OpenRaiser/Intern-Atlas huggingface dataset:
DeepRTE: Pre-trained Attention-based Neural Network for Radiative Transfer
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we propose a novel neural network approach, termed DeepRTE, to address the steady-state Radiative Transfer Equation (RTE). The RTE is a differential-integral equation that governs the propagation of radiation through a participating medium, with applications spanning diverse domains such as neutron transport, atmospheric radiative transfer, heat transfer, and optical imaging. Our DeepRTE framework demonstrates superior computational efficiency for solving the steady-state RTE, surpassing traditional methods and existing neural network approaches. This efficiency is achieved by embedding physical information through derivation of the RTE and mathematically-informed network architecture. Concurrently, DeepRTE achieves high accuracy with significantly fewer parameters, largely due to its incorporation of mechanisms such as multi-head attention. Furthermore, DeepRTE is a mesh-free neural operator framework with inherent zero-shot capability. This is achieved by incorporating Green's function theory and pre-training with delta-function inflow boundary conditions into both its architecture design and training data construction. The efficacy of the proposed approach is substantiated through comprehensive numerical experiments.
Certified algorithms for numerical semigroups in Rocq
Venue: arXiv cs.DM 2025
Year: 2025

A numerical semigroup is a co-finite submonoid of the monoid of non-negative integers under addition. Many properties of numerical semigroups rely on some fundamental invariants, such as, among others, the set of gaps (and its cardinality), the Apéry set or the Frobenius number. Algorithms for calculating invariants are currently based on computational tools, such as GAP, which lack proofs (either formal or informal) of their correctness. In this paper we introduce a Rocq formalization of numerical semigroups. Given the semigroup generators, we provide certified algorithms for computing some of the fundamental invariants: the set of gaps, of small elements, the Apéry set, the multiplicity, the conductor and the Frobenius number. To the best of our knowledge this is the first formalization of numerical semigroups in any proof assistant.
MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration
Venue: arXiv cs.CL 2025
Year: 2025

In recent years, multimodal large language models (MLLMs) have made significant progress but continue to face inherent challenges in multimodal reasoning, which requires multi-level (e.g., perception, reasoning) and multi-granular (e.g., multi-step reasoning chain) advanced inferencing. Prior work on estimating model confidence tends to focus on the overall response for training and calibration, but fails to assess confidence in each reasoning step, leading to undesirable hallucination snowballing. In this work, we present MMBoundary, a novel framework that advances the knowledge boundary awareness of MLLMs through reasoning step confidence calibration. To achieve this, we propose to incorporate complementary textual and cross-modal self-rewarding signals to estimate confidence at each step of the MLLM reasoning process. In addition to supervised fine-tuning MLLM on this set of self-rewarded confidence estimation signal for initial confidence expression warm-up, we introduce a reinforcement learning stage with multiple reward functions for further aligning model knowledge and calibrating confidence at each reasoning step, enhancing reasoning chain self-correction. Empirical results show that MMBoundary significantly outperforms existing methods across diverse domain datasets and metrics, achieving an average of 7.5% reduction in multimodal confidence calibration errors and up to 8.3% improvement in task performance.
REDDIX-NET: A Novel Dataset and Benchmark for Moderating Online Explicit Services
Venue: arXiv cs.CY 2025
Year: 2025

The rise of online platforms has enabled covert illicit activities, including online prostitution, to pose challenges for detection and regulation. In this study, we introduce REDDIX-NET, a novel benchmark dataset specifically designed for moderating online sexual services and going beyond traditional NSFW filters. The dataset is derived from thousands of web-scraped NSFW posts on Reddit and categorizes users into six behavioral classes reflecting different service offerings and user intentions. We evaluate the classification performance of state-of-the-art large language models (GPT-4, LlaMA 3.3-70B-Instruct, Gemini 1.5 Flash, Mistral 8x7B, Qwen 2.5 Turbo, Claude 3.5 Haiku) using advanced quantitative metrics, finding promising results with models like GPT-4 and Gemini 1.5 Flash. Beyond classification, we conduct sentiment and comment analysis, leveraging LLM and PLM-based approaches and metadata extraction to uncover behavioral and temporal patterns. These analyses reveal peak engagement times and distinct user interaction styles across categories. Our findings provide critical insights into AI-driven moderation and enforcement, offering a scalable framework for platforms to combat online prostitution and associated harms.
Unsupervised Transcript-assisted Video Summarization and Highlight Detection
Venue: arXiv cs.CV 2025
Year: 2025

