OpenRaiser/Intern-Atlas huggingface dataset:
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Visual Question Answering (VQA) requires reasoning across visual and textual modalities, yet Large Vision-Language Models (LVLMs) often lack integrated commonsense knowledge, limiting their robustness in real-world scenarios. To address this, we introduce MAGIC-VQA, a novel framework that enhances VQA by systematically integrating commonsense knowledge with LVLMs. MAGIC-VQA employs a three-stage process: (1) Explicit Knowledge Integration from external sources, (2) By-Type Post-Processing for contextual refinement, and (3) Implicit Knowledge Augmentation using a Graph Neural Network (GNN) for structured reasoning. While GNNs bring greater depth to structured inference, they enable superior relational inference beyond LVLMs. MAGIC-VQA bridges a key gap by unifying commonsensse knowledge with LVLM-driven reasoning, eliminating the need for extensive pre-training or complex prompt tuning. Our framework achieves state-of-the-art performance on benchmark datasets, significantly improving commonsense reasoning in VQA.
Real-Time Streaming Telemetry Based Detection and Mitigation of OOK and Power Interference in Multi-User OSaaS Networks
Venue: arXiv cs.NI 2025
Year: 2025

We present a framework to identify and mitigate rogue OOK signals and user-generated power interference in a multi-user Optical-Spectrum-as-a-Service network. Experimental tests on the OpenIreland-testbed achieve up to 89% detection rate within 10 seconds of an interference event.
Distilling Stereo Networks for Performant and Efficient Leaner Networks
Venue: arXiv cs.CV 2025
Year: 2025

Knowledge distillation has been quite popular in vision for tasks like classification and segmentation however not much work has been done for distilling state-of-the-art stereo matching methods despite their range of applications. One of the reasons for its lack of use in stereo matching networks is due to the inherent complexity of these networks, where a typical network is composed of multiple two- and three-dimensional modules. In this work, we systematically combine the insights from state-of-the-art stereo methods with general knowledge-distillation techniques to develop a joint framework for stereo networks distillation with competitive results and faster inference. Moreover, we show, via a detailed empirical analysis, that distilling knowledge from the stereo network requires careful design of the complete distillation pipeline starting from backbone to the right selection of distillation points and corresponding loss functions. This results in the student networks that are not only leaner and faster but give excellent performance . For instance, our student network while performing better than the performance oriented methods like PSMNet [1], CFNet [2], and LEAStereo [3]) on benchmark SceneFlow dataset, is 8x, 5x, and 8x faster respectively. Furthermore, compared to speed oriented methods having inference time less than 100ms, our student networks perform better than all the tested methods. In addition, our student network also shows better generalization capabilities when tested on unseen datasets like ETH3D and Middlebury.
The (Un)suitability of Passwords and Password Managers in Virtual Reality
Venue: arXiv cs.CR 2025
Year: 2025

As Virtual Reality (VR) expands into fields like healthcare and education, ensuring secure and user-friendly authentication becomes essential. Traditional password entry methods in VR are cumbersome and insecure, making password managers (PMs) a potential solution. To explore this field, we conducted a user study (n=126 VR users) where participants expressed a strong preference for simpler passwords and showed interest in biometric authentication and password managers. On these grounds, we provide the first in-depth evaluation of PMs in VR. We report findings from 91 cognitive walkthroughs, revealing that while PMs improve usability, they are not yet ready for prime time. Key features like cross-app autofill are missing, and user experiences highlight the need for better solutions. Based on consolidated user views and expert analysis, we make recommendations on how to move forward in improving VR authentication systems, ultimately creating more practical solutions for this growing field.
Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Despite the significant success of Large Vision-Language models(LVLMs), these models still suffer hallucinations when describing images, generating answers that include non-existent objects. It is reported that these models tend to over-focus on certain irrelevant image tokens that do not contain critical information for answering the question and distort the output. To address this, we propose an Instruction-Aligned Visual Attention(IAVA) approach, which identifies irrelevant tokens by comparing changes in attention weights under two different instructions. By applying contrastive decoding, we dynamically adjust the logits generated from original image tokens and irrelevant image tokens, reducing the model's over-attention to irrelevant information. The experimental results demonstrate that IAVA consistently outperforms existing decoding techniques on benchmarks such as MME, POPE, and TextVQA in mitigating object hallucinations. Our IAVA approach is available online at https://github.com/Lee-lab558/IAVA.
Dense Retrieval for Low Resource Languages -- the Case of Amharic Language
Venue: arXiv cs.IR 2025
Year: 2025

