OpenRaiser/Intern-Atlas huggingface dataset:
PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
Venue: arXiv cs.SE 2025
Year: 2025

The rapid emergence of pretrained models (PTMs) has attracted significant attention from both Deep Learning (DL) researchers and downstream application developers. However, selecting appropriate PTMs remains challenging because existing methods typically rely on keyword-based searches in which the keywords are often derived directly from function descriptions. This often fails to fully capture user intent and makes it difficult to identify suitable models when developers also consider factors such as bias mitigation, hardware requirements, or license compliance. To address the limitations of keyword-based model search, we propose PTMPicker to accurately identify suitable PTMs. We first define a structured template composed of common and essential attributes for PTMs and then PTMPicker represents both candidate models and user-intended features (i.e., model search requests) in this unified format. To determine whether candidate models satisfy user requirements, it computes embedding similarities for function-related attributes and uses well-crafted prompts to evaluate special constraints such as license compliance and hardware requirements. We scraped a total of 543,949 pretrained models from Hugging Face to prepare valid candidates for selection. PTMPicker then represented them in the predefined structured format by extracting their associated descriptions. Guided by the extracted metadata, we synthesized a total of 15,207 model search requests with carefully designed prompts, as no such search requests are readily available. Experiments on the curated PTM dataset and the synthesized model search requests show that PTMPicker can help users effectively identify models,with 85% of the sampled requests successfully locating appropriate PTMs within the top-10 ranked candidates.
A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labels
Venue: arXiv cs.LG 2025
Year: 2025

Multi-view learning is widely applied to real-life datasets, such as multiple omics biological data, but it often suffers from both missing views and missing labels. Prior probabilistic approaches addressed the missing view problem by using a product-of-experts scheme to aggregate representations from present views and achieved superior performance over deterministic classifiers, using the information bottleneck (IB) principle. However, the IB framework is inherently fully supervised and cannot leverage unlabeled data. In this work, we propose a semi-supervised generative model that utilizes both labeled and unlabeled samples in a unified framework. Our method maximizes the likelihood of unlabeled samples to learn a latent space shared with the IB on labeled data. We also perform cross-view mutual information maximization in the latent space to enhance the extraction of shared information across views. Compared to existing approaches, our model achieves better predictive and imputation performance on both image and multi-omics data with missing views and limited labeled samples.
On Strong and Weak Admissibility in Non-Flat Assumption-Based Argumentation
Venue: arXiv cs.AI 2025
Year: 2025