Video consumption is a key part of daily life, but watching entire videos can be tedious. To address this, researchers have explored video summarization and highlight detection to identify key video segments. While some works combine video frames and transcripts, and others tackle video summarization and highlight detection using Reinforcement Learning (RL), no existing work, to the best of our knowledge, integrates both modalities within an RL framework. In this paper, we propose a multimodal pipeline that leverages video frames and their corresponding transcripts to generate a more condensed version of the video and detect highlights using a modality fusion mechanism. The pipeline is trained within an RL framework, which rewards the model for generating diverse and representative summaries while ensuring the inclusion of video segments with meaningful transcript content. The unsupervised nature of the training allows for learning from large-scale unannotated datasets, overcoming the challenge posed by the limited size of existing annotated datasets. Our experiments show that using the transcript in video summarization and highlight detection achieves superior results compared to relying solely on the visual content of the video.
Wireless Agentic AI with Retrieval-Augmented Multimodal Semantic Perception
Venue: arXiv cs.NI 2025
Year: 2025

The rapid development of multimodal AI and Large Language Models (LLMs) has greatly enhanced real-time interaction, decision-making, and collaborative tasks. However, in wireless multi-agent scenarios, limited bandwidth poses significant challenges to exchanging semantically rich multimodal information efficiently. Traditional semantic communication methods, though effective, struggle with redundancy and loss of crucial details. To overcome these challenges, we propose a Retrieval-Augmented Multimodal Semantic Communication (RAMSemCom) framework. RAMSemCom incorporates iterative, retrieval-driven semantic refinement tailored for distributed multi-agent environments, enabling efficient exchange of critical multimodal elements through local caching and selective transmission. Our approach dynamically optimizes retrieval using deep reinforcement learning (DRL) to balance semantic fidelity with bandwidth constraints. A comprehensive case study on multi-agent autonomous driving demonstrates that our DRL-based retrieval strategy significantly improves task completion efficiency and reduces communication overhead compared to baseline methods.
Score-based Generative Modeling for Conditional Independence Testing
Venue: arXiv cs.LG 2025
Year: 2025

Determining conditional independence (CI) relationships between random variables is a fundamental yet challenging task in machine learning and statistics, especially in high-dimensional settings. Existing generative model-based CI testing methods, such as those utilizing generative adversarial networks (GANs), often struggle with undesirable modeling of conditional distributions and training instability, resulting in subpar performance. To address these issues, we propose a novel CI testing method via score-based generative modeling, which achieves precise Type I error control and strong testing power. Concretely, we first employ a sliced conditional score matching scheme to accurately estimate conditional score and use Langevin dynamics conditional sampling to generate null hypothesis samples, ensuring precise Type I error control. Then, we incorporate a goodness-of-fit stage into the method to verify generated samples and enhance interpretability in practice. We theoretically establish the error bound of conditional distributions modeled by score-based generative models and prove the validity of our CI tests. Extensive experiments on both synthetic and real-world datasets show that our method significantly outperforms existing state-of-the-art methods, providing a promising way to revitalize generative model-based CI testing.
Investigating A Geometrical Solution to the Vergence-Accommodation Conflict for Targeted Movements in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

