OpenRaiser/Intern-Atlas huggingface dataset:
Multifaceted Exploration of Spatial Openness in Rental Housing: A Big Data Analysis in Tokyo's 23 Wards
Venue: arXiv cs.CV 2025
Year: 2025

Understanding spatial openness is vital for improving residential quality and design; however, studies often treat its influencing factors separately. This study developed a quantitative framework to evaluate the spatial openness in housing from two- (2D) and three- (3D) dimensional perspectives. Using data from 4,004 rental units in Tokyo's 23 wards, we examined the temporal and spatial variations in openness and its relationship with rent and housing attributes. 2D openness was computed via planar visibility using visibility graph analysis (VGA) from floor plans, whereas 3D openness was derived from interior images analysed using Mask2Former, a semantic segmentation model that identifies walls, ceilings, floors, and windows. The results showed an increase in living room visibility and a 1990s peak in overall openness. Spatial analyses revealed partial correlations among openness, rent, and building characteristics, reflecting urban redevelopment trends. Although the 2D and 3D openness indicators were not directly correlated, higher openness tended to correspond to higher rent. The impression scores predicted by the existing models were only weakly related to openness, suggesting that the interior design and furniture more strongly shape perceived space. This study offers a new multidimensional data-driven framework for quantifying residential spatial openness and linking it with urban and market dynamics.
Analyzing Crime Discourse in U.S. Metropolitan Communities on Reddit: Trends, Influences, and Insights
Venue: arXiv cs.SI 2025
Year: 2025

The relationship between crime and the media has long been a focal point of academic research, with traditional media playing a significant role in shaping public perceptions of safety and community well-being. However, the advent of social media has introduced a new dimension to this discourse, offering unique platforms for user-driven discussions. Despite the prominence of social media, research examining its impact on crime-related discourse remains limited. This paper investigates crime-related discussions across Reddit communities representing 384 Metropolitan Areas in the United States. By analyzing user submissions, we identify key trends in crime discourse, including the higher prevalence of such discussions in larger metropolitan areas and communities with more liberal political leanings. Interestingly, we find that reported crime rates do not strongly influence the frequency or intensity of these discussions. These findings provide novel insights into how social media platforms, like Reddit, shape public narratives around crime, highlighting the growing need to examine digital spaces as influential mediators of public perception.
Investigating Spatial Attention Bias in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models have demonstrated remarkable capabilities in understanding visual content, yet systematic biases in their spatial processing remain largely unexplored. This work identifies and characterizes a systematic spatial attention bias where VLMs consistently prioritize describing left-positioned content before right-positioned content in horizontally concatenated images. Through controlled experiments on image pairs using both open-source and closed-source models, we demonstrate that this bias persists across different architectures, with models describing left-positioned content first in approximately 97% of cases under neutral prompting conditions. Testing on an Arabic-finetuned model reveals that the bias persists despite right-to-left language training, ruling out language reading direction as the primary cause. Investigation of training dataset annotation guidelines from PixMo and Visual Genome reveals no explicit left-first ordering instructions, suggesting the bias is consistent with architectural factors rather than explicit training data instructions. These findings reveal fundamental limitations in how current VLMs process spatial information.
The Social Blindspot in Human-AI Collaboration: How Undetected AI Personas Reshape Team Dynamics
Venue: arXiv cs.HC 2025
Year: 2025

As generative AI systems become increasingly embedded in collaborative work, they are evolving from visible tools into human-like communicative actors that participate socially rather than merely providing information. Yet little is known about how such agents shape team dynamics when their artificial nature is not recognised, a growing concern as human-like AI is deployed at scale in education, organisations, and civic contexts where collaboration underpins collective outcomes. In a large-scale mixed-design experiment (N = 905), we examined how AI teammates with distinct communicative personas, supportive or contrarian, affected collaboration across analytical, creative, and ethical tasks. Participants worked in triads that were fully human or hybrid human-AI teams, without being informed of AI involvement. Results show that participants had limited ability to detect AI teammates, yet AI personas exerted robust social effects. Contrarian personas reduced psychological safety and discussion quality, whereas supportive personas improved discussion quality without affecting safety. These effects persisted after accounting for individual differences in detectability, revealing a dissociation between influence and awareness that we term the social blindspot. Linguistic analyses confirmed that personas were enacted through systematic differences in affective and relational language, with partial mediation for discussion quality but largely direct effects on psychological safety. Together, the findings demonstrate that AI systems can tacitly regulate collaborative norms through persona-level cues, even when users remain unaware of their presence. We argue that persona design constitutes a form of social governance in hybrid teams, with implications for the responsible deployment of AI in collective settings.
Sync Without Guesswork: Incomplete Time Series Alignment
Venue: arXiv cs.DB 2025
Year: 2025

