OpenRaiser/Intern-Atlas huggingface dataset:
The Forgotten Code: Validating a Century-Old Translation System with AI
Venue: arXiv cs.CL 2025
Year: 2025

A pioneering rule-based mechanical translation system (precursor of modern RBMTs) was first presented in December 1929 by its inventor, Federico Pucci, who later published the full method in a book titled "Il traduttore meccanico ed il metodo per corrispondersi fra Europei conoscendo ciascuno solo la propria lingua: Parte I", in Salerno (Italy), in 1931. This study illustrates how AI breathes new life into the system of international keys and ideograms devised by Pucci to translate from/into any Romance language (at least as a first step). The methodology involves having the AIs retranslate, following Pucci's method, the two text excerpts originally translated in 1931 and clearly documented in his publication: a passage from Dante's La Vita Nuova, translated from Italian into French, and a passage from Voltaire's Zadig, translated from French into Italian. The result is notable: the two texts, translated 94 years apart using the same method--by Pucci in 1931 and by AIs in 2025--show a low average difference, with only minor variations observed. With Pucci's system thus validated, it became feasible to have the AIs reproduce the excerpts in English, Spanish, and German according to his method. The results were consistent, and Pucci--via Artificial Intelligence--was tasked with translating more modern and technical texts, thereby reviving, nearly a century later, an invention that had remained almost entirely unknown and never applied beyond its creator, now brought to wider attention and opened to possible experimentation. Such a demonstration would not only affirm Pucci's historical status but also place him among the precursors and intellectual contributors to machine translation, whose work merits examination alongside figures such as Troyanskij, Booth, and Weaver, with possible consequences for how the history of the field is understood.
Enhancing Fitness Movement Recognition with Attention Mechanism and Pre-Trained Feature Extractors
Venue: arXiv cs.CV 2025
Year: 2025

Fitness movement recognition, a focused subdomain of human activity recognition (HAR), plays a vital role in health monitoring, rehabilitation, and personalized fitness training by enabling automated exercise classification from video data. However, many existing deep learning approaches rely on computationally intensive 3D models, limiting their feasibility in real-time or resource-constrained settings. In this paper, we present a lightweight and effective framework that integrates pre-trained 2D Convolutional Neural Networks (CNNs) such as ResNet50, EfficientNet, and Vision Transformers (ViT) with a Long Short-Term Memory (LSTM) network enhanced by spatial attention. These models efficiently extract spatial features while the LSTM captures temporal dependencies, and the attention mechanism emphasizes informative segments. We evaluate the framework on a curated subset of the UCF101 dataset, achieving a peak accuracy of 93.34\% with the ResNet50-based configuration. Comparative results demonstrate the superiority of our approach over several state-of-the-art HAR systems. The proposed method offers a scalable and real-time-capable solution for fitness activity recognition with broader applications in vision-based health and activity monitoring.
A Novel Coded Caching Scheme for Partially Cooperative Device-to-Device Networks
Venue: arXiv cs.IT 2025
Year: 2025

Device-to-device (D2D) communication is one of the most promising techniques for future wireless cellular communication systems. This paper considers coded caching in a partially cooperative wireless D2D network, where only a subset of users transmit during delivery, while all users request files. The non-transmitting users are referred to as selfish users. All existing schemes that do not require knowledge of the identity of selfish users before content placement are limited to the high-memory regime, particularly when the number of selfish users is large. We propose a novel coded caching scheme for a partially cooperative D2D network that operates in all feasible memory regimes, regardless of the number of selfish users. We also derive a lower bound on the transmission load of a partially cooperative D2D coded caching scheme. Using this bound, the proposed scheme is shown to be optimal in the high-memory regime.
Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery
Venue: arXiv cs.CV 2025
Year: 2025