This paper reports some difficulties and some results when using dense retrievers on Amharic, one of the low-resource languages spoken by 120 millions populations. The efforts put and difficulties faced by University Addis Ababa toward Amharic Information Retrieval will be developed during the presentation.
Testora: Using Natural Language Intent to Detect Behavioral Regressions
Venue: arXiv cs.SE 2025
Year: 2025

As software is evolving, code changes can introduce regression bugs or affect the behavior in other unintended ways. Traditional regression test generation is impractical for detecting unintended behavioral changes, because it reports all behavioral differences as potential regressions. However, most code changes are intended to change the behavior in some way, e.g., to fix a bug or to add a new feature. This paper presents Testora, the first automated approach that detects regressions by comparing the intentions of a code change against behavioral differences caused by the code change. Given a pull request (PR), Testora queries an LLM to generate tests that exercise the modified code, compares the behavior of the original and modified code, and classifies any behavioral differences as intended or unintended. For the classification, we present an LLM-based technique that leverages the natural language information associated with the PR, such as the title, description, and commit messages -- effectively using the natural language intent to detect behavioral regressions. Applying Testora to PRs of complex and popular Python projects, we find 19 regression bugs and 11 PRs that, despite having another intention, coincidentally fix a bug. Out of 13 regressions reported to the developers, 11 have been confirmed and 9 have already been fixed. The costs of using Testora are acceptable for real-world deployment, with 12.3 minutes to check a PR and LLM costs of only $0.003 per PR. We envision our approach to be used before or shortly after a code change gets merged into a code base, providing a way to early on detect regressions that are not caught by traditional approaches.
Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling
Venue: arXiv cs.CV 2025
Year: 2025

Lane detection is critical for autonomous driving and ad-vanced driver assistance systems (ADAS). While recent methods like CLRNet achieve strong performance, they struggle under adverse con-ditions such as extreme weather, illumination changes, occlusions, and complex curves. We propose a Wavelet-Enhanced Feature Pyramid Net-work (WE-FPN) to address these challenges. A wavelet-based non-local block is integrated before the feature pyramid to improve global context modeling, especially for occluded and curved lanes. Additionally, we de-sign an adaptive preprocessing module to enhance lane visibility under poor lighting. An attention-guided sampling strategy further reffnes spa-tial features, boosting accuracy on distant and curved lanes. Experiments on CULane and TuSimple demonstrate that our approach signiffcantly outperforms baselines in challenging scenarios, achieving better robust-ness and accuracy in real-world driving conditions.
A Comprehensive Review on Hashtag Recommendation: From Traditional to Deep Learning and Beyond
Venue: arXiv cs.IR 2025
Year: 2025

The exponential growth of user-generated content on social media platforms has precipitated significant challenges in information management, particularly in content organization, retrieval, and discovery. Hashtags, as a fundamental categorization mechanism, play a pivotal role in enhancing content visibility and user engagement. However, the development of accurate and robust hashtag recommendation systems remains a complex and evolving research challenge. Existing surveys in this domain are limited in scope and recency, focusing narrowly on specific platforms, methodologies, or timeframes. To address this gap, this review article conducts a systematic analysis of hashtag recommendation systems, comprehensively examining recent advancements across several dimensions. We investigate unimodal versus multimodal methodologies, diverse problem formulations, filtering strategies, methodological evolution from traditional frequency-based models to advanced deep learning architectures. Furthermore, we critically evaluate performance assessment paradigms, including quantitative metrics, qualitative analyses, and hybrid evaluation frameworks. Our analysis underscores a paradigm shift toward transformer-based deep learning models, which harness contextual and semantic features to achieve superior recommendation accuracy. Key challenges such as data sparsity, cold-start scenarios, polysemy, and model explainability are rigorously discussed, alongside practical applications in tweet classification, sentiment analysis, and content popularity prediction. By synthesizing insights from diverse methodological and platform-specific perspectives, this survey provides a structured taxonomy of current research, identifies unresolved gaps, and proposes future directions for developing adaptive, user-centric recommendation systems.
Merge Mode for Template-based Intra Mode Derivation (TIMD) in ECM
Venue: arXiv cs.MM 2025
Year: 2025

