OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Image Restoration through Learning Context-Rich and Detail-Accurate Features
Venue: arXiv cs.CV 2025
Year: 2025

Image restoration involves recovering high-quality images from their corrupted versions, requiring a nuanced balance between spatial details and contextual information. While certain methods address this balance, they predominantly emphasize spatial aspects, neglecting frequency variation comprehension. In this paper, we present a multi-scale design that optimally balances these competing objectives, seamlessly integrating spatial and frequency domain knowledge to selectively recover the most informative information. Specifically, we develop a hybrid scale frequency selection block (HSFSBlock), which not only captures multi-scale information from the spatial domain, but also selects the most informative components for image restoration in the frequency domain. Furthermore, to mitigate the inherent noise introduced by skip connections employing only addition or concatenation, we introduce a skip connection attention mechanism (SCAM) to selectively determines the information that should propagate through skip connections. The resulting tightly interlinked architecture, named as LCDNet. Extensive experiments conducted across diverse image restoration tasks showcase that our model attains performance levels that are either superior or comparable to those of state-of-the-art algorithms.
Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) can perform reasoning computations both internally within their latent space and externally by generating explicit token sequences like chains of thought. Significant progress in enhancing reasoning abilities has been made by scaling test-time compute. However, understanding and quantifying model-internal reasoning abilities - the inferential "leaps" models make between individual token predictions - remains crucial. This study introduces a benchmark (n = 4,000 items) designed to quantify model-internal reasoning in different domains. We achieve this by having LLMs indicate the correct solution to reasoning problems not through descriptive text, but by selecting a specific language of their initial response token that is different from English, the benchmark language. This not only requires models to reason beyond their context window, but also to overrise their default tendency to respond in the same language as the prompt, thereby posing an additional cognitive strain. We evaluate a set of 18 LLMs, showing significant performance variations, with GPT-4.5 achieving the highest accuracy (74.7%), outperforming models like Grok-2 (67.2%), and Llama 3.1 405B (65.6%). Control experiments and difficulty scaling analyses suggest that while LLMs engage in internal reasoning, we cannot rule out heuristic exploitations under certain conditions, marking an area for future investigation. Our experiments demonstrate that LLMs can "think" via latent-space computations, revealing model-internal inference strategies that need further understanding, especially regarding safety-related concerns such as covert planning, goal-seeking, or deception emerging without explicit token traces.
A Real-Time, Auto-Regression Method for In-Situ Feature Extraction in Hydrodynamics Simulations
Venue: arXiv cs.DC 2025
Year: 2025

Hydrodynamics simulations are powerful tools for studying fluid behavior under physical forces, enabling extraction of features that reveal key flow characteristics. Traditional post-analysis methods offer high accuracy but incur significant computational and I/O costs. In contrast, in-situ methods reduce data movement by analyzing data during the simulation, yet often compromise either accuracy or performance. We propose a lightweight auto-regression algorithm for real-time in-situ feature extraction. It applies curve-fitting to temporal and spatial data, reducing data volume and minimizing simulation overhead. The model is trained incrementally using mini-batches, ensuring responsiveness and low computational cost. To facilitate adoption, we provide a flexible library with simple APIs for easy integration into existing workflows. We evaluate the method on simulations of material deformation and white dwarf (WD) mergers, extracting features such as shock propagation and delay-time distribution. Results show high accuracy (94.44%-99.60%) and low performance impact (0.11%-4.95%) demonstrating the method's effectiveness for accurate and efficient in-situ analysis.
Skeleton-Based Intake Gesture Detection With Spatial-Temporal Graph Convolutional Networks
Venue: arXiv cs.CV 2025
Year: 2025

Overweight and obesity have emerged as widespread societal challenges, frequently linked to unhealthy eating patterns. A promising approach to enhance dietary monitoring in everyday life involves automated detection of food intake gestures. This study introduces a skeleton based approach using a model that combines a dilated spatial-temporal graph convolutional network (ST-GCN) with a bidirectional long-short-term memory (BiLSTM) framework, as called ST-GCN-BiLSTM, to detect intake gestures. The skeleton-based method provides key benefits, including environmental robustness, reduced data dependency, and enhanced privacy preservation. Two datasets were employed for model validation. The OREBA dataset, which consists of laboratory-recorded videos, achieved segmental F1-scores of 86.18% and 74.84% for identifying eating and drinking gestures. Additionally, a self-collected dataset using smartphone recordings in more adaptable experimental conditions was evaluated with the model trained on OREBA, yielding F1-scores of 85.40% and 67.80% for detecting eating and drinking gestures. The results not only confirm the feasibility of utilizing skeleton data for intake gesture detection but also highlight the robustness of the proposed approach in cross-dataset validation.
SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imaging
Venue: arXiv cs.CV 2025
Year: 2025

