OpenRaiser/Intern-Atlas huggingface dataset:
Improving realistic semi-supervised learning with doubly robust estimation
Venue: arXiv cs.LG 2025
Year: 2025

A major challenge in Semi-Supervised Learning (SSL) is the limited information available about the class distribution in the unlabeled data. In many real-world applications this arises from the prevalence of long-tailed distributions, where the standard pseudo-label approach to SSL is biased towards the labeled class distribution and thus performs poorly on unlabeled data. Existing methods typically assume that the unlabeled class distribution is either known a priori, which is unrealistic in most situations, or estimate it on-the-fly using the pseudo-labels themselves. We propose to explicitly estimate the unlabeled class distribution, which is a finite-dimensional parameter, \emph{as an initial step}, using a doubly robust estimator with a strong theoretical guarantee; this estimate can then be integrated into existing methods to pseudo-label the unlabeled data during training more accurately. Experimental results demonstrate that incorporating our techniques into common pseudo-labeling approaches improves their performance.
K Nearest Neighbor-Guided Trajectory Similarity Learning
Venue: arXiv cs.LG 2025
Year: 2025

Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarity approximation accuracy due to difficulties in trajectory granularity modeling and in exploiting similarity signals in the training data. To fill this gap, we propose TSMini, a highly effective trajectory similarity model with a sub-view modeling mechanism capable of learning multi-granularity trajectory patterns and a k nearest neighbor-based loss that guides TSMini to learn not only absolute similarity values between trajectories but also their relative similarity ranks. Together, these two innovations enable highly accurate trajectory similarity approximation. Experiments show that TSMini can outperform the state-of-the-art models by 22% in accuracy on average when learning trajectory similarity measures.
Agentic AI: Autonomy, Accountability, and the Algorithmic Society
Venue: arXiv cs.CY 2025
Year: 2025

Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn workflows. Unlike traditional generative AI, which responds reactively to prompts, agentic AI proactively orchestrates processes, such as autonomously managing complex tasks or making real-time decisions. This transition from advisory roles to proactive execution challenges established legal, economic, and creative frameworks. In this paper, we explore challenges in three interrelated domains: creativity and intellectual property, legal and ethical considerations, and competitive effects. Central to our analysis is the tension between novelty and usefulness in AI-generated creative outputs, as well as the intellectual property and authorship challenges arising from AI autonomy. We highlight gaps in responsibility attribution and liability that create a "moral crumple zone"--a condition where accountability is diffused across multiple actors, leaving end-users and developers in precarious legal and ethical positions. We examine the competitive dynamics of two-sided algorithmic markets, where both sellers and buyers deploy AI agents, potentially mitigating or amplifying tacit collusion risks. We explore the potential for emergent self-regulation within networks of agentic AI--the development of an "algorithmic society"--raising critical questions: To what extent would these norms align with societal values? What unintended consequences might arise? How can transparency and accountability be ensured? Addressing these challenges will necessitate interdisciplinary collaboration to redefine legal accountability, align AI-driven choices with stakeholder values, and maintain ethical safeguards. We advocate for frameworks that balance autonomy with accountability, ensuring all parties can harness agentic AI's potential while preserving trust, fairness, & societal welfare.
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
Venue: arXiv cs.CR 2025
Year: 2025

Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameters. Although the absence of weight tuning prevents leakage via model parameters, it introduces the risk of inference adversaries exploiting retrieved documents in the model's context. Existing methods for membership inference and data extraction often rely on jailbreaking or carefully crafted unnatural queries, which can be easily detected or thwarted with query rewriting techniques common in RAG systems. In this work, we present Interrogation Attack (IA), a membership inference technique targeting documents in the RAG datastore. By crafting natural-text queries that are answerable only with the target document's presence, our approach demonstrates successful inference with just 30 queries while remaining stealthy; straightforward detectors identify adversarial prompts from existing methods up to ~76x more frequently than those generated by our attack. We observe a 2x improvement in TPR@1%FPR over prior inference attacks across diverse RAG configurations, all while costing less than $0.02 per document inference.
DIST: Efficient k-Clique Listing via Induced Subgraph Trie
Venue: arXiv cs.DB 2025
Year: 2025