In this work, we broaden the investigation of admissibility notions in the context of assumption-based argumentation (ABA). More specifically, we study two prominent alternatives to the standard notion of admissibility from abstract argumentation, namely strong and weak admissibility, and introduce the respective preferred, complete and grounded semantics for general (sometimes called non-flat) ABA. To do so, we use abstract bipolar set-based argumentation frameworks (BSAFs) as formal playground since they concisely capture the relations between assumptions and are expressive enough to represent general non-flat ABA frameworks, as recently shown. While weak admissibility has been recently investigated for a restricted fragment of ABA in which assumptions cannot be derived (flat ABA), strong admissibility has not been investigated for ABA so far. We introduce strong admissibility for ABA and investigate desirable properties. We furthermore extend the recent investigations of weak admissibility in the flat ABA fragment to the non-flat case. We show that the central modularization property is maintained under classical, strong, and weak admissibility. We also show that strong and weakly admissible semantics in non-flat ABA share some of the shortcomings of standard admissible semantics and discuss ways to address these.
Versatile Video Tokenization with Generative 2D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Video tokenization procedure is critical for a wide range of video processing tasks. Most existing approaches directly transform video into fixed-grid and patch-wise tokens, which exhibit limited versatility. Spatially, uniformly allocating a fixed number of tokens often leads to over-encoding in low-information regions. Temporally, reducing redundancy remains challenging without explicitly distinguishing between static and dynamic content. In this work, we propose the Gaussian Video Transformer (GVT), a versatile video tokenizer built upon a generative 2D Gaussian Splatting (2DGS) strategy. We first extract latent rigid features from a video clip and represent them with a set of 2D Gaussians generated by our proposed Spatio-Temporal Gaussian Embedding (STGE) mechanism in a feed-forward manner. Such generative 2D Gaussians not only enhance spatial adaptability by assigning higher (resp., lower) rendering weights to regions with higher (resp., lower) information content during rasterization, but also improve generalization by avoiding per-video optimization.To enhance the temporal versatility, we introduce a Gaussian Set Partitioning (GSP) strategy that separates the 2D Gaussians into static and dynamic sets, which explicitly model static content shared across different time-steps and dynamic content specific to each time-step, enabling a compact representation.We primarily evaluate GVT on the video reconstruction, while also assessing its performance on action recognition and compression using the UCF101, Kinetics, and DAVIS datasets. Extensive experiments demonstrate that GVT achieves a state-of-the-art video reconstruction quality, outperforms the baseline MAGVIT-v2 in action recognition, and delivers comparable compression performance.
UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in vision-language models (VLMs) have demonstrated strong generalization in natural image tasks. However, their performance often degrades on unmanned aerial vehicle (UAV)-based aerial imagery, which features high resolution, complex spatial semantics, and strict real-time constraints. These challenges limit the applicability of general-purpose VLMs to structured aerial reasoning tasks. To address these challenges, we propose UAV-VL-R1, a lightweight VLM explicitly designed for aerial visual reasoning. It is trained using a hybrid method that combines supervised fine-tuning (SFT) and multi-stage reinforcement learning (RL). We leverage the group relative policy optimization (GRPO) algorithm to promote structured and interpretable reasoning through rule-guided rewards and intra-group policy alignment. To support model training and evaluation, we introduce a high-resolution visual question answering dataset named HRVQA-VL, which consists of 50,019 annotated samples covering eight UAV-relevant reasoning tasks, including object counting, transportation recognition, and spatial scene inference. Experimental results show that UAV-VL-R1 achieves a 48.17% higher zero-shot accuracy than the Qwen2-VL-2B-Instruct baseline and even outperforms its 72B-scale variant, which is 36x larger, on multiple tasks. Ablation studies reveal that while SFT improves semantic alignment, it may reduce reasoning diversity in mathematical tasks. GRPO-based RL compensates for this limitation by enhancing logical flexibility and the robustness of inference. Additionally, UAV-VL-R1 requires only 3.9GB of memory under FP16 inference and can be quantized to 2.5GB with INT8, supporting real-time deployment on resource-constrained UAV platforms.
A Coarse-to-Fine Human Pose Estimation Method based on Two-stage Distillation and Progressive Graph Neural Network
Venue: arXiv cs.CV 2025
Year: 2025

Human pose estimation has been widely applied in the human-centric understanding and generation, but most existing state-of-the-art human pose estimation methods require heavy computational resources for accurate predictions. In order to obtain an accurate, robust yet lightweight human pose estimator, one feasible way is to transfer pose knowledge from a powerful teacher model to a less-parameterized student model by knowledge distillation. However, the traditional knowledge distillation framework does not fully explore the contextual information among human joints. Thus, in this paper, we propose a novel coarse-to-fine two-stage knowledge distillation framework for human pose estimation. In the first-stage distillation, we introduce the human joints structure loss to mine the structural information among human joints so as to transfer high-level semantic knowledge from the teacher model to the student model. In the second-stage distillation, we utilize an Image-Guided Progressive Graph Convolutional Network (IGP-GCN) to refine the initial human pose obtained from the first-stage distillation and supervise the training of the IGP-GCN in the progressive way by the final output pose of teacher model. The extensive experiments on the benchmark dataset: COCO keypoint and CrowdPose datasets, show that our proposed method performs favorably against lots of the existing state-of-the-art human pose estimation methods, especially for the more complex CrowdPose dataset, the performance improvement of our model is more significant.
Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM
Venue: arXiv cs.LG 2025
Year: 2025

