OpenRaiser/Intern-Atlas huggingface dataset:
Vehicle-group-based Crash Risk Prediction and Interpretation on Highways
Venue: arXiv cs.LG 2025
Year: 2025

Previous studies in predicting crash risks primarily associated the number or likelihood of crashes on a road segment with traffic parameters or geometric characteristics, usually neglecting the impact of vehicles' continuous movement and interactions with nearby vehicles. Recent technology advances, such as Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs) are able to collect high-resolution trajectory data, which enables trajectory-based risk analysis. This study investigates a new vehicle group (VG) based risk analysis method and explores risk evolution mechanisms considering VG features. An impact-based vehicle grouping method is proposed to cluster vehicles into VGs by evaluating their responses to the erratic behaviors of nearby vehicles. The risk of a VG is aggregated based on the risk between each vehicle pair in the VG, measured by inverse Time-to-Collision (iTTC). A Logistic Regression and a Graph Neural Network (GNN) are then employed to predict VG risks using aggregated and disaggregated VG information. Both methods achieve excellent performance with AUC values exceeding 0.93. For the GNN model, GNNExplainer with feature perturbation is applied to identify critical individual vehicle features and their directional impact on VG risks. Overall, this research contributes a new perspective for identifying, predicting, and interpreting traffic risks.
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Venue: arXiv cs.LG 2025
Year: 2025

Statistical heterogeneity of clients' local data is an important characteristic in federated learning, motivating personalized algorithms tailored to the local data statistics. Though there has been a plethora of algorithms proposed for personalized supervised learning, discovering the structure of local data through personalized unsupervised learning is less explored. We initiate a systematic study of such personalized unsupervised learning by developing algorithms based on optimization criteria inspired by a hierarchical Bayesian statistical framework. We develop adaptive algorithms that discover the balance between using limited local data and collaborative information. We do this in the context of two unsupervised learning tasks: personalized dimensionality reduction and personalized diffusion models. We develop convergence analyses for our adaptive algorithms which illustrate the dependence on problem parameters (e.g., heterogeneity, local sample size). We also develop a theoretical framework for personalized diffusion models, which shows the benefits of collaboration even under heterogeneity. We finally evaluate our proposed algorithms using synthetic and real data, demonstrating the effective sample amplification for personalized tasks, induced through collaboration, despite data heterogeneity.
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

Standard benchmarks of bias and fairness in large language models (LLMs) measure the association between the user attributes stated or implied by a prompt and the LLM's short text response, but human-AI interaction increasingly requires long-form and context-specific system output to solve real-world tasks. In the commonly studied domain of gender-occupation bias, we test whether these benchmarks are robust to lengthening the LLM responses as a measure of Realistic Use and Tangible Effects (i.e., RUTEd evaluations). From the current literature, we adapt three standard bias metrics (neutrality, skew, and stereotype) and develop analogous RUTEd evaluations from three contexts of real-world use: children's bedtime stories, user personas, and English language learning exercises. We find that standard bias metrics have no significant correlation with the more realistic bias metrics. For example, selecting the least biased model based on the standard "trick tests" coincides with selecting the least biased model as measured in more realistic use no more than random chance. We suggest that there is not yet evidence to justify standard benchmarks as reliable proxies of real-world AI biases, and we encourage further development of evaluations grounded in particular contexts.
The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

Back in the early 20th century, a horse named Hans appeared to perform arithmetic and other intellectual tasks during exhibitions in Germany, while it actually relied solely on involuntary cues in the body language from the human trainer. Modern machine learning models are no different. These models are known to be sensitive to spurious correlations between non-essential features of the inputs (e.g., background, texture, and secondary objects) and the corresponding labels. Such features and their correlations with the labels are known as "spurious" because they tend to change with shifts in real-world data distributions, which can negatively impact the model's generalization and robustness. In this paper, we provide a comprehensive survey of this emerging issue, along with a fine-grained taxonomy of existing state-of-the-art methods for addressing spurious correlations in machine learning models. Additionally, we summarize existing datasets, benchmarks, and metrics to facilitate future research. The paper concludes with a discussion of the broader impacts, the recent advancements, and future challenges in the era of generative AI, aiming to provide valuable insights for researchers in the related domains of the machine learning community.
PIP-Net: Pedestrian Intention Prediction in the Wild
Venue: arXiv cs.CV 2025
Year: 2025

Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field. In this article, we introduce PIP-Net, a novel framework designed to predict pedestrian crossing intentions by AVs in real-world urban scenarios. We offer two variants of PIP-Net designed for different camera mounts and setups. Leveraging both kinematic data and spatial features from the driving scene, the proposed model employs a recurrent and temporal attention-based solution, outperforming state-of-the-art performance. To enhance the visual representation of road users and their proximity to the ego vehicle, we introduce a categorical depth feature map, combined with a local motion flow feature, providing rich insights into the scene dynamics. Additionally, we explore the impact of expanding the camera's field of view, from one to three cameras surrounding the ego vehicle, leading to an enhancement in the model's contextual perception. Depending on the traffic scenario and road environment, the model excels in predicting pedestrian crossing intentions up to 4 seconds in advance, which is a breakthrough in current research studies in pedestrian intention prediction. Finally, for the first time, we present the Urban-PIP dataset, a customised pedestrian intention prediction dataset, with multi-camera annotations in real-world automated driving scenarios.
Faster, Deterministic and Space Efficient Subtrajectory Clustering
Venue: arXiv cs.CG 2025
Year: 2025

Given a trajectory $T$ and a distance $Δ$, we wish to find a set $C$ of curves of complexity at most $\ell$, such that we can cover $T$ with subcurves that each are within Fréchet distance $Δ$ to at least one curve in $C$. We call $C$ an $(\ell,Δ)$-clustering and aim to find an $(\ell,Δ)$-clustering of minimum cardinality. This problem variant was introduced by Akitaya $et$ $al.$ (2021) and shown to be NP-complete. The main focus has therefore been on bicriteria approximation algorithms, allowing for the clustering to be an $(\ell, Θ(Δ))$-clustering of roughly optimal size. We present algorithms that construct $(\ell,4Δ)$-clusterings of $\mathcal{O}(k \log n)$ size, where $k$ is the size of the optimal $(\ell, Δ)$-clustering. We use $\mathcal{O}(n^3)$ space and $\mathcal{O}(k n^3 \log^4 n)$ time. Our algorithms significantly improve upon the clustering quality (improving the approximation factor in $Δ$) and size (whenever $\ell \in Ω(\log n / \log k)$). We offer deterministic running times improving known expected bounds by a factor near-linear in $\ell$. Additionally, we match the space usage of prior work, and improve it substantially, by a factor super-linear in $n\ell$, when compared to deterministic results.
VGMShield: Mitigating Misuse of Video Generative Models
Venue: arXiv cs.CR 2025
Year: 2025

With the rapid advancement in video generation, people can conveniently use video generation models to create videos tailored to their specific desires. As a result, there are also growing concerns about the potential misuse of video generation for spreading illegal content and misinformation. In this work, we introduce VGMShield: a set of straightforward but effective mitigations through the lifecycle of fake video generation. We start from fake video detection, trying to understand whether there is uniqueness in generated videos and whether we can differentiate them from real videos; then, we investigate the fake video source tracing problem, which maps a fake video back to the model that generated it. Towards these, we propose to leverage pre-trained models that focus on spatial-temporal dynamics as the backbone to identify inconsistencies in videos. In detail, we analyze fake videos from the perspective of the generation process. Based on the observation of attention shifts, motion variations, and frequency fluctuations, we identify common patterns in the generated video. These patterns serve as the foundation for our experiments on fake video detection and source tracing. Through experiments on seven state-of-the-art open-source models, we demonstrate that current models still cannot reliably reproduce spatial-temporal relationships, and thus, we can accomplish detection and source tracing with over 90% accuracy. Furthermore, anticipating future generative model improvements, we propose a prevention method that adds invisible perturbations to the query images to make the generated videos look unreal. Together with detection and tracing, our multi-faceted set of solutions can effectively mitigate misuse of video generative models.
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
Venue: arXiv cs.LG 2025
Year: 2025

Besides natural language processing, transformers exhibit extraordinary performance in solving broader applications, including scientific computing and computer vision. Previous works try to explain this from the expressive power and capability perspectives that standard transformers are capable of performing some algorithms. To empower transformers with algorithmic capabilities and motivated by the recently proposed looped transformer, we design a novel transformer framework, dubbed Algorithm Transformer (abbreviated as AlgoFormer). We provide an insight that efficient transformer architectures can be designed by leveraging prior knowledge of tasks and the underlying structure of potential algorithms. Compared with the standard transformer and vanilla looped transformer, the proposed AlgoFormer can perform efficiently in algorithm representation in some specific tasks. In particular, inspired by the structure of human-designed learning algorithms, our transformer framework consists of a pre-transformer that is responsible for task preprocessing, a looped transformer for iterative optimization algorithms, and a post-transformer for producing the desired results after post-processing. We provide theoretical evidence of the expressive power of the AlgoFormer in solving some challenging problems, mirroring human-designed algorithms. Furthermore, some theoretical and empirical results are presented to show that the designed transformer has the potential to perform algorithm representation and learning. Experimental results demonstrate the empirical superiority of the proposed transformer in that it outperforms the standard transformer and vanilla looped transformer in some specific tasks. An extensive experiment on real language tasks (e.g., neural machine translation of German and English, and text classification) further validates the expressiveness and effectiveness of AlgoFormer.
Trustworthy Distributed Certification of Program Execution
Venue: arXiv cs.SE 2025
Year: 2025

