OpenRaiser/Intern-Atlas huggingface dataset:
Agentic Username Suggestion and Multimodal Gender Detection in Online Platforms: Introducing the PNGT-26K Dataset
Venue: arXiv cs.LG 2025
Year: 2025

Persian names present unique challenges for natural language processing applications, particularly in gender detection and digital identity creation, due to transliteration inconsistencies and cultural-specific naming patterns. Existing tools exhibit significant performance degradation on Persian names, while the scarcity of comprehensive datasets further compounds these limitations. To address these challenges, the present research introduces PNGT-26K, a comprehensive dataset of Persian names, their commonly associated gender, and their English transliteration, consisting of approximately 26,000 tuples. As a demonstration of how this resource can be utilized, we also introduce two frameworks, namely Open Gender Detection and Nominalist. Open Gender Detection is a production-grade, ready-to-use framework for using existing data from a user, such as profile photo and name, to give a probabilistic guess about the person's gender. Nominalist, the second framework introduced by this paper, utilizes agentic AI to help users choose a username for their social media accounts on any platform. It can be easily integrated into any website to provide a better user experience. The PNGT-26K dataset, Nominalist and Open Gender Detection frameworks are publicly available on Github.
When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity
Venue: arXiv cs.CL 2025
Year: 2025

Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across contexts. While usually associated with friendliness or playfulness, it is observed that emojis may trigger toxic content generation in LLMs. Motivated by such a observation, we aim to investigate: (1) whether emojis can clearly enhance the toxicity generation in LLMs and (2) how to interpret this phenomenon. We begin with a comprehensive exploration of emoji-triggered LLM toxicity generation by automating the construction of prompts with emojis to subtly express toxic intent. Experiments across 5 mainstream languages on 7 famous LLMs along with jailbreak tasks demonstrate that prompts with emojis could easily induce toxicity generation. To understand this phenomenon, we conduct model-level interpretations spanning semantic cognition, sequence generation and tokenization, suggesting that emojis can act as a heterogeneous semantic channel to bypass the safety mechanisms. To pursue deeper insights, we further probe the pre-training corpus and uncover potential correlation between the emoji-related data polution with the toxicity generation behaviors. Supplementary materials provide our implementation code and data. (Warning: This paper contains potentially sensitive contents)
AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions
Venue: arXiv cs.AI 2025
Year: 2025

Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.
Stabilizing Data-Free Model Extraction
Venue: arXiv cs.LG 2025
Year: 2025

Model extraction is a severe threat to Machine Learning-as-a-Service systems, especially through data-free approaches, where dishonest users can replicate the functionality of a black-box target model without access to realistic data. Despite recent advancements, existing data-free model extraction methods suffer from the oscillating accuracy of the substitute model. This oscillation, which could be attributed to the constant shift in the generated data distribution during the attack, makes the attack impractical since the optimal substitute model cannot be determined without access to the target model's in-distribution data. Hence, we propose MetaDFME, a novel data-free model extraction method that employs meta-learning in the generator training to reduce the distribution shift, aiming to mitigate the substitute model's accuracy oscillation. In detail, we train our generator to iteratively capture the meta-representations of the synthetic data during the attack. These meta-representations can be adapted with a few steps to produce data that facilitates the substitute model to learn from the target model while reducing the effect of distribution shifts. Our experiments on popular baseline image datasets, MNIST, SVHN, CIFAR-10, and CIFAR-100, demonstrate that MetaDFME outperforms the current state-of-the-art data-free model extraction method while exhibiting a more stable substitute model's accuracy during the attack.
Beyond Sliders: Mastering the Art of Diffusion-based Image Manipulation
Venue: arXiv cs.CV 2025
Year: 2025