Unsupervised multi-object discovery (MOD) aims to detect and localize distinct object instances in visual scenes without any form of human supervision. Recent approaches leverage object-centric learning (OCL) and motion cues from video to identify individual objects. However, these approaches use supervision to generate pseudo labels to train the OCL model. We address this limitation with MR-DINOSAUR -- Motion-Refined DINOSAUR -- a minimalistic unsupervised approach that extends the self-supervised pre-trained OCL model, DINOSAUR, to the task of unsupervised multi-object discovery. We generate high-quality unsupervised pseudo labels by retrieving video frames without camera motion for which we perform motion segmentation of unsupervised optical flow. We refine DINOSAUR's slot representations using these pseudo labels and train a slot deactivation module to assign slots to foreground and background. Despite its conceptual simplicity, MR-DINOSAUR achieves strong multi-object discovery results on the TRI-PD and KITTI datasets, outperforming the previous state of the art despite being fully unsupervised.
Surrogate Benchmarks for Model Merging Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Model merging techniques aim to integrate the abilities of multiple models into a single model. Most model merging techniques have hyperparameters, and their setting affects the performance of the merged model. Because several existing works show that tuning hyperparameters in model merging can enhance the merging outcome, developing hyperparameter optimization algorithms for model merging is a promising direction. However, its optimization process is computationally expensive, particularly in merging LLMs. In this work, we develop surrogate benchmarks for optimization of the merging hyperparameters to realize algorithm development and performance comparison at low cost. We define two search spaces and collect data samples to construct surrogate models to predict the performance of a merged model from a hyperparameter. We demonstrate that our benchmarks can predict the performance of merged models well and simulate optimization algorithm behaviors.
Synthetic Founders: AI-Generated Social Simulations for Startup Validation Research in Computational Social Science
Venue: arXiv cs.MA 2025
Year: 2025

We present a comparative docking experiment that aligns human-subject interview data with large language model (LLM)-driven synthetic personas to evaluate fidelity, divergence, and blind spots in AI-enabled simulation. Fifteen early-stage startup founders were interviewed about their hopes and concerns regarding AI-powered validation, and the same protocol was replicated with AI-generated founder and investor personas. A structured thematic synthesis revealed four categories of outcomes: (1) Convergent themes - commitment-based demand signals, black-box trust barriers, and efficiency gains were consistently emphasized across both datasets; (2) Partial overlaps - founders worried about outliers being averaged away and the stress of real customer validation, while synthetic personas highlighted irrational blind spots and framed AI as a psychological buffer; (3) Human-only themes - relational and advocacy value from early customer engagement and skepticism toward moonshot markets; and (4) Synthetic-only themes - amplified false positives and trauma blind spots, where AI may overstate adoption potential by missing negative historical experiences. We interpret this comparative framework as evidence that LLM-driven personas constitute a form of hybrid social simulation: more linguistically expressive and adaptable than traditional rule-based agents, yet bounded by the absence of lived history and relational consequence. Rather than replacing empirical studies, we argue they function as a complementary simulation category - capable of extending hypothesis space, accelerating exploratory validation, and clarifying the boundaries of cognitive realism in computational social science.
Chatbot Deployment Considerations for Application-Agnostic Human-Machine Dialogues
Venue: arXiv cs.CY 2025
Year: 2025

Automatic conversation systems based on natural language responses are becoming ubiquitous, in part, due to major advances in computational linguistics and machine learning. The easy access to robust and affordable platforms are causing companies to have an unprecedented rush to adopt chatbot technologies for customer service and support. However, this rush has caused judgment lapses when releasing chatbot technologies into production systems. This paper aims to shed light on basic, elemental, considerations that technologists must consider before deploying a chatbot. Our approach takes one particular case to draw lessons for those considering the implementation of chatbots. By looking at this case-study, we aim to call for consideration of societal values as a paramount factor before deploying a chatbot and consider the societal implications of releasing these types of systems.
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Venue: arXiv cs.AI 2025
Year: 2025

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.
Mentality: A Mamba-based Approach towards Foundation Models for EEG
Venue: arXiv cs.LG 2025
Year: 2025

This work explores the potential of foundation models, specifically a Mamba-based selective state space model, for enhancing EEG analysis in neurological disorder diagnosis. EEG, crucial for diagnosing conditions like epilepsy, presents significant challenges due to its noisy, high-dimensional, and nonlinear nature. Traditional machine learning methods have made advances in automating EEG analysis but often fail to capture its complex spatio-temporal dynamics. Recent advances in deep learning, particularly in sequence modeling, offer new avenues for creating more generalized and expressive models capable of handling such complexities. By training a Mamba-based model on a large dataset containing seizure and non-seizure EEG recordings through a self-supervised reconstruction task followed by a seizure detection task, we demonstrate the model's effectiveness, achieving an AUROC of 0.72 on a held-out test set. This approach marks a significant step toward developing large-scale, clinically applicable foundation models for EEG data analysis.
Synthetic generation of online social networks through homophily
Venue: arXiv cs.SI 2025
Year: 2025