Verifying the execution of a program is complicated and often limited by the inability to validate the code's correctness. It is a crucial aspect of scientific research, where it is needed to ensure the reproducibility and validity of experimental results. Similarly, in customer software testing, it is difficult for customers to verify that their specific program version was tested or executed at all. Existing state-of-the-art solutions, such as hardware-based approaches, constraint solvers, and verifiable computation systems, do not provide definitive proof of execution, which hinders reliable testing and analysis of program results. In this paper, we propose an innovative approach that combines a prototype programming language called Mona with a certification protocol OCCP to enable the distributed and decentralized re-execution of program segments. Our protocol allows for certification of program segments in a distributed, immutable, and trustworthy system without the need for naive re-execution, resulting in significant improvements in terms of time and computational resources used. We also explore the use of blockchain technology to manage the protocol workflow following other approaches in this space. Our approach offers a promising solution to the challenges of program execution verification and opens up opportunities for further research and development in this area. Our findings demonstrate the efficiency of our approach in reducing the number of program executions compared to existing state-of-the-art methods, thus improving the efficiency of certifying program executions.
SecurePose: Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
Venue: arXiv cs.CV 2025
Year: 2025

Movement disorder diagnosis often relies on expert evaluation of patient videos, but sharing these videos poses privacy risks. Current methods for de-identifying videos, such as blurring faces, are often manual, inconsistent, or inaccurate. Furthermore, these methods can compromise objective kinematic analysis - a crucial component of diagnosis. To address these challenges, we developed SecurePose, an open-source software that simultaneously provides reliable de-identification and automated kinematic extraction from videos recorded in clinic settings using smartphones/tablets. SecurePose utilizes pose estimation (using OpenPose) to extract full body kinematics, track individuals, identify the patient, and then accurately blur faces in the videos. We validated SecurePose on gait videos recorded in outpatient clinic visits of 116 children with cerebral palsy, assessing both the accuracy of its de-identification compared to the ground truth (manual blurring) and the reliability of the intermediate steps of kinematics extraction. Results demonstrate that SecurePose outperformed six existing methods in automated face detection and achieved comparable accuracy to robust manual blurring, but in significantly less time (91.08% faster). Ten experienced researchers also confirmed SecurePose's usability via System Usability Scale scores. These findings validate SecurePose as a practical and effective tool for protecting patient privacy while enabling accurate kinematics extraction in clinical settings.
Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting
Venue: arXiv cs.LG 2025
Year: 2025

In machine learning, ensembles are important tools for improving the model performance. In natural language processing specifically, ensembles boost the performance of a method due to multiple large models available in open source. However, existing approaches mostly rely on simple averaging of predictions by ensembles with equal weights for each model, ignoring differences in the quality and conformity of models. We propose to estimate weights for ensembles of NLP models using not only knowledge of their individual performance but also their similarity to each other. By adopting distance measures based on Topological Data Analysis (TDA), we improve our ensemble. The quality improves for both text classification accuracy and relevant uncertainty estimation.
Whose LLM is it Anyway? Linguistic Comparison and LLM Attribution for GPT-3.5, GPT-4 and Bard
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are capable of generating text that is similar to or surpasses human quality. However, it is unclear whether LLMs tend to exhibit distinctive linguistic styles akin to how human authors do. Through a comprehensive linguistic analysis, we compare the vocabulary, Part-Of-Speech (POS) distribution, dependency distribution, and sentiment of texts generated by three of the most popular LLMS today (GPT-3.5, GPT-4, and Bard) to diverse inputs. The results point to significant linguistic variations which, in turn, enable us to attribute a given text to its LLM origin with a favorable 88\% accuracy using a simple off-the-shelf classification model. Theoretical and practical implications of this intriguing finding are discussed.
What's in a Name? Auditing Large Language Models for Race and Gender Bias
Venue: arXiv cs.CL 2025
Year: 2025