Medical Visual Language Models have shown great potential in various healthcare applications, including medical image captioning and diagnostic assistance. However, most existing models rely on text-based instructions, limiting their usability in real-world clinical environments especially in scenarios such as surgery, text-based interaction is often impractical for physicians. In addition, current medical image analysis models typically lack comprehensive reasoning behind their predictions, which reduces their reliability for clinical decision-making. Given that medical diagnosis errors can have life-changing consequences, there is a critical need for interpretable and rational medical assistance. To address these challenges, we introduce an end-to-end speech-driven medical VLM, SilVar-Med, a multimodal medical image assistant that integrates speech interaction with VLMs, pioneering the task of voice-based communication for medical image analysis. In addition, we focus on the interpretation of the reasoning behind each prediction of medical abnormalities with a proposed reasoning dataset. Through extensive experiments, we demonstrate a proof-of-concept study for reasoning-driven medical image interpretation with end-to-end speech interaction. We believe this work will advance the field of medical AI by fostering more transparent, interactive, and clinically viable diagnostic support systems. Our code and dataset are publicly available at SiVar-Med.
Un marco conceptual para la generación de requerimientos de software de calidad
Venue: arXiv cs.SE 2025
Year: 2025

Requirements expressed in natural language are an indispensable artifact in the software development process, as all stakeholders can understand them. However, their ambiguity poses a persistent challenge. To address this issue, organizations such as IEEE and INCOSE publish guidelines for writing requirements, offering rules that assist in this task. On the other hand, agile methodologies provide patterns and structures for expressing stakeholder needs in natural language, attempting to constrain the language to avoid ambiguity. Nevertheless, the knowledge gap among stakeholders regarding the requirements and the correct way to express them further complicates the specification task. In recent years, large language models (LLMs) have emerged to enhance natural language processing tasks. These are Deep learning-based architectures that emulate attention mechanisms like those of humans. This work aims to test the demonstrated power of LLMs in this domain. The objective is to use these models to improve the quality of software requirements written in natural language, assisting analysts in the requirements specification. The proposed framework, its architecture, key components, and their interactions are detailed. Furthermore, a conceptual test of the proposal is developed to assess its usefulness. Finally, the potential and limitations of the framework are discussed, along with future directions for its continued validation and refinement.
Characterizing Knowledge Manipulation in a Russian Wikipedia Fork
Venue: arXiv cs.CL 2025
Year: 2025

Wikipedia is powered by MediaWiki, a free and open-source software that is also the infrastructure for many other wiki-based online encyclopedias. These include the recently launched website Ruwiki, which has copied and modified the original Russian Wikipedia content to conform to Russian law. To identify practices and narratives that could be associated with different forms of knowledge manipulation, this article presents an in-depth analysis of this Russian Wikipedia fork. We propose a methodology to characterize the main changes with respect to the original version. The foundation of this study is a comprehensive comparative analysis of more than 1.9M articles from Russian Wikipedia and its fork. Using meta-information and geographical, temporal, categorical, and textual features, we explore the changes made by Ruwiki editors. Furthermore, we present a classification of the main topics of knowledge manipulation in this fork, including a numerical estimation of their scope. This research not only sheds light on significant changes within Ruwiki, but also provides a methodology that could be applied to analyze other Wikipedia forks and similar collaborative projects.
Perturbed State Space Feature Encoders for Optical Flow with Event Cameras
Venue: arXiv cs.CV 2025
Year: 2025

With their motion-responsive nature, event-based cameras offer significant advantages over traditional cameras for optical flow estimation. While deep learning has improved upon traditional methods, current neural networks adopted for event-based optical flow still face temporal and spatial reasoning limitations. We propose Perturbed State Space Feature Encoders (P-SSE) for multi-frame optical flow with event cameras to address these challenges. P-SSE adaptively processes spatiotemporal features with a large receptive field akin to Transformer-based methods, while maintaining the linear computational complexity characteristic of SSMs. However, the key innovation that enables the state-of-the-art performance of our model lies in our perturbation technique applied to the state dynamics matrix governing the SSM system. This approach significantly improves the stability and performance of our model. We integrate P-SSE into a framework that leverages bi-directional flows and recurrent connections, expanding the temporal context of flow prediction. Evaluations on DSEC-Flow and MVSEC datasets showcase P-SSE's superiority, with 8.48% and 11.86% improvements in EPE performance, respectively.
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we present a multi-agent reinforcement learning (MARL) framework for optimizing tissue repair processes using engineered biological agents. Our approach integrates: (1) stochastic reaction-diffusion systems modeling molecular signaling, (2) neural-like electrochemical communication with Hebbian plasticity, and (3) a biologically informed reward function combining chemical gradient tracking, neural synchronization, and robust penalties. A curriculum learning scheme guides the agent through progressively complex repair scenarios. In silico experiments demonstrate emergent repair strategies, including dynamic secretion control and spatial coordination.
Keyword Extraction, and Aspect Classification in Sinhala, English, and Code-Mixed Content
Venue: arXiv cs.CL 2025
Year: 2025