Listing k-cliques plays a fundamental role in various data mining tasks, such as community detection and mining of cohesive substructures. Existing algorithms for the k-clique listing problem are built upon a general framework, which finds k-cliques by recursively finding (k-1)-cliques within subgraphs induced by the out-neighbors of each vertex. However, this framework has inherent inefficiency of finding smaller cliques within certain subgraphs repeatedly. In this paper, we propose an algorithm DIST for the k-clique listing problem. In contrast to existing works, the main idea in our approach is to compute each clique in the given graph only once and store it into a data structure called Induced Subgraph Trie, which allows us to retrieve the cliques efficiently. Furthermore, we propose a method to prune search space based on a novel concept called soft embedding of an l-tree, which further improves the running time. We show the superiority of our approach in terms of time and space usage through comprehensive experiments conducted on real-world networks; DIST outperforms the state-of-the-art algorithm by up to two orders of magnitude in both single-threaded and parallel experiments.
Denoising Score Matching with Random Features: Insights on Diffusion Models from Precise Learning Curves
Venue: arXiv cs.LG 2025
Year: 2025

We theoretically investigate the phenomena of generalization and memorization in diffusion models. Empirical studies suggest that these phenomena are influenced by model complexity and the size of the training dataset. In our experiments, we further observe that the number of noise samples per data sample ($m$) used during Denoising Score Matching (DSM) plays a significant and non-trivial role. We capture these behaviors and shed insights into their mechanisms by deriving asymptotically precise expressions for test and train errors of DSM under a simple theoretical setting. The score function is parameterized by random features neural networks, with the target distribution being $d$-dimensional Gaussian. We operate in a regime where the dimension $d$, number of data samples $n$, and number of features $p$ tend to infinity while keeping the ratios $ψ_n=\frac{n}{d}$ and $ψ_p=\frac{p}{d}$ fixed. By characterizing the test and train errors, we identify regimes of generalization and memorization as a function of $ψ_n,ψ_p$, and $m$. Our theoretical findings are consistent with the empirical observations.
Enhancing Token Filtering Efficiency in Large Language Model Training with Collider
Venue: arXiv cs.LG 2025
Year: 2025

Token filtering has been proposed to enhance utility of large language models (LLMs) by eliminating inconsequential tokens during training. While using fewer tokens should reduce computational workloads, existing studies have not succeeded in achieving higher efficiency. This is primarily due to the insufficient sparsity caused by filtering tokens only in the output layers, as well as inefficient sparse GEMM (General Matrix Multiplication), even when having sufficient sparsity. This paper presents Collider, a system unleashing the full efficiency of token filtering in LLM training. At its core, Collider filters activations of inconsequential tokens across all layers to maintain sparsity. Additionally, it features an automatic workflow that transforms sparse GEMM into dimension-reduced dense GEMM for optimized efficiency. Evaluations on three LLMs-TinyLlama-1.1B, Qwen2.5-1.5B, and Phi1.5-1.4B-demonstrate that Collider reduces backpropagation time by up to 35.1% and end-to-end training time by up to 22.0% when filtering 40% of tokens. Utility assessments of training TinyLlama on 15B tokens indicate that Collider sustains the utility advancements of token filtering by relatively improving model utility by 16.3% comparing to regular training, and reduces training time from 4.7 days to 3.5 days using 8 GPUs. Collider is designed for easy integration into existing LLM training frameworks, allowing systems already using token filtering to accelerate training with just one line of code.
FinchGPT: a Transformer based language model for birdsong analysis
Venue: arXiv cs.CL 2025
Year: 2025

The long-range dependencies among the tokens, which originate from hierarchical structures, are a defining hallmark of human language. However, whether similar dependencies exist within the sequential vocalization of non-human animals remains a topic of investigation. Transformer architectures, known for their ability to model long-range dependencies among tokens, provide a powerful tool for investigating this phenomenon. In this study, we employed the Transformer architecture to analyze the songs of Bengalese finch (Lonchura striata domestica), which are characterized by their highly variable and complex syllable sequences. To this end, we developed FinchGPT, a Transformer-based model trained on a textualized corpus of birdsongs, which outperformed other architecture models in this domain. Attention weight analysis revealed that FinchGPT effectively captures long-range dependencies within syllables sequences. Furthermore, reverse engineering approaches demonstrated the impact of computational and biological manipulations on its performance: restricting FinchGPT's attention span and disrupting birdsong syntax through the ablation of specific brain nuclei markedly influenced the model's outputs. Our study highlights the transformative potential of large language models (LLMs) in deciphering the complexities of animal vocalizations, offering a novel framework for exploring the structural properties of non-human communication systems while shedding light on the computational distinctions between biological brains and artificial neural networks.
IoT-enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology
Venue: arXiv cs.CR 2025
Year: 2025