This paper presents an intra coding tool, named Merge mode for Template-based Intra Mode Derivation (TIMD). TIMD-Merge has been adopted in the 15\textsuperscript{th} version of the Enhanced Compression Model (ECM) software that explores video coding technologies beyond Versatile Video Coding (VVC) standard. This proposed tool operates on top of the regular TIMD mode that applies a template-based search on a causal adjacent template at top and at left of the current block, in order to find the best Intra Prediction Modes (IPMs) that matches the template. The proposed TIMD-Merge in this paper addresses a shortcoming in the regular TIMD method where due to texture discrepancy, the adjacent template information around the block is not reliable. To do so, the proposed TIMD-Merge constructs a list of all TIMD-coded blocks in relatively larger template area than the template of the regular TIMD, which also includes non-adjacent neighboring blocks. This list, called the merge list, is then sorted on the template to give one best set of TIMD modes. The use of TIMD-Merge mode is signalled at the block level and the implementation in the ECM-14.0 demonstrates -0.08\% performance improvement in terms of luma BDR gain, with negligible encoding and decoding runtime increase of 100.6% and 100.2%, respectively.
Wireless Hearables With Programmable Speech AI Accelerators
Venue: arXiv cs.SD 2025
Year: 2025

The conventional wisdom has been that designing ultra-compact, battery-constrained wireless hearables with on-device speech AI models is challenging due to the high computational demands of streaming deep learning models. Speech AI models require continuous, real-time audio processing, imposing strict computational and I/O constraints. We present NeuralAids, a fully on-device speech AI system for wireless hearables, enabling real-time speech enhancement and denoising on compact, battery-constrained devices. Our system bridges the gap between state-of-the-art deep learning for speech enhancement and low-power AI hardware by making three key technical contributions: 1) a wireless hearable platform integrating a speech AI accelerator for efficient on-device streaming inference, 2) an optimized dual-path neural network designed for low-latency, high-quality speech enhancement, and 3) a hardware-software co-design that uses mixed-precision quantization and quantization-aware training to achieve real-time performance under strict power constraints. Our system processes 6 ms audio chunks in real-time, achieving an inference time of 5.54 ms while consuming 71.6 mW. In real-world evaluations, including a user study with 28 participants, our system outperforms prior on-device models in speech quality and noise suppression, paving the way for next-generation intelligent wireless hearables that can enhance hearing entirely on-device.
Accenture-NVS1: A Novel View Synthesis Dataset
Venue: arXiv cs.CV 2025
Year: 2025

This paper introduces ACC-NVS1, a specialized dataset designed for research on Novel View Synthesis specifically for airborne and ground imagery. Data for ACC-NVS1 was collected in Austin, TX and Pittsburgh, PA in 2023 and 2024. The collection encompasses six diverse real-world scenes captured from both airborne and ground cameras, resulting in a total of 148,000 images. ACC-NVS1 addresses challenges such as varying altitudes and transient objects. This dataset is intended to supplement existing datasets, providing additional resources for comprehensive research, rather than serving as a benchmark.
From Trust to Truth: Actionable policies for the use of AI in fact-checking in Germany and Ukraine
Venue: arXiv cs.CY 2025
Year: 2025

The rise of Artificial Intelligence (AI) presents unprecedented opportunities and challenges for journalism, fact-checking and media regulation. While AI offers tools to combat disinformation and enhance media practices, its unregulated use and associated risks necessitate clear policies and collaborative efforts. This policy paper explores the implications of artificial intelligence (AI) for journalism and fact-checking, with a focus on addressing disinformation and fostering responsible AI integration. Using Germany and Ukraine as key case studies, it identifies the challenges posed by disinformation, proposes regulatory and funding strategies, and outlines technical standards to enhance AI adoption in media. The paper offers actionable recommendations to ensure AI's responsible and effective integration into media ecosystems. AI presents significant opportunities to combat disinformation and enhance journalistic practices. However, its implementation lacks cohesive regulation, leading to risks such as bias, transparency issues, and over-reliance on automated systems. In Ukraine, establishing an independent media regulatory framework adapted to its governance is crucial, while Germany can act as a leader in advancing EU-wide collaborations and standards. Together, these efforts can shape a robust AI-driven media ecosystem that promotes accuracy and trust.
Construction Identification and Disambiguation Using BERT: A Case Study of NPN
Venue: arXiv cs.CL 2025
Year: 2025

Construction Grammar hypothesizes that knowledge of a language consists chiefly of knowledge of form-meaning pairs (''constructions'') that include vocabulary, general grammar rules, and even idiosyncratic patterns. Recent work has shown that transformer language models represent at least some constructional patterns, including ones where the construction is rare overall. In this work, we probe BERT's representation of the form and meaning of a minor construction of English, the NPN (noun-preposition-noun) construction -- exhibited in such expressions as face to face and day to day -- which is known to be polysemous. We construct a benchmark dataset of semantically annotated corpus instances (including distractors that superficially resemble the construction). With this dataset, we train and evaluate probing classifiers. They achieve decent discrimination of the construction from distractors, as well as sense disambiguation among true instances of the construction, revealing that BERT embeddings carry indications of the construction's semantics. Moreover, artificially permuting the word order of true construction instances causes them to be rejected, indicating sensitivity to matters of form. We conclude that BERT does latently encode at least some knowledge of the NPN construction going beyond a surface syntactic pattern and lexical cues.
CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research
Venue: arXiv cs.IR 2025
Year: 2025