In the realm of image generation, the quest for realism and customization has never been more pressing. While existing methods like concept sliders have made strides, they often falter when it comes to no-AIGC images, particularly images captured in real world settings. To bridge this gap, we introduce Beyond Sliders, an innovative framework that integrates GANs and diffusion models to facilitate sophisticated image manipulation across diverse image categories. Improved upon concept sliders, our method refines the image through fine grained guidance both textual and visual in an adversarial manner, leading to a marked enhancement in image quality and realism. Extensive experimental validation confirms the robustness and versatility of Beyond Sliders across a spectrum of applications.
MIS-LSTM: Multichannel Image-Sequence LSTM for Sleep Quality and Stress Prediction
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents MIS-LSTM, a hybrid framework that joins CNN encoders with an LSTM sequence model for sleep quality and stress prediction at the day level from multimodal lifelog data. Continuous sensor streams are first partitioned into N-hour blocks and rendered as multi-channel images, while sparse discrete events are encoded with a dedicated 1D-CNN. A Convolutional Block Attention Module fuses the two modalities into refined block embeddings, which an LSTM then aggregates to capture long-range temporal dependencies. To further boost robustness, we introduce UALRE, an uncertainty-aware ensemble that overrides lowconfidence majority votes with high-confidence individual predictions. Experiments on the 2025 ETRI Lifelog Challenge dataset show that Our base MISLSTM achieves Macro-F1 0.615; with the UALRE ensemble, the score improves to 0.647, outperforming strong LSTM, 1D-CNN, and CNN baselines. Ablations confirm (i) the superiority of multi-channel over stacked-vertical imaging, (ii) the benefit of a 4-hour block granularity, and (iii) the efficacy of modality-specific discrete encoding.
Implementation of Learning with Errors in Non-Commuting Multiplicative Groups
Venue: arXiv cs.CR 2025
Year: 2025

In this paper, we demonstrate a way to generalize learning with errors (LWE) to the family of so-called modular-maximal cyclic groups which are non-commuting. Since the group M2t has two cycles of maximal multiplicative order, we use this fact to construct an accurate criterion for restoring the message bit with overwhelming probability. Furthermore, we implement the original idea by O. Regev in the considered group to gain benefits from the non-commutativity of M2t . Also we prove that using this approach we can achieve a level of security comparable to the original idea.
Make Identity Unextractable yet Perceptible: Synthesis-Based Privacy Protection for Subject Faces in Photos
Venue: arXiv cs.CR 2025
Year: 2025

Deep learning-based face recognition (FR) technology exacerbates privacy concerns in photo sharing. In response, the research community developed a suite of anti-FR methods to block identity extraction by unauthorized FR systems. Benefiting from quasi-imperceptible alteration, perturbation-based methods are well-suited for privacy protection of subject faces in photos, as they allow familiar persons to recognize subjects via naked eyes. However, we reveal that perturbation-based methods provide a false sense of privacy through theoretical analysis and experimental validation. Therefore, new alternative solutions should be found to protect subject faces. In this paper, we explore synthesis-based methods as a promising solution, whose challenge is to enable familiar persons to recognize subjects. To solve the challenge, we present a key insight: In most photo sharing scenarios, familiar persons recognize subjects through identity perception rather than meticulous face analysis. Based on the insight, we propose the first synthesis-based method dedicated to subject faces, i.e., PerceptFace, which can make identity unextractable yet perceptible. To enhance identity perception, a new perceptual similarity loss is designed for faces, reducing the alteration in regions of high sensitivity to human vision. As a synthesis-based method, PerceptFace can inherently provide reliable identity protection. Meanwhile, out of the confine of meticulous face analysis, PerceptFace focuses on identity perception from a more practical scenario, which is also enhanced by the designed perceptual similarity loss. Sufficient experiments show that PerceptFace achieves a superior trade-off between identity protection and identity perception compared to existing methods. We provide a public API of PerceptFace and believe that it has great potential to become a practical anti-FR tool.
PINGS: Physics-Informed Neural Network for Fast Generative Sampling
Venue: arXiv cs.LG 2025
Year: 2025

We introduce PINGS (Physics-Informed Neural Network for Fast Generative Sampling), a framework that amortizes diffusion sampling by training a physics-informed network to approximate reverse-time probability-flow dynamics, reducing sampling to a single forward pass (NFE = 1). As a proof of concept, we learn a direct map from a 3D standard normal to a non-Gaussian Gaussian Mixture Model (GMM). PINGS preserves the target's distributional structure (multi-bandwidth kernel $MMD^2 = 1.88 \times 10^{-2}$ with small errors in mean, covariance, skewness, and excess kurtosis) and achieves constant-time generation: $10^4$ samples in $16.54 \pm 0.56$ millisecond on an RTX 3090, versus 468-843 millisecond for DPM-Solver (10/20) and 960 millisecond for DDIM (50) under matched conditions. We also sanity-check the PINN/automatic-differentiation pipeline on a damped harmonic oscillator, obtaining MSEs down to $\mathcal{O}(10^{-5})$. Compared to fast but iterative ODE solvers and direct-map families (Flow, Rectified-Flow, Consistency), PINGS frames generative sampling as a PINN-style residual problem with endpoint anchoring, yielding a white-box, differentiable map with NFE = 1. These proof-of-concept results position PINGS as a promising route to fast, function-based generative sampling with potential extensions to scientific simulation (e.g., fast calorimetry).
Leveraging Geometric Priors for Unaligned Scene Change Detection
Venue: arXiv cs.CV 2025
Year: 2025