Online social networks (OSNs) have become increasingly relevant for studying social behavior and information diffusion. Nevertheless, they are limited by restricted access to real OSN data due to privacy, legal, and platform-related constraints. In response, synthetic social networks serve as a viable approach to support controlled experimentation, but current generators reproduce only topology and overlook attribute-driven homophily and semantic realism. This work proposes a homophily-based algorithm that produces synthetic microblogging social networks such as X. The model creates a social graph for a given number of users, integrating semantic affinity among user attributes, stochastic variation in link formation, triadic closure to foster clustering, and long-range connections to ensure global reachability. A systematic grid search is used to calibrate five hyperparameters (affinity strength, noise, closure probability, distant link probability, and candidate pool size) for reaching five structural values observed in real social networks (density, clustering coefficient, LCC proportion, normalized shortest path, and modularity). The framework is validated by generating synthetic OSNs at four scales (10^3-10^6 nodes), and benchmarking them against a real-world Bluesky network comprising 4 million users. Comparative results show that the framework reliably reproduces the structural properties of the real network. Overall, the framework outperforms leading importance-sampling techniques applied to the same baseline. The generated graphs capture topological realism and yield attribute-driven communities that align with sociological expectations, providing a realistic, scalable testbed that liberates social researchers from relying on live digital platforms.
Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers
Venue: arXiv cs.RO 2025
Year: 2025

Autonomously controlling quadrotors in large-scale subterranean environments is applicable to many areas such as environmental surveying, mining operations, and search and rescue. Learning-based controllers represent an appealing approach to autonomy, but are known to not generalize well to `out-of-distribution' environments not encountered during training. In this work, we train a normalizing flow-based prior over the environment, which provides a measure of how far out-of-distribution the quadrotor is at any given time. We use this measure as a runtime monitor, allowing us to switch between a learning-based controller and a safe controller when we are sufficiently out-of-distribution. Our methods are benchmarked on a point-to-point navigation task in a simulated 3D cave environment based on real-world point cloud data from the DARPA Subterranean Challenge Final Event Dataset. Our experimental results show that our combined controller simultaneously possesses the liveness of the learning-based controller (completing the task quickly) and the safety of the safety controller (avoiding collision).
GPS Spoofing Attacks on Automated Frequency Coordination System in Wi-Fi 6E and Beyond
Venue: arXiv cs.NI 2025
Year: 2025

The 6 GHz spectrum, recently opened for unlicensed use under Wi-Fi 6E and Wi-Fi 7, overlaps with frequencies used by mission-critical incumbent systems such as public safety communications and utility infrastructure. To prevent interference, the FCC mandates the use of Automated Frequency Coordination (AFC) systems, which assign safe frequency and power levels based on Wi-Fi Access Point (AP)-reported locations. In this work, we demonstrate that GPS-based location reporting, which Wi-Fi APs use, can be spoofed using inexpensive, off-the-shelf radio equipment. This enables attackers to manipulate AP behavior, gain unauthorized spectrum access, cause harmful interference, or disable APs entirely by spoofing them into foreign locations. We validate these attacks in a controlled lab setting against a commercial AP and evaluate a commercial AFC system under spoofed scenarios. Our findings highlight critical gaps in the security assumptions of AFC and motivate the need for stronger location integrity protections.
Ensemble Learning for Healthcare: A Comparative Analysis of Hybrid Voting and Ensemble Stacking in Obesity Risk Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Obesity is a critical global health issue driven by dietary, physiological, and environmental factors, and is strongly associated with chronic diseases such as diabetes, cardiovascular disorders, and cancer. Machine learning has emerged as a promising approach for early obesity risk prediction, yet a comparative evaluation of ensemble techniques -- particularly hybrid majority voting and ensemble stacking -- remains limited. This study aims to compare hybrid majority voting and ensemble stacking methods for obesity risk prediction, identifying which approach delivers higher accuracy and efficiency. The analysis seeks to highlight the complementary strengths of these ensemble techniques in guiding better predictive model selection for healthcare applications. Two datasets were utilized to evaluate three ensemble models: Majority Hard Voting, Weighted Hard Voting, and Stacking (with a Multi-Layer Perceptron as meta-classifier). A pool of nine Machine Learning (ML) algorithms, evaluated across a total of 50 hyperparameter configurations, was analyzed to identify the top three models to serve as base learners for the ensemble methods. Preprocessing steps involved dataset balancing, and outlier detection, and model performance was evaluated using Accuracy and F1-Score. On Dataset-1, weighted hard voting and stacking achieved nearly identical performance (Accuracy: 0.920304, F1: 0.920070), outperforming majority hard voting. On Dataset-2, stacking demonstrated superior results (Accuracy: 0.989837, F1: 0.989825) compared to majority hard voting (Accuracy: 0.981707, F1: 0.981675) and weighted hard voting, which showed the lowest performance. The findings confirm that ensemble stacking provides stronger predictive capability, particularly for complex data distributions, while hybrid majority voting remains a robust alternative.
SSVD: Structured SVD for Parameter-Efficient Fine-Tuning and Benchmarking under Domain Shift in ASR
Venue: arXiv cs.CL 2025
Year: 2025