Data are crucial in various computer-related fields, including music information retrieval (MIR), an interdisciplinary area bridging computer science and music. This paper introduces CCMusic, an open and diverse database comprising multiple datasets specifically designed for tasks related to Chinese music, highlighting our focus on this culturally rich domain. The database integrates both published and unpublished datasets, with steps taken such as data cleaning, label refinement, and data structure unification to ensure data consistency and create ready-to-use versions. We conduct benchmark evaluations for all datasets using a unified evaluation framework developed specifically for this purpose. This publicly available framework supports both classification and detection tasks, ensuring standardized and reproducible results across all datasets. The database is hosted on HuggingFace and ModelScope, two open and multifunctional data and model hosting platforms, ensuring ease of accessibility and usability.
Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems
Venue: arXiv cs.AI 2025
Year: 2025

Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this technology also raises ethical, societal, and legal concerns. Key among these are the potential displacement of human creativity, copyright infringement stemming from vast training datasets, and the lack of transparency, explainability, and fairness mechanisms. As generative systems become pervasive in this domain, responsible design is crucial. Whilst previous work has tackled isolated aspects of generative systems (e.g., transparency, evaluation, data), we take a comprehensive approach, grounding these efforts within the Ethics Guidelines for Trustworthy Artificial Intelligence produced by the High-Level Expert Group on AI appointed by the European Commission - a framework for designing responsible AI systems across seven macro requirements. Focusing on generative music AI, we illustrate how these requirements can be contextualised for the field, addressing trustworthiness across multiple dimensions and integrating insights from the existing literature. We further propose a roadmap for operationalising these contextualised requirements, emphasising interdisciplinary collaboration and stakeholder engagement. Our work provides a foundation for designing and evaluating responsible music generation systems, calling for collaboration among AI experts, ethicists, legal scholars, and artists. This manuscript is accompanied by a website: https://amresearchlab.github.io/raim-framework/.
Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

With the rapid development of e-commerce, e-commerce platforms are facing an increasing number of fraud threats. Effectively identifying and preventing these fraudulent activities has become a critical research problem. Traditional fraud detection methods typically rely on supervised learning, which requires large amounts of labeled data. However, such data is often difficult to obtain, and the continuous evolution of fraudulent activities further reduces the adaptability and effectiveness of traditional methods. To address this issue, this study proposes an unsupervised e-commerce fraud detection algorithm based on SimCLR. The algorithm leverages the contrastive learning framework to effectively detect fraud by learning the underlying representations of transaction data in an unlabeled setting. Experimental results on the eBay platform dataset show that the proposed algorithm outperforms traditional unsupervised methods such as K-means, Isolation Forest, and Autoencoders in terms of accuracy, precision, recall, and F1 score, demonstrating strong fraud detection capabilities. The results confirm that the SimCLR-based unsupervised fraud detection method has broad application prospects in e-commerce platform security, improving both detection accuracy and robustness. In the future, with the increasing scale and diversity of datasets, the model's performance will continue to improve, and it could be integrated with real-time monitoring systems to provide more efficient security for e-commerce platforms.
Reasoning to Learn from Latent Thoughts
Venue: arXiv cs.LG 2025
Year: 2025

Compute scaling for language model (LM) pretraining has outpaced the growth of human-written texts, leading to concerns that data will become the bottleneck to LM scaling. To continue scaling pretraining in this data-constrained regime, we propose that explicitly modeling and inferring the \emph{latent thoughts} that underlie the text generation process can significantly improve pretraining data efficiency. Intuitively, our approach views web text as the compressed final outcome of a verbose human thought process and that the latent thoughts contain important contextual knowledge and reasoning steps that are critical to data-efficient learning. We empirically demonstrate the effectiveness of our approach through data-constrained continued pretraining for math. We first show that synthetic data approaches to inferring latent thoughts significantly improve data efficiency over training on the same amount of raw data. Furthermore, we demonstrate latent thought inference without a strong teacher, where an LM \emph{bootstraps its own performance} by using an EM algorithm to iteratively improve the capability of the trained LM and the quality of thought-augmented pretraining data. We show that a 1B LM can bootstrap its performance across at least three iterations and significantly outperform baselines trained on raw data, with increasing gains from additional inference compute when performing the E-step. The gains from inference scaling and EM iterations suggest new opportunities for scaling data-constrained pretraining.