Unaligned Scene Change Detection aims to detect scene changes between image pairs captured at different times without assuming viewpoint alignment. To handle viewpoint variations, current methods rely solely on 2D visual cues to establish cross-image correspondence to assist change detection. However, large viewpoint changes can alter visual observations, causing appearance-based matching to drift or fail. Additionally, supervision limited to 2D change masks from small-scale SCD datasets restricts the learning of generalizable multi-view knowledge, making it difficult to reliably identify visual overlaps and handle occlusions. This lack of explicit geometric reasoning represents a critical yet overlooked limitation. In this work, we introduce geometric priors for the first time to address the core challenges of unaligned SCD, for reliable identification of visual overlaps, robust correspondence establishment, and explicit occlusion detection. Building on these priors, we propose a training-free framework that integrates them with the powerful representations of a visual foundation model to enable reliable change detection under viewpoint misalignment. Through extensive evaluation on the PSCD, ChangeSim, and PASLCD datasets, we demonstrate that our approach achieves superior and robust performance. Our code will be released at https://github.com/ZilingLiu/GeoSCD.
Brain-Robot Interface for Exercise Mimicry
Venue: arXiv cs.RO 2025
Year: 2025

For social robots to maintain long-term engagement as exercise instructors, rapport-building is essential. Motor mimicry--imitating one's physical actions--during social interaction has long been recognized as a powerful tool for fostering rapport, and it is widely used in rehabilitation exercises where patients mirror a physiotherapist or video demonstration. We developed a novel Brain-Robot Interface (BRI) that allows a social robot instructor to mimic a patient's exercise movements in real-time, using mental commands derived from the patient's intention. The system was evaluated in an exploratory study with 14 participants (3 physiotherapists and 11 hemiparetic patients recovering from stroke or other injuries). We found our system successfully demonstrated exercise mimicry in 12 sessions; however, accuracy varied. Participants had positive perceptions of the robot instructor, with high trust and acceptance levels, which were not affected by the introduction of BRI technology.
Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
Venue: arXiv cs.AI 2025
Year: 2025

The widespread use of plastics and their persistence in the environment have led to the accumulation of micro- and nano-plastics across air, water, and soil, posing serious health risks including respiratory, gastrointestinal, and neurological disorders. We propose a novel framework that leverages large language models to extract relational metapaths, multi-hop semantic chains linking pollutant sources to health impacts, from scientific abstracts. Our system identifies and connects entities across diverse contexts to construct structured relational metapaths, which are aggregated into a Toxicity Trajectory Graph that traces pollutant propagation through exposure routes and biological systems. Moreover, to ensure consistency and reliability, we incorporate a dynamic evidence reconciliation module that resolves semantic conflicts arising from evolving or contradictory research findings. Our approach demonstrates strong performance in extracting reliable, high-utility relational knowledge from noisy scientific text and offers a scalable solution for mining complex cause-effect structures in domain-specific corpora.
MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Recently, large language models (LLMs) have achieved remarkable breakthroughs in general domains such as programming and writing, and have demonstrated strong potential in various scientific research scenarios. However, the capabilities of AI models in the highly specialized field of materials characterization and analysis have not yet been systematically or sufficiently validated. To address this gap, we present MatQnA, the first multi-modal benchmark dataset specifically designed for material characterization techniques. MatQnA includes ten mainstream characterization methods, such as X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), etc. We employ a hybrid approach combining LLMs with human-in-the-loop validation to construct high-quality question-answer pairs, integrating both multiple-choice and subjective questions. Our preliminary evaluation results show that the most advanced multi-modal AI models (e.g., GPT-4.1, Claude 4, Gemini 2.5, and Doubao Vision Pro 32K) have already achieved nearly 90% accuracy on objective questions in materials data interpretation and analysis tasks, demonstrating strong potential for applications in materials characterization and analysis. The MatQnA dataset is publicly available at https://huggingface.co/datasets/richardhzgg/matQnA.
The power of dynamic causality in observer-based design for soft sensor applications
Venue: arXiv cs.AI 2025
Year: 2025

