OpenRaiser/Intern-Atlas huggingface dataset:
What Builds Effective In-Context Examples for Code Generation?
Venue: arXiv cs.SE 2025
Year: 2025

In-Context Learning (ICL) has emerged as a promising solution to enhance the code generation capabilities of Large Language Models (LLMs), which incorporates code examples inside the prompt to let LLMs learn from demonstrations. However, despite the substantial effectiveness of the code example-based ICL approach, the specific features (e.g., identifier naming styles, code formatting, solution insight) within the ICL-provided code examples that significantly contribute to the ICL's effectiveness remain unclear. This paper systematically investigates the impact of various code features on ICL with code examples through controlled ablation studies. Our findings reveal that the appropriate naming of variables and functions is crucial for effective code generation, with their elimination leading to performance decreases of up to 30 percentage points. We further demonstrate that LLMs prioritize semantically meaningful identifier names over formatting conventions, with language-specific preferences regarding identifier verbosity. Additionally, our investigation into ICL's potential for enhancing reflection and inference capabilities reveals that current LLMs struggle to extract generalizable problem-solving insights from similar code solutions, despite being capable of utilizing direct information effectively. These findings are expected to provide valuable insights for optimizing ICL systems in code generation applications and highlight fundamental challenges in reflection-based learning for code generation tasks.
MotionSwap
Venue: arXiv cs.CV 2025
Year: 2025

Face swapping technology has gained significant attention in both academic research and commercial applications. This paper presents our implementation and enhancement of SimSwap, an efficient framework for high fidelity face swapping. We introduce several improvements to the original model, including the integration of self and cross-attention mechanisms in the generator architecture, dynamic loss weighting, and cosine annealing learning rate scheduling. These enhancements lead to significant improvements in identity preservation, attribute consistency, and overall visual quality. Our experimental results, spanning 400,000 training iterations, demonstrate progressive improvements in generator and discriminator performance. The enhanced model achieves better identity similarity, lower FID scores, and visibly superior qualitative results compared to the baseline. Ablation studies confirm the importance of each architectural and training improvement. We conclude by identifying key future directions, such as integrating StyleGAN3, improving lip synchronization, incorporating 3D facial modeling, and introducing temporal consistency for video-based applications.
Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) offer new opportunities for scalable analysis of online discourse. Yet their use in multilingual social science research remains constrained by model size, cost and linguistic bias. We develop a lightweight, open-source LLM framework using fine-tuned LLaMA 3.2-3B models to classify immigration-related tweets across 13 languages. Unlike prior work relying on BERT style models or translation pipelines, we combine topic classification with stance detection and demonstrate that LLMs fine-tuned in just one or two languages can generalize topic understanding to unseen languages. Capturing ideological nuance, however, benefits from multilingual fine-tuning. Our approach corrects pretraining biases with minimal data from under-represented languages and avoids reliance on proprietary systems. With 26-168x faster inference and over 1000x cost savings compared to commercial LLMs, our method supports real-time analysis of billions of tweets. This scale-first framework enables inclusive, reproducible research on public attitudes across linguistic and cultural contexts.
Echoes of Automation: The Increasing Use of LLMs in Newsmaking
Venue: arXiv cs.CL 2025
Year: 2025

The rapid rise of Generative AI (GenAI), particularly LLMs, poses concerns for journalistic integrity and authorship. This study examines AI-generated content across over 40,000 news articles from major, local, and college news media, in various media formats. Using three advanced AI-text detectors (e.g., Binoculars, Fast-Detect GPT, and GPTZero), we find substantial increase of GenAI use in recent years, especially in local and college news. Sentence-level analysis reveals LLMs are often used in the introduction of news, while conclusions usually written manually. Linguistic analysis shows GenAI boosts word richness and readability but lowers formality, leading to more uniform writing styles, particularly in local media.
TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2025
Year: 2025

Recent unsupervised domain adaptation (UDA) methods have shown great success in addressing classical domain shifts (e.g., synthetic-to-real), but they still suffer under complex shifts (e.g. geographical shift), where both the background and object appearances differ significantly across domains. Prior works showed that the language modality can help in the adaptation process, exhibiting more robustness to such complex shifts. In this paper, we introduce TRUST, a novel UDA approach that exploits the robustness of the language modality to guide the adaptation of a vision model. TRUST generates pseudo-labels for target samples from their captions and introduces a novel uncertainty estimation strategy that uses normalised CLIP similarity scores to estimate the uncertainty of the generated pseudo-labels. Such estimated uncertainty is then used to reweight the classification loss, mitigating the adverse effects of wrong pseudo-labels obtained from low-quality captions. To further increase the robustness of the vision model, we propose a multimodal soft-contrastive learning loss that aligns the vision and language feature spaces, by leveraging captions to guide the contrastive training of the vision model on target images. In our contrastive loss, each pair of images acts as both a positive and a negative pair and their feature representations are attracted and repulsed with a strength proportional to the similarity of their captions. This solution avoids the need for hardly determining positive and negative pairs, which is critical in the UDA setting. Our approach outperforms previous methods, setting the new state-of-the-art on classical (DomainNet) and complex (GeoNet) domain shifts. The code will be available upon acceptance.
Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
Venue: arXiv cs.IR 2025
Year: 2025