Significant losses in terms of life and property occur from road traffic accidents, which are often caused by drunk and drowsy drivers. Reducing accidents requires effective detection of alcohol impairment and drowsiness as well as real-time driver monitoring. This paper aims to create an Internet of Things (IoT)--enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology. The system features an alcohol sensor and an IR sensor for detecting alcohol presence and monitoring driver eye movements, respectively. Upon detecting alcohol, alarms and warning lights are activated, the vehicle speed is progressively reduced, and the motor stops within ten to fifteen seconds if the alcohol presence persists. The IR sensor monitors prolonged eye closure, triggering alerts, or automatic vehicle stoppage to prevent accidents caused by drowsiness. Data from the IR sensor is transmitted to a mobile phone via Bluetooth for real-time monitoring and alerts. By identifying driver alcoholism and drowsiness, this system seeks to reduce accidents and save lives by providing safer transportation.
Exploring Representation-Aligned Latent Space for Better Generation
Venue: arXiv cs.LG 2025
Year: 2025

Generative models serve as powerful tools for modeling the real world, with mainstream diffusion models, particularly those based on the latent diffusion model paradigm, achieving remarkable progress across various tasks, such as image and video synthesis. Latent diffusion models are typically trained using Variational Autoencoders (VAEs), interacting with VAE latents rather than the real samples. While this generative paradigm speeds up training and inference, the quality of the generated outputs is limited by the latents' quality. Traditional VAE latents are often seen as spatial compression in pixel space and lack explicit semantic representations, which are essential for modeling the real world. In this paper, we introduce ReaLS (Representation-Aligned Latent Space), which integrates semantic priors to improve generation performance. Extensive experiments show that fundamental DiT and SiT trained on ReaLS can achieve a 15% improvement in FID metric. Furthermore, the enhanced semantic latent space enables more perceptual downstream tasks, such as segmentation and depth estimation.
The Impact of Persona-based Political Perspectives on Hateful Content Detection
Venue: arXiv cs.CL 2025
Year: 2025

While pretraining language models with politically diverse content has been shown to improve downstream task fairness, such approaches require significant computational resources often inaccessible to many researchers and organizations. Recent work has established that persona-based prompting can introduce political diversity in model outputs without additional training. However, it remains unclear whether such prompting strategies can achieve results comparable to political pretraining for downstream tasks. We investigate this question using persona-based prompting strategies in multimodal hate-speech detection tasks, specifically focusing on hate speech in memes. Our analysis reveals that when mapping personas onto a political compass and measuring persona agreement, inherent political positioning has surprisingly little correlation with classification decisions. Notably, this lack of correlation persists even when personas are explicitly injected with stronger ideological descriptors. Our findings suggest that while LLMs can exhibit political biases in their responses to direct political questions, these biases may have less impact on practical classification tasks than previously assumed. This raises important questions about the necessity of computationally expensive political pretraining for achieving fair performance in downstream tasks.
FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
Venue: arXiv cs.RO 2025
Year: 2025

Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An important field of research is the generation of point cloud maps, referring to the topic of simultaneous localization and mapping (SLAM). As most recent developments do not include global position data, the resulting point cloud maps suffer from internal distortion and missing georeferencing, preventing their use for map-based localization approaches. Therefore, we propose FlexCloud for an automatic georeferencing of point cloud maps created from SLAM. Our approach is designed to work modularly with different SLAM methods, utilizing only the generated local point cloud map and its odometry. Using the corresponding GNSS positions enables direct georeferencing without additional control points. By leveraging a 3D rubber-sheet transformation, we can correct distortions within the map caused by long-term drift while maintaining its structure. Our approach enables the creation of consistent, globally referenced point cloud maps from data collected by a mobile mapping system (MMS). The source code of our work is available at https://github.com/TUMFTM/FlexCloud.
Exploring Linear Attention Alternative for Single Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning-based single-image super-resolution (SISR) technology focuses on enhancing low-resolution (LR) images into high-resolution (HR) ones. Although significant progress has been made, challenges remain in computational complexity and quality, particularly in remote sensing image processing. To address these issues, we propose our Omni-Scale RWKV Super-Resolution (OmniRWKVSR) model which presents a novel approach that combines the Receptance Weighted Key Value (RWKV) architecture with feature extraction techniques such as Visual RWKV Spatial Mixing (VRSM) and Visual RWKV Channel Mixing (VRCM), aiming to overcome the limitations of existing methods and achieve superior SISR performance. This work has proved able to provide effective solutions for high-quality image reconstruction. Under the 4x Super-Resolution tasks, compared to the MambaIR model, we achieved an average improvement of 0.26% in PSNR and 0.16% in SSIM.
GO-GAN: Geometry Optimization Generative Adversarial Network for Achieving Optimized Structures with Targeted Physical Properties
Venue: arXiv cs.CE 2025
Year: 2025