With the intensification of global climate change, accurate prediction of air quality indicators, especially PM2.5 concentration, has become increasingly important in fields such as environmental protection, public health, and urban management. To address this, we propose an air quality PM2.5 index prediction model based on a hybrid CNN-LSTM architecture. The model effectively combines Convolutional Neural Networks (CNN) for local spatial feature extraction and Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in time series data. Using a multivariate dataset collected from an industrial area in Beijing between 2010 and 2015 -- which includes hourly records of PM2.5 concentration, temperature, dew point, pressure, wind direction, wind speed, and precipitation -- the model predicts the average PM2.5 concentration over 6-hour intervals. Experimental results show that the model achieves a root mean square error (RMSE) of 5.236, outperforming traditional time series models in both accuracy and generalization. This demonstrates its strong potential in real-world applications such as air pollution early warning systems. However, due to the complexity of multivariate inputs, the model demands high computational resources, and its ability to handle diverse atmospheric factors still requires optimization. Future work will focus on enhancing scalability and expanding support for more complex multivariate weather prediction tasks.
Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Multi-hop question answering (MHQA) requires integrating knowledge scattered across multiple passages to derive the correct answer. Traditional retrieval-augmented generation (RAG) methods primarily focus on coarse-grained textual semantic similarity and ignore structural associations among dispersed knowledge, which limits their effectiveness in MHQA tasks. GraphRAG methods address this by leveraging knowledge graphs (KGs) to capture structural associations, but they tend to overly rely on structural information and fine-grained word- or phrase-level retrieval, resulting in an underutilization of textual semantics. In this paper, we propose a novel RAG approach called HGRAG for MHQA that achieves cross-granularity integration of structural and semantic information via hypergraphs. Structurally, we construct an entity hypergraph where fine-grained entities serve as nodes and coarse-grained passages as hyperedges, and establish knowledge association through shared entities. Semantically, we design a hypergraph retrieval method that integrates fine-grained entity similarity and coarse-grained passage similarity via hypergraph diffusion. Finally, we employ a retrieval enhancement module, which further refines the retrieved results both semantically and structurally, to obtain the most relevant passages as context for answer generation with the LLM. Experimental results on benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in QA performance, and achieves a 6$\times$ speedup in retrieval efficiency.
Graph Neural Diffusion via Generalized Opinion Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

There has been a growing interest in developing diffusion-based Graph Neural Networks (GNNs), building on the connections between message passing mechanisms in GNNs and physical diffusion processes. However, existing methods suffer from three critical limitations: (1) they rely on homogeneous diffusion with static dynamics, limiting adaptability to diverse graph structures; (2) their depth is constrained by computational overhead and diminishing interpretability; and (3) theoretical understanding of their convergence behavior remains limited. To address these challenges, we propose GODNF, a Generalized Opinion Dynamics Neural Framework, which unifies multiple opinion dynamics models into a principled, trainable diffusion mechanism. Our framework captures heterogeneous diffusion patterns and temporal dynamics via node-specific behavior modeling and dynamic neighborhood influence, while ensuring efficient and interpretable message propagation even at deep layers. We provide a rigorous theoretical analysis demonstrating GODNF's ability to model diverse convergence configurations. Extensive empirical evaluations of node classification and influence estimation tasks confirm GODNF's superiority over state-of-the-art GNNs.
Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
Venue: arXiv cs.LG 2025
Year: 2025

Instruction fine-tuned large language models (LLMs) enable a simple zero-shot or few-shot prompting paradigm, also known as in-context learning, for building prediction models. This convenience, combined with continued advances in LLM capability, has the potential to drive their adoption across a broad range of domains, including high-stakes applications where group fairness -- preventing disparate impacts across demographic groups -- is essential. The majority of existing approaches to enforcing group fairness on LLM-based classifiers rely on traditional fair algorithms applied via model fine-tuning or head-tuning on final-layer embeddings, but they are no longer applicable to closed-weight LLMs under the in-context learning setting, which include some of the most capable commercial models today, such as GPT-4, Gemini, and Claude. In this paper, we propose a framework for deriving fair classifiers from closed-weight LLMs via prompting: the LLM is treated as a feature extractor, and features are elicited from its probabilistic predictions (e.g., token log probabilities) using prompts strategically designed for the specified fairness criterion to obtain sufficient statistics for fair classification; a fair algorithm is then applied to these features to train a lightweight fair classifier in a post-hoc manner. Experiments on five datasets, including three tabular ones, demonstrate strong accuracy-fairness tradeoffs for the classifiers derived by our framework from both open-weight and closed-weight LLMs; in particular, our framework is data-efficient and outperforms fair classifiers trained on LLM embeddings (i.e., head-tuning) or from scratch on raw tabular features.
UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs?
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have demonstrated potential in reasoning tasks, but their performance on linguistics puzzles remains consistently poor. These puzzles, often derived from Linguistics Olympiad (LO) contests, provide a minimal contamination environment to assess LLMs' linguistic reasoning abilities across low-resource languages. This work analyses LLMs' performance on 629 problems across 41 low-resource languages by labelling each with linguistically informed features to unveil weaknesses. Our analyses show that LLMs struggle with puzzles involving higher morphological complexity and perform better on puzzles involving linguistic features that are also found in English. We also show that splitting words into morphemes as a pre-processing step improves solvability, indicating a need for more informed and language-specific tokenisers. These findings thus offer insights into some challenges in linguistic reasoning and modelling of low-resource languages.
Allen: Rethinking MAS Design through Step-Level Policy Autonomy
Venue: arXiv cs.MA 2025
Year: 2025