Multivariate time series alignment is critical for ensuring coherent analysis across variables, but missing values and timestamp inconsistencies make this task highly challenging. Existing approaches often rely on prior imputation, which can introduce errors and lead to suboptimal alignments. To address these limitations, we propose a constraint-based alignment framework for incomplete multivariate time series that avoids imputation and ensures temporal and structural consistency. We further design efficient approximation algorithms to balance accuracy and scalability. Experiments on multiple real-world datasets demonstrate that our approach achieves superior alignment quality compared to existing methods under varying missing rates. Our contributions include: (1) formally defining incomplete multiple temporal data alignment problem; (2) proposing three approximation algorithms balancing accuracy and efficiency; and (3) validating our approach on diverse real-world datasets, where it consistently outperforms existing methods in alignment accuracy and the number of aligned tuples.
Spectral Discrepancy and Cross-modal Semantic Consistency Learning for Object Detection in Hyperspectral Image
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral images with high spectral resolution provide new insights into recognizing subtle differences in similar substances. However, object detection in hyperspectral images faces significant challenges in intra- and inter-class similarity due to the spatial differences in hyperspectral inter-bands and unavoidable interferences, e.g., sensor noises and illumination. To alleviate the hyperspectral inter-bands inconsistencies and redundancy, we propose a novel network termed \textbf{S}pectral \textbf{D}iscrepancy and \textbf{C}ross-\textbf{M}odal semantic consistency learning (SDCM), which facilitates the extraction of consistent information across a wide range of hyperspectral bands while utilizing the spectral dimension to pinpoint regions of interest. Specifically, we leverage a semantic consistency learning (SCL) module that utilizes inter-band contextual cues to diminish the heterogeneity of information among bands, yielding highly coherent spectral dimension representations. On the other hand, we incorporate a spectral gated generator (SGG) into the framework that filters out the redundant data inherent in hyperspectral information based on the importance of the bands. Then, we design the spectral discrepancy aware (SDA) module to enrich the semantic representation of high-level information by extracting pixel-level spectral features. Extensive experiments on two hyperspectral datasets demonstrate that our proposed method achieves state-of-the-art performance when compared with other ones.
Color, Sentiment, and Structure: A Comparative Study of Instagram Marketing Across Economies
Venue: arXiv cs.CY 2025
Year: 2025

Instagram has become a key platform for global food brands to engage diverse audiences through visual storytelling. While previous research emphasizes content-based strategies, this study bridges the gap between content and context by examining how aesthetic elements -- such as dominant image colors and caption sentiment -- and structural factors like GDP, population, and obesity rates collectively shape consumer engagement. Using a multimodal analysis of Instagram posts from major food outlets across developed and developing countries, we assess how color schemes and sentiment influence key engagement metrics. We then extend this analysis with regression modeling to evaluate how these macroeconomic and demographic variables moderate engagement. Our results reveal that engagement patterns vary widely across regions. In developing countries, color combinations like off-white and green significantly enhance interactions, and GDP is a strong positive predictor of engagement. Conversely, in developed countries, a larger population boosts engagement while higher GDP correlates with reduced user attention. Obesity rates show a mixed influence, moderately enhancing likes in some regions while lowering comments in others. These findings highlight the critical need for localized digital marketing strategies that align content design with structural realities to optimize audience engagement.
Asynchronous Pipeline Parallelism for Real-Time Multilingual Lip Synchronization in Video Communication Systems
Venue: arXiv cs.MM 2025
Year: 2025

This paper introduces a parallel and asynchronous Transformer framework designed for efficient and accurate multilingual lip synchronization in real-time video conferencing systems. The proposed architecture integrates translation, speech processing, and lip-synchronization modules within a pipeline-parallel design that enables concurrent module execution through message-queue-based decoupling, reducing end-to-end latency by up to 3.1 times compared to sequential approaches. To enhance computational efficiency and throughput, the inference workflow of each module is optimized through low-level graph compilation, mixed-precision quantization, and hardware-accelerated kernel fusion. These optimizations provide substantial gains in efficiency while preserving model accuracy and visual quality. In addition, a context-adaptive silence-detection component segments the input speech stream at semantically coherent boundaries, improving translation consistency and temporal alignment across languages. Experimental results demonstrate that the proposed parallel architecture outperforms conventional sequential pipelines in processing speed, synchronization stability, and resource utilization. The modular, message-oriented design makes this work applicable to resource-constrained IoT communication scenarios including telemedicine, multilingual kiosks, and remote assistance systems. Overall, this work advances the development of low-latency, resource-efficient multimodal communication frameworks for next-generation AIoT systems.
Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors. With the literature focusing on controlling the four rotors' RPMs directly, this paper aims to control the quadrotor's thrust vector. The RL agent computes the percentage of overall thrust along the quadrotor's z-axis along with the desired Roll ($ϕ$) and Pitch ($θ$) angles. The agent then sends the calculated control signals along with the current quadrotor's Yaw angle ($ψ$) to an attitude PID controller. The PID controller then maps the control signals to motor RPMs. The Soft Actor-Critic algorithm, a model-free off-policy stochastic RL algorithm, was used to train the RL agents. Training results show the faster training time of the proposed thrust vector controller in comparison to the conventional RPM controllers. Simulation results show smoother and more accurate path-following for the proposed thrust vector controller.
Quantization for Vector Search under Streaming Updates
Venue: arXiv cs.DS 2025
Year: 2025

