrates the feasibility of democratizing AI-driven global weather forecasting models among university research groups by leveraging Graphics Processing Units (GPUs) and freely available AI models, such as NVIDIA's FourCastNetv2. FourCastNetv2 is an NVIDIA's advanced neural network for weather prediction and is trained on a 73-channel subset of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset at single levels and different pressure levels. Although the training specifications for FourCastNetv2 are not released to the public, the training documentation of the model's first generation, FourCastNet, is available to all users. The training had 64 A100 GPUs and took 16 hours to complete. Although NVIDIA's models offer significant reductions in both time and cost compared to traditional Numerical Weather Prediction (NWP), reproducing published forecasting results presents ongoing challenges for resource-constrained university research groups with limited GPU availability. We demonstrate both (i) leveraging FourCastNetv2 to create predictions through the designated application programming interface (API) and (ii) utilizing NVIDIA hardware to train the original FourCastNet model. Further, this paper demonstrates the capabilities and limitations of NVIDIA A100's for resource-limited research groups in universities. We also explore data management, training efficiency, and model validation, highlighting the advantages and challenges of using limited high-performance computing resources. Consequently, this paper and its corresponding GitHub materials may serve as an initial guide for other university research groups and courses related to machine learning, climate science, and data science to develop research and education programs on AI weather forecasting, and hence help democratize the AI NWP in the digital economy.
DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs
Venue: arXiv cs.CV 2025
Year: 2025

We present DyMU, an efficient, training-free framework that dynamically reduces the computational burden of vision-language models (VLMs) while maintaining high task performance. Our approach comprises two key components. First, Dynamic Token Merging (DToMe) reduces the number of visual token embeddings by merging similar tokens based on image complexity, addressing the inherent inefficiency of fixed-length outputs in vision transformers. Second, Virtual Token Unmerging (VTU) simulates the expected token sequence for large language models (LLMs) by efficiently reconstructing the attention dynamics of a full sequence, thus preserving the downstream performance without additional fine-tuning. Unlike previous approaches, our method dynamically adapts token compression to the content of the image and operates completely training-free, making it readily applicable to most state-of-the-art VLM architectures. Extensive experiments on image and video understanding tasks demonstrate that DyMU can reduce the average visual token count by 32%-85% while achieving comparable performance to full-length models across diverse VLM architectures, including the recently popularized AnyRes-based visual encoders. Furthermore, through qualitative analyses, we demonstrate that DToMe effectively adapts token reduction based on image complexity and, unlike existing systems, provides users more control over computational costs. Project page: https://mikewangwzhl.github.io/dymu/.
Enhanced load balancing technique for SDN controllers: A multi-threshold approach with migration of switches
Venue: arXiv cs.NI 2025
Year: 2025

Deploying multiple controllers in the control panel of software-defined networks increases scalability, availability, and performance, but it also brings challenges, such as controller overload. To address this, load-balancing techniques are employed in software-defined networks. Controller load balancing can be categorized into two main approaches: (1) single-level thresholds and (2) multi-level thresholds. However, previous studies have predominantly relied on single-level thresholds, which result in an imprecise classification of controllers or have assumed uniform controller capacities in multi-level threshold methods. This study explores controller load balancing with a focus on utilizing multi-level thresholds to accurately assess controller status. Switch migration operations are utilized to achieve load balancing, considering factors such as the degree of load imbalance of the target controller and migration efficiency. This includes evaluating the post-migration status of the target controller and the distance between the migrating switch and the target controller to select the appropriate target controller and migrating switch. The proposed scheme reduces controller response time, migration costs, communication overhead, and throughput rate. Results demonstrate that our scheme outperforms others regarding response time and overall performance.
Psychological Effect of AI driven marketing tools for beauty/facial feature enhancement
Venue: arXiv cs.HC 2025
Year: 2025

AI-powered facial assessment tools are reshaping how individuals evaluate appearance and internalize social judgments. This study examines the psychological impact of such tools on self-objectification, self-esteem, and emotional responses, with attention to gender differences. Two samples used distinct versions of a facial analysis tool: one overtly critical (N=75; M=22.9 years), and another more neutral (N=51; M=19.9 years). Participants completed validated self-objectification and self-esteem scales and custom items measuring emotion, digital/physical appearance enhancement (DAE, PAEE), and perceived social emotion (PSE). Results revealed consistent links between high self-objectification, low self-esteem, and increased appearance enhancement behaviors across both versions. Despite softer framing, the newer tool still evoked negative emotional responses (U=1466.5, p=0.013), indicating implicit feedback may reinforce appearance-related insecurities. Gender differences emerged in DAE (p=0.025) and PSE (p<0.001), with females more prone to digital enhancement and less likely to perceive emotional impact in others. These findings reveal how AI tools may unintentionally reinforce and amplify existing social biases and underscore the critical need for responsible AI design and development. Future research will investigate how human ideologies embedded in the training data of such tools shape their evaluative outputs, and how these, in turn, influence user attitudes and decisions.