While virtual reality (VR) holds significant potential to revolutionize digital user interaction, how visual information is presented through VR head-mounted displays (HMDs) differs from naturalistic viewing and interactions in physical environments, leading to performance decrements. One critical challenge in VR development is the vergence-accommodation conflict (VAC), which arises due to the intrinsic constraints of approximating the natural viewing geometry through digital displays. Although various hardware and software solutions have been proposed to address VAC, no commercially viable option has been universally adopted by manufacturers. This paper presents and evaluates a software solution grounded in a vision-based geometrical model of VAC that mediates VAC's impact on movement in VR. This model predicts the impact of VAC as a constant offset to the vergence angle, distorting the binocular viewing geometry that results in movement undershooting. In Experiment 1, a 3D pointing task validated the model's predictions and demonstrated that VAC primarily affects online movements involving real-time visual feedback. Experiment 2 implemented a shader program to rectify the effect of VAC, improving movement accuracy by approximately 30%. Overall, this work presented a practical approach to reducing the impact of VAC on HMD-based manual interactions, enhancing the user experience in virtual environments.
TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image diffusion models have shown unprecedented generative capability, but their ability to produce undesirable concepts (e.g.~pornographic content, sensitive identities, copyrighted styles) poses serious concerns for privacy, fairness, and safety. {Concept erasure} aims to remove or suppress specific concept information in a generative model. In this paper, we introduce \textbf{TRACE (Trajectory-Constrained Attentional Concept Erasure)}, a novel method to erase targeted concepts from diffusion models while preserving overall generative quality. Our approach combines a rigorous theoretical framework, establishing formal conditions under which a concept can be provably suppressed in the diffusion process, with an effective fine-tuning procedure compatible with both conventional latent diffusion (Stable Diffusion) and emerging rectified flow models (e.g.~FLUX). We first derive a closed-form update to the model's cross-attention layers that removes hidden representations of the target concept. We then introduce a trajectory-aware finetuning objective that steers the denoising process away from the concept only in the late sampling stages, thus maintaining the model's fidelity on unrelated content. Empirically, we evaluate TRACE on multiple benchmarks used in prior concept erasure studies (object classes, celebrity faces, artistic styles, and explicit content from the I2P dataset). TRACE achieves state-of-the-art performance, outperforming recent methods such as ANT, EraseAnything, and MACE in terms of removal efficacy and output quality.
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Pedestrian Attribute Recognition (PAR) is an indispensable task in human-centered research and has made great progress in recent years with the development of deep neural networks. However, the potential vulnerability and anti-interference ability have still not been fully explored. To bridge this gap, this paper proposes the first adversarial attack and defense framework for pedestrian attribute recognition. Specifically, we exploit both global- and patch-level attacks on the pedestrian images, based on the pre-trained CLIP-based PAR framework. It first divides the input pedestrian image into non-overlapping patches and embeds them into feature embeddings using a projection layer. Meanwhile, the attribute set is expanded into sentences using prompts and embedded into attribute features using a pre-trained CLIP text encoder. A multi-modal Transformer is adopted to fuse the obtained vision and text tokens, and a feed-forward network is utilized for attribute recognition. Based on the aforementioned PAR framework, we adopt the adversarial semantic and label-perturbation to generate the adversarial noise, termed ASL-PAR. We also design a semantic offset defense strategy to suppress the influence of adversarial attacks. Extensive experiments conducted on both digital domains (i.e., PETA, PA100K, MSP60K, RAPv2) and physical domains fully validated the effectiveness of our proposed adversarial attack and defense strategies for the pedestrian attribute recognition. The source code of this paper will be released on https://github.com/Event-AHU/OpenPAR.
Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization
Venue: arXiv cs.CV 2025
Year: 2025

Fine-tuning pre-trained generative models with Reinforcement Learning (RL) has emerged as an effective approach for aligning outputs more closely with nuanced human preferences. In this paper, we investigate the application of Group Relative Policy Optimization (GRPO) to fine-tune next-scale visual autoregressive (VAR) models. Our empirical results demonstrate that this approach enables alignment to intricate reward signals derived from aesthetic predictors and CLIP embeddings, significantly enhancing image quality and enabling precise control over the generation style. Interestingly, by leveraging CLIP, our method can help VAR models generalize beyond their initial ImageNet distribution: through RL-driven exploration, these models can generate images aligned with prompts referencing image styles that were absent during pre-training. In summary, we show that RL-based fine-tuning is both efficient and effective for VAR models, benefiting particularly from their fast inference speeds, which are advantageous for online sampling, an aspect that poses significant challenges for diffusion-based alternatives.
Beyond Optimal Transport: Model-Aligned Coupling for Flow Matching
Venue: arXiv cs.CV 2025
Year: 2025

Flow Matching (FM) is an effective framework for training a model to learn a vector field that transports samples from a source distribution to a target distribution. To train the model, early FM methods use random couplings, which often result in crossing paths and lead the model to learn non-straight trajectories that require many integration steps to generate high-quality samples. To address this, recent methods adopt Optimal Transport (OT) to construct couplings by minimizing geometric distances, which helps reduce path crossings. However, we observe that such geometry-based couplings do not necessarily align with the model's preferred trajectories, making it difficult to learn the vector field induced by these couplings, which prevents the model from learning straight trajectories. Motivated by this, we propose Model-Aligned Coupling (MAC), an effective method that matches training couplings based not only on geometric distance but also on alignment with the model's preferred transport directions based on its prediction error. To avoid the time-costly match process, MAC proposes to select the top-$k$ fraction of couplings with the lowest error for training. Extensive experiments show that MAC significantly improves generation quality and efficiency in few-step settings compared to existing methods. Project page: https://yexionglin.github.io/mac
Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores
Venue: arXiv cs.AR 2025
Year: 2025