Cold-start challenges in recommender systems necessitate leveraging auxiliary features beyond user-item interactions. However, the presence of irrelevant or noisy features can degrade predictive performance, whereas an excessive number of features increases computational demands, leading to higher memory consumption and prolonged training times. To address this, we propose a feature selection strategy that prioritizes the user behavioral information. Our method enhances the feature representation by incorporating correlations from collaborative behavior data using a hybrid matrix factorization technique and then ranks features using a mechanism based on the maximum volume algorithm. This approach identifies the most influential features, striking a balance between recommendation accuracy and computational efficiency. We conduct an extensive evaluation across various datasets and hybrid recommendation models, demonstrating that our method excels in cold-start scenarios by selecting minimal yet highly effective feature subsets. Even under strict feature reduction, our approach surpasses existing feature selection techniques while maintaining superior efficiency.
Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data
Venue: arXiv cs.HC 2025
Year: 2025

Non-technical end-users increasingly rely on AI code generation to perform technical tasks like data analysis. However, large language models (LLMs) remain unreliable, and it is unclear whether end-users can effectively identify model errors $\unicode{x2014}$ especially in realistic and domain-specific scenarios. We surveyed marketing and sales professionals to assess their ability to critically evaluate LLM-generated analyses of marketing data. Participants were shown natural language explanations of the AI's code, repeatedly informed the AI often makes mistakes, and explicitly prompted to identify them. Yet, participants frequently failed to detect critical flaws that could compromise decision-making, many of which required no technical knowledge to recognize. To investigate why, we reformatted AI responses into clearly delineated steps and provided alternative approaches for each decision to support critical evaluation. While these changes had a positive effect, participants often struggled to reason through the AI's steps and alternatives. Our findings suggest that business professionals cannot reliably verify AI-generated data analyses on their own and explore reasons why to inform future designs. As non-programmers adopt code-generating AI for technical tasks, unreliable AI and insufficient human oversight poses risks of unsafe or low-quality decisions.
WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion
Venue: arXiv cs.CV 2025
Year: 2025

Urbanization, climate change, and agricultural stress are increasing the demand for precise and timely environmental monitoring. Land Surface Temperature (LST) is a key variable in this context and is retrieved from remote sensing satellites. However, these systems face a trade-off between spatial and temporal resolution. While spatio-temporal fusion methods offer promising solutions, few have addressed the estimation of daily LST at 10 m resolution. In this study, we present WGAST, a weakly-supervised generative network for daily 10 m LST estimation via spatio-temporal fusion of Terra MODIS, Landsat 8, and Sentinel-2. WGAST is the first end-to-end deep learning framework designed for this task. It adopts a conditional generative adversarial architecture, with a generator composed of four stages: feature extraction, fusion, LST reconstruction, and noise suppression. The first stage employs a set of encoders to extract multi-level latent representations from the inputs, which are then fused in the second stage using cosine similarity, normalization, and temporal attention mechanisms. The third stage decodes the fused features into high-resolution LST, followed by a Gaussian filter to suppress high-frequency noise. Training follows a weakly supervised strategy based on physical averaging principles and reinforced by a PatchGAN discriminator. Experiments demonstrate that WGAST outperforms existing methods in both quantitative and qualitative evaluations. Compared to the best-performing baseline, on average, WGAST reduces RMSE by 17.05% and improves SSIM by 4.22%. Furthermore, WGAST effectively captures fine-scale thermal patterns, as validated against near-surface air temperature measurements from 33 near-ground sensors. The code is available at https://github.com/Sofianebouaziz1/WGAST.git.
Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models
Venue: arXiv cs.CL 2025
Year: 2025