Large-scale vector databases for approximate nearest neighbor (ANN) search typically store a quantized dataset in main memory for fast access, and full precision data on remote disk. State-of-the-art ANN quantization methods are highly data-dependent, rendering them unable to handle point insertions and deletions. This either leads to degraded search quality over time, or forces costly global rebuilds of the entire search index. In this paper, we formally study data-dependent quantization under streaming dataset updates. We formulate a computation model of limited remote disk access and define a dynamic consistency property that guarantees freshness under updates. We use it to obtain the following results: Theoretically, we prove that static data-dependent quantization can be made dynamic with bounded disk I/O per update while retaining formal accuracy guarantees for ANN search. Algorithmically, we develop a practical data-dependent quantization method which is provably dynamically consistent, adapting itself to the dataset as it evolves over time. Our experiments show that the method outperforms baselines in large-scale nearest neighbor search quantization under streaming updates.
Downlink Power Allocation for STAR-RIS-Assisted Cell-Free Massive MIMO with Multi-antenna Users
Venue: arXiv cs.IT 2025
Year: 2025

This paper investigates the downlink power allocation of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted cell-free massive multiple-input multiple-output (MIMO) system with multi-antenna users. We introduce downlink spectral efficiency (SE) and derive novel closed-form SE expressions using linear minimum mean squared error (MMSE) detectors. We also address the downlink power allocation via a sum SE maximization problem framed within an alternating direction method of multipliers (ADMM)-based fractional programming (FP) algorithm. Numerical results demonstrate that systems utilizing multi-antenna users significantly enhance SE, achieving at least a 20% SE increase as the number of antennas increases from one to six. Additionally, our proposed ADMM-based FP algorithm outperforms existing fractional power control approaches, yielding a more than 20% SE increase. These results highlight the necessity for adopting multi-antenna users and efficient power allocation algorithms in STAR-RIS-assisted cell-free massive MIMO systems.
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of deciding whether, and when an organization should replace a trained incumbent model with a challenger relying on newly available features. We develop a unified economic and statistical framework that links learning-curve dynamics, data-acquisition and retraining costs, and discounting of future gains. First, we characterize the optimal switching time in stylized settings and derive closed-form expressions that quantify how horizon length, learning-curve curvature, and cost differentials shape the optimal decision. Second, we propose three practical algorithms: a one-shot baseline, a greedy sequential method, and a look-ahead sequential method. Using a real-world credit-scoring dataset with gradually arriving alternative data, we show that (i) optimal switching times vary systematically with cost parameters and learning-curve behavior, and (ii) the look-ahead sequential method outperforms other methods and is able to approach in value an oracle with full foresight. Finally, we establish finite-sample guarantees, including conditions under which the sequential look-ahead method achieve sublinear regret relative to that oracle. Our results provide an operational blueprint for economically sound model transitions as new data sources become available.
AraToken: Optimizing Arabic Tokenization with Normalization Pipeline and Language Extension for Qwen3
Venue: arXiv cs.CL 2025
Year: 2025

Tokenization is a critical preprocessing step for large language models (LLMs), directly impacting training efficiency and downstream performance. General-purpose tokenizers trained predominantly on English and Latin-script languages exhibit suboptimal performance on morphologically rich languages such as Arabic, resulting in inflated token sequences and reduced compression efficiency. In this work, we present AraToken, an Arabic-optimized tokenizer built on SentencePiece Unigram algorithm with a comprehensive normalization pipeline addressing Arabic-specific orthographic variations including Alif variants, diacritics, and Arabic-Indic numerals. We systematically compare BPE, WordPiece, and SentencePiece algorithms across multiple configurations, demonstrating that SentencePiece with normalization achieves 18% lower fertility (1.199 vs 1.35 tokens/word) compared to unnormalized baselines. Furthermore, we introduce the Language Extension Pipeline (LEP), a method for integrating the optimized tokenizer into Qwen3-0.6B through vocabulary extension with mean subtoken initialization and selective transformer layer unfreezing. Our experiments show that LEP reduces evaluation loss from 8.28 to 2.43 within 800 training steps on 100K Arabic samples. We release our tokenizer, training scripts, and model checkpoints to facilitate Arabic NLP research.
Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks
Venue: arXiv cs.CR 2025
Year: 2025