This paper presents GO-GAN, a novel Generative Adversarial Network (GAN) architecture for geometry optimization (GO), specifically to generate structures based on user-specified input parameters. The architecture for GO-GAN proposed here combines a \texttt{Pix2Pix} GAN with a new input mechanism, involving a dynamic batch gradient descent-based training loop that leverages dataset symmetries. The model, implemented here using \texttt{TensorFlow} and \texttt{Keras}, is trained using input images representing scalar physical properties generated by a custom MatLab code. After training, GO-GAN rapidly generates optimized geometries from input images representing scalar inputs of the physical properties. Results demonstrate GO-GAN's ability to produce acceptable designs with desirable variations. These variations are followed by the influence of discriminators during training and are of practical significance in ensuring adherence to specifications while enabling creative exploration of the design space.
How Do Model Export Formats Impact the Development of ML-Enabled Systems? A Case Study on Model Integration
Venue: arXiv cs.SE 2025
Year: 2025

Machine learning (ML) models are often integrated into ML-enabled systems to provide software functionality that would otherwise be impossible. This integration requires the selection of an appropriate ML model export format, for which many options are available. These formats are crucial for ensuring a seamless integration, and choosing a suboptimal one can negatively impact system development. However, little evidence is available to guide practitioners during the export format selection. We therefore evaluated various model export formats regarding their impact on the development of ML-enabled systems from an integration perspective. Based on the results of a preliminary questionnaire survey (n=17), we designed an extensive embedded case study with two ML-enabled systems in three versions with different technologies. We then analyzed the effect of five popular export formats, namely ONNX, Pickle, TensorFlow's SavedModel, PyTorch's TorchScript, and Joblib. In total, we studied 30 units of analysis (2 systems x 3 tech stacks x 5 formats) and collected data via structured field notes. The holistic qualitative analysis of the results indicated that ONNX offered the most efficient integration and portability across most cases. SavedModel and TorchScript were very convenient to use in Python-based systems, but otherwise required workarounds (TorchScript more than SavedModel). SavedModel also allowed the easy incorporation of preprocessing logic into a single file, which made it scalable for complex deep learning use cases. Pickle and Joblib were the most challenging to integrate, even in Python-based systems. Regarding technical support, all model export formats had strong technical documentation and strong community support across platforms such as Stack Overflow and Reddit. Practitioners can use our findings to inform the selection of ML export formats suited to their context.
Evaluation of End-to-End Continuous Spanish Lipreading in Different Data Conditions
Venue: arXiv cs.CV 2025
Year: 2025

Visual speech recognition remains an open research problem where different challenges must be considered by dispensing with the auditory sense, such as visual ambiguities, the inter-personal variability among speakers, and the complex modeling of silence. Nonetheless, recent remarkable results have been achieved in the field thanks to the availability of large-scale databases and the use of powerful attention mechanisms. Besides, multiple languages apart from English are nowadays a focus of interest. This paper presents noticeable advances in automatic continuous lipreading for Spanish. First, an end-to-end system based on the hybrid CTC/Attention architecture is presented. Experiments are conducted on two corpora of disparate nature, reaching state-of-the-art results that significantly improve the best performance obtained to date for both databases. In addition, a thorough ablation study is carried out, where it is studied how the different components that form the architecture influence the quality of speech recognition. Then, a rigorous error analysis is carried out to investigate the different factors that could affect the learning of the automatic system. Finally, a new Spanish lipreading benchmark is consolidated. Code and trained models are available at https://github.com/david-gimeno/evaluating-end2end-spanish-lipreading.
Oscillations Make Neural Networks Robust to Quantization
Venue: arXiv cs.LG 2025
Year: 2025

We challenge the prevailing view that weight oscillations observed during Quantization Aware Training (QAT) are merely undesirable side-effects and argue instead that they are an essential part of QAT. We show in a univariate linear model that QAT results in an additional loss term that causes oscillations by pushing weights away from their nearest quantization level. Based on the mechanism from the analysis, we then derive a regularizer that induces oscillations in the weights of neural networks during training. Our empirical results on ResNet-18 and Tiny Vision Transformer, evaluated on CIFAR-10 and Tiny ImageNet datasets, demonstrate across a range of quantization levels that training with oscillations followed by post-training quantization (PTQ) is sufficient to recover the performance of QAT in most cases. With this work we provide further insight into the dynamics of QAT and contribute a novel insight into explaining the role of oscillations in QAT which until now have been considered to have a primarily negative effect on quantization.