We employ an audit design to investigate biases in state-of-the-art large language models, including GPT-4. In our study, we prompt the models for advice involving a named individual across a variety of scenarios, such as during car purchase negotiations or election outcome predictions. We find that the advice systematically disadvantages names that are commonly associated with racial minorities and women. Names associated with Black women receive the least advantageous outcomes. The biases are consistent across 42 prompt templates and several models, indicating a systemic issue rather than isolated incidents. While providing numerical, decision-relevant anchors in the prompt can successfully counteract the biases, qualitative details have inconsistent effects and may even increase disparities. Our findings underscore the importance of conducting audits at the point of LLM deployment and implementation to mitigate their potential for harm against marginalized communities.
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents an analytical study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Generalizing beyond extant approaches grounded in random walk analysis or particle systems, we approach this problem through operator semigroup theory. This theoretical framework allows us to rigorously prove that oversmoothing is intrinsically linked to the ergodicity of the diffusion operator. Relying on semigroup method, we can quantitatively analyze the dynamic of graph diffusion and give a specific mathematical form of the smoothing feature by ergodicity and invariant measure of operator, which improves previous works only show existence of oversmoothing. This finding further poses a general and mild ergodicity-breaking condition, encompassing the various specific solutions previously offered, thereby presenting a more universal and theoretically grounded approach to relieve oversmoothing in diffusion-based GNNs. Additionally, we offer a probabilistic interpretation of our theory, forging a link with prior works and broadening the theoretical horizon. Our experimental results reveal that this ergodicity-breaking term effectively mitigates oversmoothing measured by Dirichlet energy, and simultaneously enhances performance in node classification tasks.
Towards Principled Task Grouping for Multi-Task Learning
Venue: arXiv cs.LG 2025
Year: 2025

Multi-task learning (MTL) aims to leverage shared information among tasks to improve learning efficiency and accuracy. However, MTL often struggles to effectively manage positive and negative transfer between tasks, which can hinder performance improvements. Task grouping addresses this challenge by organizing tasks into meaningful clusters, maximizing beneficial transfer while minimizing detrimental interactions. This paper introduces a principled approach to task grouping in MTL, advancing beyond existing methods by addressing key theoretical and practical limitations. Unlike prior studies, our method offers a theoretically grounded approach that does not depend on restrictive assumptions for constructing transfer gains. We also present a flexible mathematical programming formulation that accommodates a wide range of resource constraints, thereby enhancing its versatility. Experimental results across diverse domains, including computer vision datasets, combinatorial optimization benchmarks, and time series tasks, demonstrate the superiority of our method over extensive baselines, thereby validating its effectiveness and general applicability in MTL without sacrificing efficiency.
Tight Approximation and Kernelization Bounds for Vertex-Disjoint Shortest Paths
Venue: arXiv cs.DS 2025
Year: 2025

We examine the possibility of approximating Maximum Vertex-Disjoint Shortest Paths. In this problem, the input is an edge-weighted (directed or undirected) $n$-vertex graph $G$ along with $k$ terminal pairs $(s_1,t_1),(s_2,t_2),\ldots,(s_k,t_k)$. The task is to connect as many terminal pairs as possible by pairwise vertex-disjoint paths such that each path is a shortest path between the respective terminals. Our work is anchored in the recent breakthrough by Lochet [SODA '21], which demonstrates the polynomial-time solvability of the problem for a fixed value of $k$. Lochet's result implies the existence of a polynomial-time $ck$-approximation for Maximum Vertex-Disjoint Shortest Paths, where $c \leq 1$ is a constant. Our first result suggests that this approximation algorithm is, in a sense, the best we can hope for. More precisely, assuming the gap-ETH, we exclude the existence of an $o(k)$-approximations within $f(k)$poly($n$) time for any function $f$ that only depends on $k$. Our second result demonstrates the infeasibility of achieving an approximation ratio of $n^{\frac{1}{2}-\varepsilon}$ in polynomial time, unless P = NP. It is not difficult to show that a greedy algorithm selecting a path with the minimum number of arcs results in a $\sqrt{\ell}$-approximation, where $\ell$ is the number of edges in all the paths of an optimal solution. Since $\ell \leq n$, this underscores the tightness of the $n^{\frac{1}{2}-\varepsilon}$-inapproximability bound. Additionally, we establish that the problem can be solved in $2^{O(\ell)}$poly($n$) time, but does not admit a polynomial kernel in $\ell$. Moreover, it cannot be solved in $2^{o(\ell)}$poly($n$) time unless the ETH fails. Our hardness results hold for undirected graphs with unit weights, while our positive results extend to scenarios where the input graph is directed and features arbitrary (non-negative) edge weights.