Optimizing performance and energy efficiency in many-core processors, especially within Non-Uniform Cache Access (NUCA) architectures, remains a critical challenge. The performance heterogeneity inherent in S-NUCA systems complicates task scheduling due to varying cache access latencies across cores. This paper introduces a novel QoS management policy to maintain application execution within predefined Quality of Service (QoS) targets, measured using the Application Heartbeats framework. QoS metrics like Heartbeats ensure predictable application performance in dynamic computing environments. The proposed policy dynamically controls QoS by orchestrating task migrations within the S-NUCA many-core system and adjusting the clock frequency of cores. After satisfying the QoS objectives, the policy optimizes energy efficiency, reducing overall system energy consumption without compromising performance constraints. Our work leverages the state-of-the-art multi-/many-core simulator {\em HotSniper}. We have extended it with two key components: an integrated heartbeat framework for precise, application-specific performance monitoring, and our QoS management policy that maintains application QoS requirements while minimizing the system's energy consumption. Experimental evaluations demonstrate that our approach effectively maintains desired QoS levels and achieves 18.7\% energy savings compared to state-of-the-art scheduling methods.
Beam-Guided Knowledge Replay for Knowledge-Rich Image Captioning using Vision-Language Model
Venue: arXiv cs.CV 2025
Year: 2025

Generating informative and knowledge-rich image captions remains a challenge for many existing captioning models, which often produce generic descriptions that lack specificity and contextual depth. To address this limitation, we propose KRCapVLM, a knowledge replay-based novel image captioning framework using vision-language model. We incorporate beam search decoding to generate more diverse and coherent captions. We also integrate attention-based modules into the image encoder to enhance feature representation. Finally, we employ training schedulers to improve stability and ensure smoother convergence during training. These proposals accelerate substantial gains in both caption quality and knowledge recognition. Our proposed model demonstrates clear improvements in both the accuracy of knowledge recognition and the overall quality of generated captions. It shows a stronger ability to generalize to previously unseen knowledge concepts, producing more informative and contextually relevant descriptions. These results indicate the effectiveness of our approach in enhancing the model's capacity to generate meaningful, knowledge-grounded captions across a range of scenarios.
VModA: An Effective Framework for Adaptive NSFW Image Moderation
Venue: arXiv cs.CV 2025
Year: 2025

Not Safe/Suitable for Work (NSFW) content is rampant on social networks and poses serious harm to citizens, especially minors. Current detection methods mainly rely on deep learning-based image recognition and classification. However, NSFW images are now presented in increasingly sophisticated ways, often using image details and complex semantics to obscure their true nature or attract more views. Although still understandable to humans, these images often evade existing detection methods, posing a significant threat. Further complicating the issue, varying regulations across platforms and regions create additional challenges for effective moderation, leading to detection bias and reduced accuracy. To address this, we propose VModA, a general and effective framework that adapts to diverse moderation rules and handles complex, semantically rich NSFW content across categories. Experimental results show that VModA significantly outperforms existing methods, achieving up to a 54.3% accuracy improvement across NSFW types, including those with complex semantics. Further experiments demonstrate that our method exhibits strong adaptability across categories, scenarios, and base VLMs. We also identified inconsistent and controversial label samples in public NSFW benchmark datasets, re-annotated them, and submitted corrections to the original maintainers. Two datasets have confirmed the updates so far. Additionally, we evaluate VModA in real-world scenarios to demonstrate its practical effectiveness.
Robust and Annotation-Free Wound Segmentation on Noisy Real-World Pressure Ulcer Images: Towards Automated DESIGN-R\textsuperscript{\textregistered} Assessment
Venue: arXiv cs.CV 2025
Year: 2025

Purpose: Accurate wound segmentation is essential for automated DESIGN-R scoring. However, existing models such as FUSegNet, which are trained primarily on foot ulcer datasets, often fail to generalize to wounds on other body sites. Methods: We propose an annotation-efficient pipeline that combines a lightweight YOLOv11n-based detector with the pre-trained FUSegNet segmentation model. Instead of relying on pixel-level annotations or retraining for new anatomical regions, our method achieves robust performance using only 500 manually labeled bounding boxes. This zero fine-tuning approach effectively bridges the domain gap and enables direct deployment across diverse wound types. This is an advance not previously demonstrated in the wound segmentation literature. Results: Evaluated on three real-world test sets spanning foot, sacral, and trochanter wounds, our YOLO plus FUSegNet pipeline improved mean IoU by 23 percentage points over vanilla FUSegNet and increased end-to-end DESIGN-R size estimation accuracy from 71 percent to 94 percent (see Table 3 for details). Conclusion: Our pipeline generalizes effectively across body sites without task-specific fine-tuning, demonstrating that minimal supervision, with 500 annotated ROIs, is sufficient for scalable, annotation-light wound segmentation. This capability paves the way for real-world DESIGN-R automation, reducing reliance on pixel-wise labeling, streamlining documentation workflows, and supporting objective and consistent wound scoring in clinical practice. We will publicly release the trained detector weights and configuration to promote reproducibility and facilitate downstream deployment.