Biomedical named entity recognition (NER) is a high-utility natural language processing (NLP) task, and large language models (LLMs) show promise particularly in few-shot settings (i.e., limited training data). In this article, we address the performance challenges of LLMs for few-shot biomedical NER by investigating a dynamic prompting strategy involving retrieval-augmented generation (RAG). In our approach, the annotated in-context learning examples are selected based on their similarities with the input texts, and the prompt is dynamically updated for each instance during inference. We implemented and optimized static and dynamic prompt engineering techniques and evaluated them on five biomedical NER datasets. Static prompting with structured components increased average F1-scores by 12% for GPT-4, and 11% for GPT-3.5 and LLaMA 3-70B, relative to basic static prompting. Dynamic prompting further improved performance, with TF-IDF and SBERT retrieval methods yielding the best results, improving average F1-scores by 7.3% and 5.6% in 5-shot and 10-shot settings, respectively. These findings highlight the utility of contextually adaptive prompts via RAG for biomedical NER.
What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision-Language Models (LVLMs), empowered by the success of Large Language Models (LLMs), have achieved impressive performance across domains. Despite the great advances in LVLMs, they still suffer from the unavailable object hallucination issue, which tends to generate objects inconsistent with the image content. The most commonly used Polling-based Object Probing Evaluation (POPE) benchmark evaluates this issue by sampling negative categories according to category-level statistics, \textit{e.g.}, category frequencies and co-occurrence. However, with the continuous advancement of LVLMs, the POPE benchmark has shown diminishing effectiveness in assessing object hallucination, as it employs a simplistic sampling strategy that overlooks image-specific information and restricts distractors to negative object categories only. In this paper, we introduce the Hallucination searching-based Object Probing Evaluation (HOPE) benchmark, aiming to generate the most misleading distractors (\textit{i.e.}, non-existent objects or incorrect image descriptions) that can trigger hallucination in LVLMs, which serves as a means to more rigorously assess their immunity to hallucination. To explore the image-specific information, the content-aware hallucination searching leverages Contrastive Language-Image Pre-Training (CLIP) to approximate the predictive behavior of LVLMs by selecting negative objects with the highest predicted likelihood as distractors. To expand the scope of hallucination assessment, the description-based hallucination searching constructs highly misleading distractors by pairing true objects with false descriptions. Experimental results show that HOPE leads to a precision drop of at least 9\% and up to 23\% across various state-of-the-art LVLMs, significantly outperforming POPE in exposing hallucination vulnerabilities. The code is available at https://github.com/xiemk/HOPE.
Historical Prediction Attention Mechanism based Trajectory Forecasting for Proactive Work Zone Safety in a Digital Twin Environment
Venue: arXiv cs.OH 2025
Year: 2025

Proactive safety systems aim to mitigate risks by anticipating potential conflicts between vehicles and enabling early intervention to prevent work zone-related crashes. This study presents an infrastructure-enabled proactive work zone safety warning system that leverages a Digital Twin environment, integrating real-time multi-sensor data, detailed High-Definition (HD) maps, and a historical prediction attention mechanism-based trajectory prediction model. Using a co-simulation environment that combines Simulation of Urban MObility (SUMO) and CAR Learning to Act (CARLA) simulators, along with Lanelet2 HD maps and the Historical Prediction Network (HPNet) model, we demonstrate effective trajectory prediction and early warning generation for vehicle interactions in freeway work zones. To evaluate the accuracy of predicted trajectories, we use two standard metrics: Joint Average Displacement Error (ADE) and Joint Final Displacement Error (FDE). Specifically, the infrastructure-enabled HPNet model demonstrates superior performance on the work-zone datasets generated from the co-simulation environment, achieving a minimum Joint FDE of 0.3228 meters and a minimum Joint ADE of 0.1327 meters, lower than the benchmarks on the Argoverse (minJointFDE: 1.0986 m, minJointADE: 0.7612 m) and Interaction (minJointFDE: 0.8231 m, minJointADE: 0.2548 m) datasets. In addition, our proactive safety warning generation application, utilizing vehicle bounding boxes and probabilistic conflict modeling, demonstrates its capability to issue alerts for potential vehicle conflicts.
Factor Augmented Supervised Learning with Text Embeddings
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) generate text embeddings from text data, producing vector representations that capture the semantic meaning and contextual relationships of words. However, the high dimensionality of these embeddings often impedes efficiency and drives up computational cost in downstream tasks. To address this, we propose AutoEncoder-Augmented Learning with Text (AEALT), a supervised, factor-augmented framework that incorporates dimension reduction directly into pre-trained LLM workflows. First, we extract embeddings from text documents; next, we pass them through a supervised augmented autoencoder to learn low-dimensional, task-relevant latent factors. By modeling the nonlinear structure of complex embeddings, AEALT outperforms conventional deep-learning approaches that rely on raw embeddings. We validate its broad applicability with extensive experiments on classification, anomaly detection, and prediction tasks using multiple real-world public datasets. Numerical results demonstrate that AEALT yields substantial gains over both vanilla embeddings and several standard dimension reduction methods.
On the effectiveness of multimodal privileged knowledge distillation in two vision transformer based diagnostic applications
Venue: arXiv cs.CV 2025
Year: 2025