Brand reputation in the banking sector is maintained through insightful analysis of customer opinion on code-mixed and multilingual content. Conventional NLP models misclassify or ignore code-mixed text, when mix with low resource languages such as Sinhala-English and fail to capture domain-specific knowledge. This study introduces a hybrid NLP method to improve keyword extraction, content filtering, and aspect-based classification of banking content. Keyword extraction in English is performed with a hybrid approach comprising a fine-tuned SpaCy NER model, FinBERT-based KeyBERT embeddings, YAKE, and EmbedRank, which results in a combined accuracy of 91.2%. Code-mixed and Sinhala keywords are extracted using a fine-tuned XLM-RoBERTa model integrated with a domain-specific Sinhala financial vocabulary, and it results in an accuracy of 87.4%. To ensure data quality, irrelevant comment filtering was performed using several models, with the BERT-base-uncased model achieving 85.2% for English and XLM-RoBERTa 88.1% for Sinhala, which was better than GPT-4o, SVM, and keyword-based filtering. Aspect classification followed the same pattern, with the BERT-base-uncased model achieving 87.4% for English and XLM-RoBERTa 85.9% for Sinhala, both exceeding GPT-4 and keyword-based approaches. These findings confirm that fine-tuned transformer models outperform traditional methods in multilingual financial text analysis. The present framework offers an accurate and scalable solution for brand reputation monitoring in code-mixed and low-resource banking environments.
PlantD: Performance, Latency ANalysis, and Testing for Data Pipelines -- An Open Source Measurement, Testing, and Simulation Framework
Venue: arXiv cs.PF 2025
Year: 2025

As the volume of data available from sensor-enabled devices such as vehicles expands, it is increasingly hard for companies to make informed decisions about the cost of capturing, processing, and storing the data from every device. Business teams may forecast costs associated with deployments and use patterns of devices that they sell, yet lack ways of forecasting the cost and performance of the data pipelines needed to support their devices. Without such forecasting, a company's safest choice is to make worst-case capacity estimates, and pay for overprovisioned infrastructure. Existing data pipeline benchmarking tools can measure latency, cost, and throughput as needed for development, but cannot easily close the gap in communicating the implications with business teams to inform cost forecasting. In this paper, we introduce an open-source tool, PlantD, a harness for measuring data pipelines as they are being developed, and for interpreting that data in a business context. PlantD collects a complete suite of metrics and visualizations, when developing or evaluating data pipeline architectures, configurations, and business use cases. It acts as a metaphorical data pipeline wind tunnel, enabling experiments with synthetic data to characterize and compare the performance of pipelines. It then uses those results to allow modeling of expected annual cost and performance under projected real-world loads. We describe the architecture of PlantD, walk through an example of using it to measure and compare three variants of a pipeline for processing automotive telemetry, and demonstrate how business and engineering teams can simulate scenarios together and answer "what-if" questions about the pipeline's performance under different business assumptions, allowing them to intelligently predict performance and cost measures of their critical, high-data generation infrastructure.
HyRRT-Connect: Bidirectional Motion Planning for Hybrid Dynamical Systems
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes a bidirectional rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. The proposed algorithm, called HyRRT-Connect, propagates in both forward and backward directions in hybrid time until an overlap between the forward and backward propagation results is detected. Then, HyRRT-Connect constructs a motion plan through the reversal and concatenation of functions defined on hybrid time domains, ensuring that the motion plan satisfies the given hybrid dynamics. To address the potential discontinuity along the flow caused by tolerating some distance between the forward and backward partial motion plans, we reconstruct the backward partial motion plan by a forward-in-hybrid-time simulation from the final state of the forward partial motion plan. effectively eliminating the discontinuity. The proposed algorithm is applied to an actuated bouncing ball system and a walking robot example to highlight its computational improvement.
Legally-Informed Explainable AI
Venue: arXiv cs.HC 2025
Year: 2025