The move to 6th Generation (6G) wireless networks creates new issues with privacy, scalability, and adaptability. The data-intensive nature of 6G is not handled well by older, centralized network models. A shift toward more secure and decentralized systems is therefore required. A new framework called the Federated Learning-based Decentralized Adaptive Intelligent Transmission Protocol (AITP) is proposed to meet these challenges. The AITP uses the distributed learning of Federated Learning (FL) within a decentralized system. Transmission parameters can be adjusted intelligently in real time. User privacy is maintained by keeping raw data on local edge devices. The protocol's performance was evaluated with mathematical modeling and detailed simulations. It was shown to be superior to traditional non-adaptive and centralized AI methods across several key metrics. These included latency, network throughput, energy efficiency, and robustness. The AITP is presented as a foundational technology for future 6G networks that supports a user-centric, privacy-first design. This study is a step forward for privacy-preserving research in 6G.
On the Universality of Transformer Architectures; How Much Attention Is Enough?
Venue: arXiv cs.LG 2025
Year: 2025

Transformers are crucial across many AI fields, such as large language models, computer vision, and reinforcement learning. This prominence stems from the architecture's perceived universality and scalability compared to alternatives. This work examines the problem of universality in Transformers, reviews recent progress, including architectural refinements such as structural minimality and approximation rates, and surveys state-of-the-art advances that inform both theoretical and practical understanding. Our aim is to clarify what is currently known about Transformers expressiveness, separate robust guarantees from fragile ones, and identify key directions for future theoretical research.
Secret mixtures of experts inside your LLM
Venue: arXiv cs.LG 2025
Year: 2025

Despite being one of the earliest neural network layers, the Multilayer Perceptron (MLP) is arguably one of the least understood parts of the transformer architecture due to its dense computation and lack of easy visualization. This paper seeks to understand the MLP layers in dense LLM models by hypothesizing that these layers secretly approximately perform a sparse computation -- namely, that they can be well approximated by sparsely-activating Mixture of Experts (MoE) layers. Our hypothesis is based on a novel theoretical connection between MoE models and Sparse Autoencoder (SAE) structure in activation space. We empirically validate the hypothesis on pretrained LLMs, and demonstrate that the activation distribution matters -- these results do not hold for Gaussian data, but rather rely crucially on structure in the distribution of neural network activations. Our results shine light on a general principle at play in MLP layers inside LLMs, and give an explanation for the effectiveness of modern MoE-based transformers. Additionally, our experimental explorations suggest new directions for more efficient MoE architecture design based on low-rank routers.
Mitigating Spurious Correlations in NLI via LLM-Synthesized Counterfactuals and Dynamic Balanced Sampling
Venue: arXiv cs.CL 2025
Year: 2025

Natural Language Inference (NLI) models frequently rely on spurious correlations rather than semantic reasoning. Existing mitigation strategies often incur high annotation costs or trigger catastrophic forgetting during fine-tuning. We propose an automated, scalable pipeline to address these limitations. First, we introduce Log-Frequency LMI (LF-LMI) to accurately detect semantic artifacts. Second, we generate a high-quality synthetic contrast set via an LLM-synthesis pipeline with multi-judge verification. Finally, we introduce Dynamic Balanced Sampling, a training strategy that rotates the original data distribution to prevent forgetting. Our method improves consistency on a challenging benchmark from 63.5% to 81.0% while maintaining 88.4% in-domain accuracy, significantly outperforming naive fine-tuning.
APC-GNN++: An Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability for Diabetes Classification
Venue: arXiv cs.LG 2025
Year: 2025

We propose APC-GNN++, an adaptive patient-centric Graph Neural Network for diabetes classification. Our model integrates context-aware edge attention, confidence-guided blending of node features and graph representations, and neighborhood consistency regularization to better capture clinically meaningful relationships between patients. To handle unseen patients, we introduce a mini-graph approach that leverages the nearest neighbors of the new patient, enabling real-time explainable predictions without retraining the global model. We evaluate APC-GNN++ on a real-world diabetes dataset collected from a regional hospital in Algeria and show that it outperforms traditional machine learning models (MLP, Random Forest, XGBoost) and a vanilla GCN, achieving higher test accuracy and macro F1- score. The analysis of node-level confidence scores further reveals how the model balances self-information and graph-based evidence across different patient groups, providing interpretable patient-centric insights. The system is also embedded in a Tkinter-based graphical user interface (GUI) for interactive use by healthcare professionals .