Deploying deep learning models in clinical practice often requires leveraging multiple data modalities, such as images, text, and structured data, to achieve robust and trustworthy decisions. However, not all modalities are always available at inference time. In this work, we propose multimodal privileged knowledge distillation (MMPKD), a training strategy that utilizes additional modalities available solely during training to guide a unimodal vision model. Specifically, we used a text-based teacher model for chest radiographs (MIMIC-CXR) and a tabular metadata-based teacher model for mammography (CBIS-DDSM) to distill knowledge into a vision transformer student model. We show that MMPKD can improve the resulting attention maps' zero-shot capabilities of localizing ROI in input images, while this effect does not generalize across domains, as contrarily suggested by prior research.
Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering
Venue: arXiv cs.LG 2025
Year: 2025

Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This paper proposes a novel two-phase learning framework to address these challenges. In the first phase, the Isolation Forest algorithm performs unsupervised anomaly detection to identify potential fraud cases and reduce the volume of data requiring further analysis. In the second phase, a self-training Support Vector Machine (SVM) refines the predictions using both labeled and high-confidence pseudo-labeled samples, enabling robust semi-supervised learning. The proposed method is evaluated on the DataCo Smart Supply Chain Dataset, a comprehensive real-world supply chain dataset with fraud indicators. It achieves an F1-score of 0.817 while maintaining a false positive rate below 3.0%. These results demonstrate the effectiveness and efficiency of combining unsupervised pre-filtering with semi-supervised refinement for supply chain fraud detection under real-world constraints, though we acknowledge limitations regarding concept drift and the need for comparison with deep learning approaches.
Hypergraph Neural Network with State Space Models for Node Classification
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, graph neural networks (GNNs) have gained significant attention for node classification tasks on graph-structured data. However, traditional GNNs primarily focus on adjacency relationships between nodes, often overlooking the role-based characteristics that can provide complementary insights for learning expressive node representations. Existing frameworks for extracting role-based features are largely unsupervised and often fail to translate effectively into downstream predictive tasks. To address these limitations, we propose a hypergraph neural network with a state space model (HGMN). The model integrates role-aware representations into GNNs by combining hypergraph construction with state-space modeling in a principled manner. HGMN employs hypergraph construction techniques to capture higher-order relationships and leverages a learnable mamba transformer mechanism to fuse role-based and adjacency-based embeddings. By exploring two distinct hypergraph construction strategies, degree-based and neighborhood-based, the framework reinforces connectivity among nodes with structural similarity, thereby enriching the learned representations. Furthermore, the inclusion of hypergraph convolution layers enables the model to account for complex dependencies within hypergraph structures. To alleviate the over-smoothing problem encountered in deeper networks, we incorporate residual connections, which improve stability and promote effective feature propagation across layers. Comprehensive experiments on benchmark datasets including OGB, ACM, DBLP, IIP TerroristRel, Cora, Citeseer, and Pubmed demonstrate that HGMN consistently outperforms strong baselines in node classification tasks. These results support the claim that explicitly incorporating role-based features within a hypergraph framework offers tangible benefits for node classification tasks.
BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent
Venue: arXiv cs.CL 2025
Year: 2025

Deep-Research agents, which integrate large language models (LLMs) with search tools, have shown success in improving the effectiveness of handling complex queries that require iterative search planning and reasoning over search results. Evaluations on current benchmarks like BrowseComp relies on black-box live web search APIs, have notable limitations in (1) fairness: dynamic and opaque web APIs hinder fair comparisons and reproducibility of deep research methods; (2) transparency: lack of control over the document corpus makes it difficult to isolate retriever contributions. In other words, the current evaluations may compare a complete deep research system at a given time, but they do not foster well-controlled experiments to provide insights into the capability of underlying deep research LLMs. To address these challenges, we introduce BrowseComp-Plus, a benchmark derived from BrowseComp, employing a fixed, carefully curated corpus. Each query in BrowseComp-Plus includes human-verified supporting documents and mined challenging negatives, enabling controlled experimentation. The benchmark is shown to be effective in distinguishing the performance of deep research systems. For instance, the open-source model Search-R1, when paired with the BM25 retriever, achieves 3.86% accuracy, whereas the GPT-5 achieves 55.9%. Integrating the GPT-5 with the Qwen3-Embedding-8B retriever further enhances its accuracy to 70.1% with fewer search calls. This benchmark allows comprehensive evaluation and disentangled analysis of deep research agents and retrieval methods, fostering insights into retrieval effectiveness, citation accuracy, and context engineering in Deep-Research system.