Parameter-efficient fine-tuning (PEFT) has emerged as a scalable solution for adapting large foundation models. While low-rank adaptation (LoRA) is widely used in speech applications, its state-of-the-art variants, e.g., VeRA, DoRA, PiSSA, and SVFT, are developed mainly for language and vision tasks, with limited validation in speech. This work presents the first comprehensive integration and benchmarking of these PEFT methods within ESPnet. We further introduce structured SVD-guided (SSVD) fine-tuning, which selectively rotates input-associated right singular vectors while keeping output-associated vectors fixed to preserve semantic mappings. This design enables robust domain adaptation with minimal trainable parameters and improved efficiency. We evaluate all methods on domain-shifted speech recognition tasks, including child speech and dialectal variation, across model scales from 0.1B to 2B. All implementations are released in ESPnet to support reproducibility and future work.
The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier
Venue: arXiv cs.CY 2025
Year: 2025

Despite extensive investment in artificial intelligence, 95% of enterprises report no measurable profit impact from AI deployments (MIT, 2025). In this theoretical paper, we argue that this gap reflects paradigmatic lock-in that channels AI into incremental optimization rather than structural transformation. Using a cross-case analysis, we propose a 2x2 framework that reconceptualizes AI strategy along two independent dimensions: the degree of transformation achieved (incremental to transformational) and the treatment of human contribution (reduced to amplified). The framework surfaces four patterns now dominant in practice: individual augmentation, process automation, workforce substitution, and a less deployed frontier of collaborative intelligence. Evidence shows that the first three dimensions reinforce legacy work models and yield localized gains without durable value capture. Realizing collaborative intelligence requires three mechanisms: complementarity (pairing distinct human and machine strengths), co-evolution (mutual adaptation through interaction), and boundary-setting (human determination of ethical and strategic parameters). Complementarity and boundary-setting are observable in regulated and high-stakes domains; co-evolution is largely absent, which helps explain limited system-level impact. Our findings in a case study analysis illustrated that advancing toward collaborative intelligence requires material restructuring of roles, governance, and data architecture rather than additional tools. The framework reframes AI transformation as an organizational design challenge: moving from optimizing the division of labor between humans and machines to architecting their convergence, with implications for operating models, workforce development, and the future of work.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation
Venue: arXiv cs.CL 2025
Year: 2025

We present an end-to-end, self-evolving adversarial workflow for long-context Question-Answer (QA) Generation in Arabic. By orchestrating multiple specialized LVLMs: a question generator, an evaluator, and a swarm of answer generators, our system iteratively refines its own performance without any human intervention. Starting from raw, multi-page Arabic documents across diverse domains, the question generator produces fine-grained, context-aware queries to be tackled by the answer generator swarm, and the evaluator assesses and feeds back quality metrics. This closed-loop cycle enables continuous learning: low-confidence outputs trigger automated re-generation and model updates, progressively enhancing question difficulty and relevance. Moreover, we set the quality metrics as a tunable hyperparameter, enabling question generation at controllable and customizable difficulty levels. We release AraLongBench, a large-scale Arabic benchmark of single- and multi-page challenges spanning hundreds of pages, and demonstrate that our self-evolving workflow substantially outperform static pipelines, markedly boosting the long-context comprehension capabilities of leading Arabic Large Vision Language Models (LVLMs). Lastly, we also meticulously architect a fully automated agentic workflow for long-context Arabic document collection.