We introduce a new Multi-Agent System (MAS) - Allen, designed to address two core challenges in current MAS design: (1) improve system's policy autonomy, empowering agents to dynamically adapt their behavioral strategies, and (2) achieving the trade-off between collaborative efficiency, task supervision, and human oversight in complex network topologies. Our core insight is to redefine the basic execution unit in the MAS, allowing agents to autonomously form different patterns by combining these units. We have constructed a four-tier state architecture (Task, Stage, Agent, Step) to constrain system behavior from both task-oriented and execution-oriented perspectives. This achieves a unification of topological optimization and controllable progress. Allen grants unprecedented Policy Autonomy, while making a trade-off for the controllability of the collaborative structure. The project code has been open source at: https://github.com/motern88/Allen
Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study
Venue: arXiv cs.CV 2025
Year: 2025

Pedestrian segmentation in automotive perception systems faces critical safety challenges due to metamerism in RGB imaging, where pedestrians and backgrounds appear visually indistinguishable.. This study investigates the potential of hyperspectral imaging (HSI) for enhanced pedestrian segmentation in urban driving scenarios using the Hyperspectral City v2 (H-City) dataset. We compared standard RGB against two dimensionality-reduction approaches by converting 128-channel HSI data into three-channel representations: Principal Component Analysis (PCA) and optimal band selection using Contrast Signal-to-Noise Ratio with Joint Mutual Information Maximization (CSNR-JMIM). Three semantic segmentation models were evaluated: U-Net, DeepLabV3+, and SegFormer. CSNR-JMIM consistently outperformed RGB with an average improvements of 1.44% in Intersection over Union (IoU) and 2.18% in F1-score for pedestrian segmentation. Rider segmentation showed similar gains with 1.43% IoU and 2.25% F1-score improvements. These improved performance results from enhanced spectral discrimination of optimally selected HSI bands effectively reducing false positives. This study demonstrates robust pedestrian segmentation through optimal HSI band selection, showing significant potential for safety-critical automotive applications.
Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning
Venue: arXiv cs.SE 2025
Year: 2025

Logging code is written by developers to capture system runtime behavior and plays a vital role in debugging, performance analysis, and system monitoring. However, defects in logging code can undermine the usefulness of logs and lead to misinterpretations. Although prior work has identified several logging defect patterns and provided valuable insights into logging practices, these studies often focus on a narrow range of defect patterns derived from limited sources (e.g., commit histories) and lack a systematic and comprehensive analysis. Moreover, large language models (LLMs) have demonstrated promising generalization and reasoning capabilities across a variety of code-related tasks, yet their potential for detecting logging code defects remains largely unexplored. In this paper, we derive a comprehensive taxonomy of logging code defects, which encompasses seven logging code defect patterns with 14 detailed scenarios. We further construct a benchmark dataset, \dataset, consisting of 164 developer-verified real-world logging defects. Then we propose an automated framework that leverages various prompting strategies and contextual information to evaluate LLMs' capability in detecting and reasoning logging code defects. Experimental results reveal that LLMs generally struggle to accurately detect and reason logging code defects based on the source code only. However, incorporating proper knowledge (e.g., detailed scenarios of defect patterns) can lead to 10.9\% improvement in detection accuracy. Overall, our findings provide actionable guidance for practitioners to avoid common defect patterns and establish a foundation for improving LLM-based reasoning in logging code defect detection.
NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
Venue: arXiv cs.LG 2025
Year: 2025

With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs have become a significant challenge. Model reuse has been widely applied to reduce training costs, but indiscriminately reusing entire models may incur significant inference overhead. Consequently, DNN modularization has gained attention, enabling module reuse by decomposing DNN models. The emerging modularizing-while-training (MwT) paradigm, which incorporates modularization into training, outperforms modularizing-after-training approaches. However, existing MwT methods focus on small-scale CNN models at the convolutional kernel level and struggle with diverse DNNs and large-scale models, particularly Transformer-based models. To address these limitations, we propose NeMo, a scalable and generalizable MwT approach. NeMo operates at the neuron level fundamental component common to all DNNs-ensuring applicability to Transformers and various architectures. We design a contrastive learning-based modular training method with an effective composite loss function, enabling scalability to large-scale models. Comprehensive experiments on two Transformer-based models and four CNN models across two classification datasets demonstrate NeMo's superiority over state-of-the-art MwT methods. Results show average gains of 1.72% in module classification accuracy and 58.10% reduction in module size, demonstrating efficacy across both CNN and large-scale Transformer-based models. A case study on open-source projects shows NeMo's potential benefits in practical scenarios, offering a promising approach for scalable and generalizable DNN modularization.