Explanations for artificial intelligence (AI) systems are intended to support the people who are impacted by AI systems in high-stakes decision-making environments, such as doctors, patients, teachers, students, housing applicants, and many others. To protect people and support the responsible development of AI, explanations need to be actionable--helping people take pragmatic action in response to an AI system--and contestable--enabling people to push back against an AI system and its determinations. For many high-stakes domains, such as healthcare, education, and finance, the sociotechnical environment includes significant legal implications that impact how people use AI explanations. For example, physicians who use AI decision support systems may need information on how accepting or rejecting an AI determination will protect them from lawsuits or help them advocate for their patients. In this paper, we make the case for Legally-Informed Explainable AI, responding to the need to integrate and design for legal considerations when creating AI explanations. We describe three stakeholder groups with different informational and actionability needs, and provide practical recommendations to tackle design challenges around the design of explainable AI systems that incorporate legal considerations.
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)
Venue: arXiv cs.LG 2025
Year: 2025

Full Waveform Inversion (FWI) is an important geophysical technique considered in subsurface property prediction. It solves the inverse problem of predicting high-resolution Earth interior models from seismic data. Traditional FWI methods are computationally demanding. Inverse problems in geophysics often face challenges of non-uniqueness due to limited data, as data are often collected only on the surface. In this study, we introduce a novel methodology that leverages Deep Operator Networks (DeepONet) to attempt to improve both the efficiency and accuracy of FWI. The proposed DeepONet methodology inverts seismic waveforms for the subsurface velocity field. This approach is able to capture some key features of the subsurface velocity field. We have shown that the architecture can be applied to noisy seismic data with an accuracy that is better than some other machine learning methods. We also test our proposed method with out-of-distribution prediction for different velocity models. The proposed DeepONet shows comparable and better accuracy in some velocity models than some other machine learning methods. To improve the FWI workflow, we propose using the DeepONet output as a starting model for conventional FWI and that it may improve FWI performance. While we have only shown that DeepONet facilitates faster convergence than starting with a homogeneous velocity field, it may have some benefits compared to other approaches to constructing starting models. This integration of DeepONet into FWI may accelerate the inversion process and may also enhance its robustness and reliability.
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates
Venue: arXiv cs.CV 2025
Year: 2025

The rapid growth of intelligent connected vehicles (ICVs) and integrated vehicle-road-cloud systems has increased the demand for accurate, real-time HD map updates. However, ensuring map reliability remains challenging due to inconsistencies in crowdsourced data, which suffer from motion blur, lighting variations, adverse weather, and lane marking degradation. This paper introduces CleanMAP, a Multimodal Large Language Model (MLLM)-based distillation framework designed to filter and refine crowdsourced data for high-confidence HD map updates. CleanMAP leverages an MLLM-driven lane visibility scoring model that systematically quantifies key visual parameters, assigning confidence scores (0-10) based on their impact on lane detection. A novel dynamic piecewise confidence-scoring function adapts scores based on lane visibility, ensuring strong alignment with human evaluations while effectively filtering unreliable data. To further optimize map accuracy, a confidence-driven local map fusion strategy ranks and selects the top-k highest-scoring local maps within an optimal confidence range (best score minus 10%), striking a balance between data quality and quantity. Experimental evaluations on a real-world autonomous vehicle dataset validate CleanMAP's effectiveness, demonstrating that fusing the top three local maps achieves the lowest mean map update error of 0.28m, outperforming the baseline (0.37m) and meeting stringent accuracy thresholds (<= 0.32m). Further validation with real-vehicle data confirms 84.88% alignment with human evaluators, reinforcing the model's robustness and reliability. This work establishes CleanMAP as a scalable and deployable solution for crowdsourced HD map updates, ensuring more precise and reliable autonomous navigation. The code will be available at https://Ankit-Zefan.github.io/CleanMap/
HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding
Venue: arXiv cs.MM 2025
Year: 2025

Comprehending extended audiovisual experiences remains a fundamental challenge for computational systems. Current approaches struggle with temporal integration and cross-modal associations that humans accomplish effortlessly through hippocampal-cortical networks. We introduce HippoMM, a biologically-inspired architecture that transforms hippocampal mechanisms into computational advantages for multimodal understanding. HippoMM implements three key innovations: (i) hippocampus-inspired pattern separation and completion specifically designed for continuous audiovisual streams, (ii) short-to-long term memory consolidation that transforms perceptual details into semantic abstractions, and (iii) cross-modal associative retrieval pathways enabling modality-crossing queries. Unlike existing retrieval systems with static indexing schemes, HippoMM dynamically forms integrated episodic representations through adaptive temporal segmentation and dual-process memory encoding. Evaluations on our challenging HippoVlog benchmark demonstrate that HippoMM significantly outperforms state-of-the-art approaches (78.2% vs. 64.2% accuracy) while providing substantially faster response times (20.4s vs. 112.5s). Our results demonstrate that translating neuroscientific memory principles into computational architectures provides a promising foundation for next-generation multimodal understanding systems. The code and benchmark dataset are publicly available at https://github.com/linyueqian/HippoMM.