This paper introduces a novel framework for optimizing observer-based soft sensors through dynamic causality analysis. Traditional approaches to sensor selection often rely on linearized observability indices or statistical correlations that fail to capture the temporal evolution of complex systems. We address this gap by leveraging liquid-time constant (LTC) networks, continuous-time neural architectures with input-dependent time constants, to systematically identify and prune sensor inputs with minimal causal influence on state estimation. Our methodology implements an iterative workflow: training an LTC observer on candidate inputs, quantifying each input's causal impact through controlled perturbation analysis, removing inputs with negligible effect, and retraining until performance degradation occurs. We demonstrate this approach on three mechanistic testbeds representing distinct physical domains: a harmonically forced spring-mass-damper system, a nonlinear continuous stirred-tank reactor, and a predator-prey model following the structure of the Lotka-Volterra model, but with seasonal forcing and added complexity. Results show that our causality-guided pruning consistently identifies minimal sensor sets that align with underlying physics while improving prediction accuracy. The framework automatically distinguishes essential physical measurements from noise and determines when derived interaction terms provide complementary versus redundant information. Beyond computational efficiency, this approach enhances interpretability by grounding sensor selection decisions in dynamic causal relationships rather than static correlations, offering significant benefits for soft sensing applications across process engineering, ecological monitoring, and agricultural domains.
Efficient Polynomial Identity Testing Over Nonassociative Algebras
Venue: arXiv cs.CC 2025
Year: 2025

We design the first efficient polynomial identity testing algorithms over the nonassociative polynomial algebra. In particular, multiplication among the formal variables is commutative but it is not associative. This complements the strong lower bound results obtained over this algebra by Hrubeš, Yehudayoff, and Wigderson (2010) and Fijalkow, Lagarde, Ohlmann, and Serre (2021) from the identity testing perspective. Our main results are the following: (1) We construct nonassociative algebras (both commutative and noncommutative) which have no low degree identities. As a result, we obtain the first Amitsur-Levitzki type theorems over nonassociative polynomial algebras. As a direct consequence, we obtain randomized polynomial-time black-box PIT algorithms for nonassociative polynomials which allow evaluation over such algebras. (2) On the derandomization side, we give a deterministic polynomial-time identity testing algorithm for nonassociative polynomials given by arithmetic circuits in the white-box setting. Previously, such an algorithm was known with the additional restriction of noncommutativity. (3) In the black-box setting, we construct a hitting set of quasipolynomial-size for nonassociative polynomials computed by arithmetic circuits of small depth. Understanding the black-box complexity of identity testing, even in the randomized setting, was open prior to our work.
3D Gaussian Modeling and Ray Marching of OpenVDB datasets for Scientific Visualization
Venue: arXiv cs.GR 2025
Year: 2025

3D Gaussians are currently being heavily investigated for their scene modeling and compression abilities. In 3D volumes, their use is being explored for representing dense volumes as sparsely as possible. However, most of these methods begin with a memory inefficient data format. Specially in Scientific Visualization(SciVis), where most popular formats are dense-grid data structures that store every grid cell, irrespective of its contribution. OpenVDB library and data format were introduced for representing sparse volumetric data specifically for visual effects use cases such as clouds, fire, fluids etc. It avoids storing empty cells by masking them during storage. It presents an opportunity for use in SciVis, specifically as a modeling framework for conversion to 3D Gaussian particles for further compression and for a unified modeling approach for different scientific volume types. This compression head-start is non-trivial and this paper would like to present this with a rendering algorithm based on line integration implemented in OptiX8.1 for calculating 3D Gaussians contribution along a ray for optical-depth accumulation. For comparing the rendering results of our ray marching Gaussians renderer, we also implement a SciVis style primary-ray only NanoVDB HDDA based ray marcher for OpenVDB voxel grids. Finally, this paper also explores application of this Gaussian model to formats of volumes other than regular grids, such as AMR volumes and point clouds, using internal representation of OpenVDB grid class types for data hierarchy and subdivision structure.
Mechanizing Synthetic Tait Computability in Istari
Venue: arXiv cs.PL 2025
Year: 2025

Categorical gluing is a powerful technique for proving meta-theorems of type theories such as canonicity and normalization. Synthetic Tait Computability (STC) provides an abstract treatment of the complex gluing models by internalizing the gluing category into a modal dependent type theory with a phase distinction. This work presents a mechanization of STC in the Istari proof assistant. Istari is a Martin-Löf-style extensional type theory with equality reflection, which avoids much of the explicit transport reasoning typically found in intensional proof assistants. This work develops a reusable library for synthetic phase distinction, including modalities, extension types, and strict glue types, and applies it to two case studies: (1) a canonicity model for dependent type theory with dependent products and booleans with large elimination, and (2) a Kripke canonicity model for the cost-aware logical framework. Our results demonstrate that the core STC constructions can be formalized essentially verbatim in Istari, preserving the elegance of the on-paper arguments while ensuring machine-checked correctness.